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Hard Problems: Human Behavior and Security

2015 (Part 2)


Human behavior creates the most complex of hard problems for the Science of Security community. The research work cited here was presented in 2015.

J. G. Proudfoot, J. L. Jenkins, J. K. Burgoon and J. F. Nunamaker, “Deception Is in the Eye of the Communicator: Investigating Pupil Diameter Variations in Automated Deception Detection Interviews,” Intelligence and Security Informatics (ISI), 2015 IEEE International Conference on, Baltimore, MD, 2015, pp. 97-102. doi: 10.1109/ISI.2015.7165946
Abstract: Deception is pervasive, often leading to adverse consequences for individuals, organizations, and society. Information systems researchers are developing tools and evaluating sensors that can be used to augment human deception judgments. One sensor exhibiting particular promise is the eye tracker. Prior work evaluating eye trackers for deception detection has focused on the detection and interpretation of brief eye behavior variations in response to stimuli (e.g, images) or interview questions. However, research is needed to understand how eye behaviors evolve over the course of an interaction with a deception detection system. Using latent growth curve modeling, we test how pupil diameter evolves over one's interaction with a deception detection system. The results indicate that pupil diameter changes over the course of a deception detection interaction, and that these trends are indicative of deception during the interaction, regardless if incriminating target items are shown.
Keywords: behavioural sciences computing; gaze tracking; image sensors; object detection; automated deception detection interviews; communicator eye; deception detection interaction; deception detection system; eye behavior variations; eye stimuli; eye tracker; human deception judgments; information systems; latent growth curve modeling; pupil diameter variations; sensor; Accuracy; Analytical models; Information systems; Interviews; Organizations; Sensors; deception detection systems; eye tracking; pupil diameter
(ID#: 16-9649)


M. Oulehla, “Investigation into Google Play Security Mechanisms via Experimental Botnet,” 2015 IEEE International Symposium on Signal Processing and Information Technology (ISSPIT), Abu Dhabi, 2015, pp. 591-596. doi: 10.1109/ISSPIT.2015.7394406
Abstract: Mobile devices such as smartphones and tablets have become a common part of human society of the 21st century and their popularity is continuously growing. However, certain research papers imply that popularity and security do not reach the same level. They suggest that there are security weaknesses allowing publishing applications with malicious behavior on Google Play. For test reasons of Google Play security mechanisms, a special pair of applications has been developed. The former is a testing application containing a mobile botnet client. It has been designed to be resistant against security scans based on dynamic analysis but its malicious intentions have been presented in uncovered form into the code of application. Such testing application has been published on Google Play. The latter is represented by a malware application with the sole purpose of being fraudulently installed on mobile devices without any security verification including Google Play. Certain interesting results have been raised by the research. Based on these results, useful future research directions to security of mobile device field have emerged.
Keywords: computer network security; invasive software; mobile handsets; Google Play security mechanism; malicious behavior; malware application; mobile botnet client; mobile device; publishing applications; security verification; security weaknesses; testing application; Google; Malware; Mobile communication; Servers; Smart phones; Android; C&C server; Google Pay; bot; botmaster; mobile botnet; mobile devices (ID#: 16-9650)


G. Xu et al., “Towards Trustworthy Participants in Social Participatory Networks,” Cyber Security and Cloud Computing (CSCloud), 2015 IEEE 2nd International Conference on, New York, NY, 2015, pp. 194-199. doi: 10.1109/CSCloud.2015.55
Abstract: By leveraging online social networks as an underlying infrastructure, Social Participatory Network (SPN) has been becoming a new paradigm of participatory sensing systems. However, a significant barrier to the widespread use of SPN applications is their vulnerability to various forms of malicious attacks. Such threats inhibit human participation and thus the viability of SPN systems in everyday use. To solve this problem, this paper proposes a trust evaluation framework for participants to encourage wider human participation in SPN. The proposal is based on the Tianjin University's own existing SPN system, named CRCS (ClassRoom Cloud System), which enables participants to use the cloud resources for online lessons or library study. It derives the trust value of participants by using entropy-weight method and data mining algorithms to deal with the behaviors data of participants. Our proposed solution can detect malicious participants easily, and more importantly, it outperforms other work for its low cost and simple deployment. For now, though our solution is based on a specified SPN system, we are confident that this solution is highly applicable to most other SPN systems.
Keywords: cloud computing; data mining; social networking (online); trusted computing; ubiquitous computing; CRCS; classroom cloud system; cloud resource; data mining algorithm; entropy-weight method; malicious attack; online social network; participatory sensing system; social participatory network; trust evaluation framework; trustworthy participant; Computer crime; Data mining; Prototypes; Sensors; System performance; Training; Training data; CRCS; Data Mining; Entropy-weight; Social Participatory Network; Trust Evaluation Framework (ID#: 16-9651)


S. Ojha and S. Sakhare, “Image Processing Techniques for Object Tracking in Video Surveillance — A Survey,” Pervasive Computing (ICPC), 2015 International Conference on, Pune, 2015, pp. 1-6. doi: 10.1109/PERVASIVE.2015.7087180
Abstract: Many researchers are getting attracted in the field of object tracking in video surveillance, which is an important application and emerging research area in image processing. Video tracking is the process of locating a moving object or multiple objects over a time using camera. Due to key features of video surveillance, it has a variety of uses like human-computer interactions, security and surveillance, video communication, traffic control, public areas such as airports, underground stations, mass events, etc. Tracking a target in a cluttered premise is still one of the challenging problems of video surveillance. A sequential flow of moving object detection, its classification, tracking and identifying the behavior completes the processing framework of video surveillance. This paper takes insight into tracking methods, their categorization into different types, focuses on important and useful tracking methods. In this paper, we provide a brief overview of tracking strategies like region based, active contour based, etc with their positive and negative aspects. Different tracking methods are mentioned with detailed description. We review general strategies under literature survey on different techniques and finally stating the analysis of possible research directions.
Keywords: cameras; image classification; object detection; object tracking; video surveillance; active contour based tracking; camera; classification; image processing technique; moving object detection; object tracking; region based tracking; target tracking; video surveillance; video tracking; Computer vision; Feature extraction; Image color analysis; Object tracking; Shape; Video surveillance; Motion segmentation; object representation (ID#: 16-9652)


Y. Xiang, L. Wang and Y. Zhang, “Power Grid Adequacy Evaluation Involving Substation Cybersecurity Issues,” Innovative Smart Grid Technologies Conference (ISGT), 2015 IEEE Power & Energy Society, Washington, DC, 2015, pp. 1-5. doi: 10.1109/ISGT.2015.7131815
Abstract: Modern power systems heavily rely on the associated cyber network, so it is crucial to develop novel methods to evaluate the overall power system adequacy considering the substation cybersecurity issues. In this study, human dynamic is applied to simulate the temporal behavior pattern of cyber attackers. The Markov game and static game are utilized to model the intelligent attack/defense behaviors in different attack scenarios. A novel framework for power system adequacy assessment incorporating the cyber and physical failures is proposed. Simulations are conducted based on a representative reliability test system, and the influences of critical parameters on system adequacy are carefully examined. It is concluded that effective measures should be implemented to ensure the overall system adequacy, and informed decisions should be made to allocate the limited resources for enhancing the cybersecurity of cyber-physical power grids.
Keywords: Markov processes; failure analysis; game theory; power grids; power system faults; power system reliability; power system security; security of data; substation protection; Markov game; cyber failure; cyber network; cyber-physical power grid adequacy evaluation; intelligent attack behavior; intelligent defense behavior; overall power system adequacy evaluation; physical failure; power system adequacy assessment; representative reliability test system; static game; substation cybersecurity issues; temporal behavior pattern simulation; Computer security; Game theory; Games; Power system dynamics; Substations; Adequacy assessment; cyber security; cyber-physical systems; human dynamics (ID#: 16-9653)


S. Choi, D. Zage, Y. R. Choe and B. Wasilow, “Physically Unclonable Digital ID,” Mobile Services (MS), 2015 IEEE International Conference on, New York, NY, 2015, pp. 105-111. doi: 10.1109/MobServ.2015.24
Abstract: The Center for Strategic and International Studies estimates the annual cost from cyber crime to be more than $400 billion. Most notable is the recent digital identity thefts that compromised millions of accounts. These attacks emphasize the security problems of using clonable static information. One possible solution is the use of a physical device known as a Physically Unclonable Function (PUF). PUFs can be used to create encryption keys, generate random numbers, or authenticate devices. While the concept shows promise, current PUF implementations are inherently problematic: inconsistent behavior, expensive, susceptible to modeling attacks, and permanent. Therefore, we propose a new solution by which an unclonable, dynamic digital identity is created between two communication endpoints such as mobile devices. This Physically Unclonable Digital ID (PUDID) is created by injecting a data scrambling PUF device at the data origin point that corresponds to a unique and matching descrambler/hardware authentication at the receiving end. This device is designed using macroscopic, intentional anomalies, making them inexpensive to produce. PUDID is resistant to cryptanalysis due to the separation of the challenge response pair and a series of hash functions. PUDID is also unique in that by combining the PUF device identity with a dynamic human identity, we can create true two-factor authentication. We also propose an alternative solution that eliminates the need for a PUF mechanism altogether by combining tamper resistant capabilities with a series of hash functions. This tamper resistant device, referred to as a Quasi-PUDID (Q-PUDID), modifies input data, using a black-box mechanism, in an unpredictable way. By mimicking PUF attributes, Q-PUDID is able to avoid traditional PUF challenges thereby providing high-performing physical identity assurance with or without a low performing PUF mechanism. Three different application scenarios with mobile devices for PUDID and Q-PUDI- have been analyzed to show their unique advantages over traditional PUFs and outline the potential for placement in a host of applications.
Keywords: authorisation; cryptography; random number generation; PUF; Q-PUDID; center for strategic and international studies; clonable static information; cryptanalysis; descrambler-hardware authentication; device authentication; digital identity thefts; dynamic human identity; encryption keys; hash functions; physically unclonable digital ID; physically unclonable function; quasi-PUDID; random number generation; two-factor authentication; Authentication; Cryptography; Immune system; Optical imaging; Optical sensors; Servers; access control; authentication; biometrics; cloning; computer security; cyber security; digital signatures; identification of persons; identity management systems; mobile hardware security (ID#: 16-9654)


K. Gai, M. Qiu, L. C. Chen and M. Liu, “Electronic Health Record Error Prevention Approach Using Ontology in Big Data,” High Performance Computing and Communications (HPCC), 2015 IEEE 7th International Symposium on Cyberspace Safety and Security (CSS), 2015 IEEE 12th International Conference on Embedded Software and Systems (ICESS), 2015 IEEE 17th International Conference on, New York, NY, 2015, pp. 752-757. doi: 10.1109/HPCC-CSS-ICESS.2015.168
Abstract: Electronic Health Record (EHR) systems have been playing a dramatically important role in tele-health domains. One of the major benefits of using EHR systems is assisting physicians to gain patients' healthcare information and shorten the process of the medical decision making. However, physicians' inputs still have a great impact on making decisions that cannot be checked by EHR systems. This consequence can be influenced by human behaviors or physicians' knowledge structures. An efficient approach of alerting to the unusual decisions is an urgent requirement for current EHR systems. This paper proposes a schema using ontology in big data to generate an alerting mechanism to assist physicians to make a proper medical diagnosis. The proposed model is Ontology-based EHR Error Prevention Model (OEHR-EPM), which is implemented by a proposed algorithm, Error Prevention Adjustment Algorithm (EPAA). The ontological approach uses Protege to represent the knowledge-based ontology. The proposed schema has been examined by our experiments and the experimental results show that our schema has a higher-level accuracy rate and acceptable operating time performance.
Keywords: Big Data; decision making; electronic health records; health care; ontologies (artificial intelligence); Big data; EHR system; EPAA; OEHR-EPM; electronic health record error prevention approach; error prevention adjustment algorithm; healthcare information; knowledge-based ontology; medical decision making; medical diagnosis; ontology-based EHR error prevention model; Algorithm design and analysis; Diseases; Electronic medical records; Medical diagnostic imaging; Ontologies; Electronic health records; big data; cloud computing; error prevention; ontology (ID#: 16-9655)


J. David Schaffer, “Evolving Spiking Neural Networks: A Novel Growth Algorithm Corrects the Teacher,” Computational Intelligence for Security and Defense Applications (CISDA), 2015 IEEE Symposium on, Verona, NY, 2015, pp. 1-8. doi: 10.1109/CISDA.2015.7208630
Abstract: Spiking neural networks (SNNs) have generated considerable excitement because of their computational properties, believed to be superior to conventional von Neumann machines, and sharing properties with living brains. Yet progress building these systems has been limited because we lack a design methodology. We present a gene-driven network growth algorithm that enables a genetic algorithm (evolutionary computation) to generate and test SNNs. The genome length for this algorithm grows O(n) where n is the number of neurons; n is also evolved. The genome not only specifies the network topology, but all its parameters as well. In experiments, the algorithm discovered SNNs that effectively produce a robust spike bursting behavior given tonic inputs, an application suitable for central pattern generators. Even though evolution did not include perturbations of the input spike trains, the evolved networks showed remarkable robustness to such perturbations. On a second task, a sequence detector, several related discriminating designs were found, all made “errors” in that they fired when input spikes were simultaneous (i.e. not strictly in sequence), but not when they were out of sequence. They also fired when the sequence was too close for the teacher to have declared they were in sequence. That is, evolution produced these behaviors even though it was not explicitly rewarded for doing so. We are optimistic that this technology might be scaled up to produce robust SNN designs that humans would be hard pressed to produce.
Keywords: brain; genetic algorithms; genetics; neural nets; topology; Neumann machine; SNN; algorithm grow O(n); brains; central pattern generator; evolutionary computation; gene-driven network growth algorithm; genetic algorithm; genome length; network topology; robust spike bursting behavior;  spiking neural network; Algorithm design and analysis; Bioinformatics; Biological neural networks; Buildings; Design methodology; Genomics; Robustness; Genetic algorithms; noise robustness; sequence detector; spiking neural networks; tonic burster; topology growth algorithm (ID#: 16-9656)


Shraddha G. Mhatre, Satishkumar Varma and Rupali Nikhare, “Visual Surveillance Using Absolute Difference Motion Detection,” Technologies for Sustainable Development (ICTSD), 2015 International Conference on, Mumbai, 2015, pp. 1-5. doi: 10.1109/ICTSD.2015.7095848
Abstract: Surveillance is the monitoring of the behavior, activities, or other changing information, usually of people for the purpose of influencing, managing, directing, or protecting them. As security is becoming the primary concern of society and hence having a security system is becoming a big requirement. Video surveillance plays a vital role in security systems. This paper describes the ability to recognise objects and humans, to describe their actions and interactions from information acquired by sensors using absolute difference motion detection technique. Real-time implementation is achieved by using a Global System for Mobile Communication (GSM) modem for SMS (Short Message Service) notification. The ablity of tracking and recognition of the visual device was implemented using OpenCVTM for displaying an output. The detected objects motion is being captured and stored in HDD.
Keywords: cellular radio; disc drives; electronic messaging; hard discs; image motion analysis; image recognition; image sensors; modems; object detection; object tracking; security; video surveillance; GSM modem; Global System for Mobile Communication; HDD; OpenCV; SMS notification; absolute difference motion detection technique; hard disc drives; security system; short message service; visual device; visual surveillance; Cameras; GSM; Modems; Motion detection; Noise; Surveillance; Tracking; GSM system; Motion detection methods; Visual surveillance; Web Cam/ External camera (ID#: 16-9657)


P. A. Legg, O. Buckley, M. Goldsmith and S. Creese, “Caught in the Act of an Insider Attack: Detection and Assessment of Insider Threat,” Technologies for Homeland Security (HST), 2015 IEEE International Symposium on, Waltham, MA, 2015, pp. 1-6. doi: 10.1109/THS.2015.7446229
Abstract: The greatest asset that any organisation has are its people, but they may also be the greatest threat. Those who are within the organisation may have authorised access to vast amounts of sensitive company records that are essential for maintaining competitiveness and market position, and knowledge of information services and procedures that are crucial for daily operations. In many cases, those who have such access do indeed require it in order to conduct their expected workload. However, should an individual choose to act against the organisation, then with their privileged access and their extensive knowledge, they are well positioned to cause serious damage. Insider threat is becoming a serious and increasing concern for many organisations, with those who have fallen victim to such attacks suffering significant damages including financial and reputational. It is clear then, that there is a desperate need for more effective tools for detecting the presence of insider threats and analyzing the potential of threats before they escalate. We propose Corporate Insider Threat Detection (CITD), an anomaly detection system that is the result of a multi-disciplinary research project that incorporates technical and behavioural activities to assess the threat posed by individuals. The system identifies user and role-based profiles, and measures how users deviate from their observed behaviours to assess the potential threat that a series of activities may pose. In this paper, we present an overview of the system and describe the concept of operations and practicalities of deploying the system. We show how the system can be utilised for unsupervised detection, and also how the human analyst can engage to provide an active learning feedback loop. By adopting an accept or reject scheme, the analyst is capable of refining the underlying detection model to better support their decision-making process and significant reduce the false positive rate.
Keywords: business data processing; learning (artificial intelligence); security of data; CITD; active learning feedback loop; anomaly detection system; authorised access; corporate insider threat detection; decision making process; insider attack; multidisciplinary research project; sensitive company records; unsupervised detection; Analytical models; Business; Electronic mail; Feature extraction; Libraries; Measurement; Media (ID#: 16-9658)


S. Biradar, S. B. Malipatil and C. Naikodi, “Releasing Energy of Compromised Nodes in a Secured Heterogeneous Ad-Hoc Network (MANETs),” Advanced Computing and Communication Systems, 2015 International Conference on, Coimbatore, 2015, pp. 1-6. doi: 10.1109/ICACCS.2015.7324103
Abstract: Heterogeneous Nodes in a Mobile Ad Hoc NET-work(MANET) are having very constrained resources like memory, bandwidth, CPU speed, battery life etc. Here, Heterogeneous Nodes means, all/few nodes are having variety of functionality. Irrespective of having higher secured nodes and security algorithms in MANETs, some time, the honest nodes can be accessed by fraud/malicious nodes or simply attacked by cracking security walls, in the rare case a node itself can also turn into a malicious node or acting on abnormal behaviour. This kind of scenario makes hard for tuning, hence human may not be able to acquire and catch a fraud/malicious/turned node to avoid adversary affect or misusing node's data for different purpose which is hazardous in some cases like Border Monitoring. In this novel approach, we tweak such scenarios, discharging the energy of heterogeneous node which is a valuable resources of MANETs called as retiring or invalidating a node, hence such node may not be a part of genuine communication.
Keywords: mobile ad hoc networks; resource allocation; telecommunication power management; telecommunication security; Border Monitoring; CPU speed;  battery life; constrained resources; fraud-malicious nodes; heterogeneous nodes; mobile ad hoc network; secured heterogeneous ad-hoc network; security algorithms; Ad hoc networks; Batteries; Mathematical model; Mobile computing Receivers; Routing; Security; MANET; genuine node; invalidating; malicious node (ID#: 16-9659)


G. Bottazzi and G. F. Italiano, “Fast Mining of Large-Scale Logs for Botnet Detection: A Field Study,” Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing (CIT/IUCC/DASC/PICOM), 2015 IEEE International Conference on, Liverpool, 2015, pp. 1989-1996. doi: 10.1109/CIT/IUCC/DASC/PICOM.2015.295
Abstract: Botnets are considered one of the most dangerous species of network-based attack today because they involve the use of very large coordinated groups of hosts simultaneously. The behavioral analysis of computer networks is at the basis of the modern botnet detection methods, in order to intercept traffic generated by malwares for which signatures do not exist yet. Defining a pattern of features to be placed at the basis of behavioral analysis, puts the emphasis on the quantity and quality of information to be caught and used to mark data streams as normal or abnormal. The problem is even more evident if we consider extensive computer networks or clouds. With the present paper we intend to show how heuristics applied to large-scale proxy logs, considering a typical phase of the life cycle of botnets such as the search for C&C Servers through AGDs (Algorithmically Generated Domains), may provide effective and extremely rapid results. The present work will introduce some novel paradigms. The first is that some of the elements of the supply chain of botnets could be completed without any interaction with the Internet, mostly in presence of wide computer networks and/or clouds. The second is that behind a large number of workstations there are usually “human beings” and it is unlikely that their behaviors will cause marked changes in the interaction with the Internet in a fairly narrow time frame. Finally, AGDs can highlight, at the moment, common lexical features, detectable quickly and without using any black/white list.
Keywords: cloud computing; computer network security; data mining; digital signatures; invasive software; AGD; C and C Servers; Internet; abnormal data streams; algorithmically generated domains; botnet detection methods; botnet life cycle; computer network behavioral analysis; feature pattern; information quality; information quantity; large-scale proxy log mining; malwares; network-based attack; normal data streams; workstations; Cloud computing; Data mining; Feature extraction; Malware; Servers; botnet; heuristics; logs; mining; proxy (ID#: 16-9660)


D. Prochazkova, J. Prochazka, Z. Prochazka, H. Patakova and V. Strymplova, “System Approach to Study of Traffic Accidents with Hazardous Substances Presence,” Smart Cities Symposium Prague (SCSP), 2015, Prague, 2015, pp. 1-8. doi: 10.1109/SCSP.2015.7181553
Abstract: The traffic accidents with presence of hazardous substances have been occurred at transportation on roads, rail roads, rivers, seas, oceans and in air. To the origination of such accident there has been contributed the items as: vehicle design, traffic speed, roadway design, environ round roadway, skill and defects in driver's behavior, and also the properties of shipped hazardous substances. On the basis of integral safety concept the considered accidents are solved as mobile sources of risks. The paper contains the results of research obtained by critical analysis of impacts of relevant accidents in the word and in the Czech Republic, and proposals of measures for upgrade of safety of considered shipping that improve protection of humans from hazardous substances.
Keywords: accidents; design engineering; hazards; road safety; road traffic; transportation; Czech Republic; hazardous substances; human protection; integral safety concept; rail roads; roadway design; system approach; traffic accidents; traffic speed; transportation; vehicle design; Accidents; Contamination; Indexes; Pediatrics; Roads; critical infrastructure; human security; impacts; safety
(ID#: 16-9661)


A. Gandhamal and S. Talbar, “Evaluation of Background Subtraction Algorithms for Object Extraction,” Pervasive Computing (ICPC), 2015 International Conference on, Pune, 2015, pp. 1-6. doi: 10.1109/PERVASIVE.2015.7087065
Abstract: There is an increase in need of video surveillance applications. Intelligent video surveillance (IVS) includes public safety and security applications, including authenticity control, crowd flow direction and crowd analysis, human behaviour detection and analysis etc. The critical part of IVS system is proper foreground estimation using background subtraction algorithms. This is a challenging task due to variations in illumination, background motion due cluttering noise like swaying trees, flowing water, etc. and the slow moving objects introduce noise in the background estimated. We have precisely concentrated on such challenges. The purpose of this evaluation is to give an overview and categorization of the approaches based on the performance measures like Precision, Recall, F measures (F1), Similarity, Matching Index and Average Classification Error. And also the available techniques are compared based on the computational complexity parameters in terms of Big-O along with their limitations for the improvement in the efficiency of the background subtraction algorithms.
Keywords: object detection; video surveillance; IVS system; background subtraction algorithms; foreground estimation; intelligent video surveillance; object extraction; Algorithm design and analysis; Brightness; Discrete cosine transforms; Estimation; Indexes; Measurement; Standards; Background Estimation; Background Subtraction; Object Segmentation; Video Surveillance (ID#: 16-9662)


J. Neel et al., “Big RF for Homeland Security Applications,” Technologies for Homeland Security (HST), 2015 IEEE International Symposium on, Waltham, MA, 2015, pp. 1-6. doi: 10.1109/THS.2015.7225294
Abstract: As homeland security network deployments evolve to rely on increasingly large amounts of data from a growing variety of data sources, the ability to synthesize actionable information will become progressively more challenging. A similar problem is seen in the Information Technology (IT) domain, which is pursuing Big Data techniques to gain new insights from the relationships among the mountains of data. We believe that by applying the Big Data lessons learned in the IT world to homeland security networking and electromagnetic spectrum (EMS) problems (an application that we call “Big RF”), networks can be made more effective and efficient, commanders can gain new understanding of behaviors, problems can be identified and rectified more quickly, and many complex network management problems currently requiring human intervention can be automated. This paper examines the parallels between Big Data problems and emerging cognitive radio and related wireless applications, appropriate Big Data tools for Big RF, new Big RF applications for homeland security networks, and other developments needed to enable warfighters, first responders, network managers, and cognitive radios to maximize the capabilities offered by Big Data applied to RF domain problems.
Keywords: Big Data; cognitive radio; national security; Big Data problems; Big RF applications; first responders; homeland security applications; homeland security network deployments; network managers; warfighters; Databases; Electromagnetics; Facebook; NASA; Radio frequency; Big Data; Big RF; Cognitive Radio; Homeland Security; REM (ID#: 16-9663)


M. Alaskar, S. Vodanovich and K. N. Shen, “Evolvement of Information Security Research on Employees’ Behavior: A Systematic Review and Future Direction,” System Sciences (HICSS), 2015 48th Hawaii International Conference on, Kauai, HI, 2015, pp. 4241-4250. doi: 10.1109/HICSS.2015.508
Abstract: Information Security (IS) is one of the biggest concerns for many organizations. This concern has led many to focus a huge effort into studying different IS areas. One of these critical areas is the human aspect, where investigation of employees' behaviors has emerged as an important topic. In this paper, we conduct a systematic review of all empirical studies published on this topic. The review will highlight the theoretical and methodological development and the dissemination of related empirical studies in academic journals throughout the years. At the end of the review, future research considerations are discussed and shared.
Keywords: educational administrative data processing; personnel; publishing; security of data; academic journals; employee behavior; information security; Ethics; Human factors; Information security; Organizations; Systematics (ID#: 16-9664)


N. A. Zanjani, G. Lilis, G. Conus and M. Kayal, “Energy Book for Buildings: Occupants Incorporation in Energy Efficiency of Buildings,” Smart Cities and Green ICT Systems (SMARTGREENS), 2015 International Conference on, Lisbon, 2015, pp. 1-6. doi: (not provided)
Abstract: This paper addresses a bottom-up approach for energy management in buildings. Future smart cities will need smart citizens, thus developing an interface to connect humans to their energy usage becomes a necessity. The goal is to give a touch of energy to occupants' daily behaviours and activities and making them aware of their decisions' consequences in terms of energy consumption, its cost and carbon footprint. Second, to allow people directly interacting and controling their living spaces, that means individual contributions to their feeling of comfort. Finally, a software solution to keep track of all personal energy related events is suggested and its possible features are explained.
Keywords: air pollution; building management systems; buildings (structures); energy conservation; energy consumption; energy management systems; smart cities; bottom-up approach; building energy efficiency; building energy management; carbon footprint; energy booking; energy consumption cost; energy usage; future smart cities; human comfort; occupant incorporation; smart citizens; Buildings; Energy consumption; Energy management; Monitoring; Security; Software; Temperature measurement; BEMS; Building Energy Management System; HBI; Human-Building Interactions; Human-Building Interface; Smart Buildings; Smart Cities; Smart Occupants (ID#: 16-9665)


D. Petters and E. Waters, “Modelling Emotional Attachment: An Integrative Framework for Architectures and Scenarios,” Neural Networks (IJCNN), 2015 International Joint Conference on, Killarney, 2015, pp. 1-8. doi: 10.1109/IJCNN.2015.7280431
Abstract: Humans possess a strong innate predisposition to emotionally attach to familiar people around them who provide physical or emotional security. Attachment Theory describes and explains diverse phenomena related to this predisposition, including: infants using their carers as secure-bases from which to explore, and havens of safety to return to when tired or anxious, the development of attachment patterns over ontogenetic and phylogenetic development, and emotional responses to separation and loss throughout the lifespan. This paper proposes that one way for computational modelling to integrate these phenomena is to organise them within temporally nested scenarios, with moment to moment phenomena organised within ontogenetic and phylogenetic sequences. A number of existing agent-based models and robotic attachment simulations capture attachment behaviour, but individual simulations created with different tools and modelling approaches typically do not integrate easily with each other. Two ways to better integrate attachment model are proposed. First, a number of simulations are described that have been created with the same agent-based modelling toolkit, so showing that moment to moment secure base behaviour and the development of individual differences in attachment security can be simulated with closely related architectural designs. Secondly, an integrative modelling approach is proposed where the evaluation of, and comparison between attachment models is guided by reference to a shared conceptual framework for architectures provided by the CogAff schema. This approach can integrate a broad range of emotional processes including: the formation of a set of richer internal representations; and loss of control that can occur in emotional episodes.
Keywords: psychology; CogAff schema; agent-based model; agent-based modelling toolkit; architectural design; attachment security; attachment theory; computational modelling; emotional security; integrative modelling approach; phylogenetic development; robotic attachment simulation; Bioinformatics; Biological system modeling; Computer architecture; Genomics; Phylogeny; Robots
(ID#: 16-9666)


Tao Feng et al., “An Investigation on Touch Biometrics: Behavioral Factors on Screen Size, Physical Context and Application Context,” Technologies for Homeland Security (HST), 2015 IEEE International Symposium on, Waltham, MA, 2015, pp. 1-6. doi: 10.1109/THS.2015.7225318
Abstract: With increasing privacy concerns and security demands present within mobile devices, behavioral biometric solutions, such as touch based user recognition, have been researched as of recent. However, several vital contextual behavior factors (i.e., screen size, physical and application context) and how those effect user identification performance, remains unaddressed in previous studies. In this paper we first introduce a context-aware mobile user recognition method. Then a comparative experiment to evaluate the impacts of these factors in relation to user identification performance is presented. Experimental results have demonstrated that a user's touch screen usage behavior may be affected given different contextual behavior information. Furthermore, several interesting occurrences have been found in the results: (1) screen size of a smartphone device changes the way a user touches and holds the device. A larger screen size will provide more potential methods of interacting with the device and in effect, a higher user recognition accuracy as well; and (2) application context and physical activity context can aid in achieving higher accuracy for user recognition.
Keywords: behavioural sciences; biometrics (access control); data privacy; human computer interaction; mobile computing; social aspects of automation; touch sensitive screens; application context; behavioral biometric solutions; behavioral factors; context-aware mobile user recognition method; contextual behavior factors; contextual behavior information; mobile devices; physical activity context; privacy concerns; security demands; smartphone device screen size; touch based user recognition; touch biometrics; touch screen usage behavior; user identification performance; Authentication; Biometrics (access control); Context; Feature extraction; Mobile communication; Mobile handsets; Performance evaluation (ID#: 16-9667)


J. Chen, F. Shen, D. Z. Chen and P. J. Flynn, “Iris Recognition Based on Human-Interpretable Features,” Identity, Security and Behavior Analysis (ISBA), 2015 IEEE International Conference on, Hong Kong, 2015, pp. 1-6. doi: 10.1109/ISBA.2015.7126352
Abstract: The iris is a stable biometric that has been widely used for human recognition in various applications. However, official deployment of the iris in forensics has not been reported. One of the main reasons is that the current iris recognition techniques in hard to visually inspect by examiners. To further promote the maturity of iris recognition in forensics, one way is to make the similarity between irises visualizable and interpretable. Recently, a human-in-the-loop iris recognition system was developed, based on detecting and matching iris crypts. Building on this framework, we propose a new approach for detecting and matching iris crypts automatically. Our detection method is able to capture iris crypts of various sizes. Our matching scheme is designed to handle potential topological changes in the detection of the same crypt in different acquisitions. Our approach outperforms the known visible feature based iris recognition method on two different datasets, by over 19% higher rank one hit rate in identification and over 46% lower equal error rate in verification.
Keywords: feature extraction; image capture; image matching; iris recognition; object detection; topology; biometrics; equal error rate; forensics; hit rate; human recognition; human-in-the-loop iris recognition system; human-interpretable features; iris crypt detection; iris crypt matching; iris crypts capture; topological changes; Cryptography; Feature extraction; Forensics; Gray-scale; Image segmentation; Iris; Iris recognition (ID#: 16-9668)


M. Mitchell, R. Patidar, M. Saini, P. Singh, A. I. Wang and P. Reiher, “Mobile Usage Patterns and Privacy Implications,” Pervasive Computing and Communication Workshops (PerCom Workshops), 2015 IEEE International Conference on, St. Louis, MO, 2015, pp. 457-462. doi: 10.1109/PERCOMW.2015.7134081
Abstract: Privacy is an important concern for mobile computing. Users might not understand the privacy implications of their actions and therefore not alter their behavior depending on where they move, when they do so, and who is in their surroundings. Since empirical data about the privacy behavior of users in mobile environments is limited, we conducted a survey study of ~600 users recruited from Florida State University and Craigslist. Major findings include: (1) People often exercise little caution preserving privacy in mobile computing environments; they perform similar computing tasks in public and private. (2) Privacy is orthogonal to trust; people tend to change their computing behavior more around people they know than strangers. (3) People underestimate the privacy threats of mobile apps, and comply with permission requests from apps more often than operating systems. (4) Users' understanding of privacy is different from that of the security community, suggesting opportunities for additional privacy studies.
Keywords: data privacy; human factors; mobile computing; operating systems (computers); Craigslist; Florida State University; empirical data; mobile applications; mobile computing environments; mobile usage patterns; operating systems; permission requests; privacy threats; security community; user computing behavior; users privacy behavior; Encryption; IEEE 802.11 Standards; Mobile communication; Mobile computing; Mobile handsets; Portable computers; Privacy; privacy; security (ID#: 16-9669)


Weihui Zhu, Xiang Fu and Weihong Han, “Online Anomaly Detection on E-Commerce Based on Variable-Length Behavior Sequence,” 11th International Conference on Wireless Communications, Networking and Mobile Computing (WiCOM 2015), Shanghai, 2015, pp. 1-8. doi: 10.1049/cp.2015.0757
Abstract: User behavior-based anomaly detection is currently one of the major concerns of system security research. For ecommerce, this paper proposes an online anomaly detection method, based on variable-length sequences of user behavior. The algorithm includes a training stage and a detection stage. In the training stage, we mainly use the variable-length sequences to represent the correlation between the contiguous operations, and also the correlation between the related items. It makes the representation ability of our model stronger. In the detection stage, in consideration of the legitimate user's behavior patterns likely having a deviation from normal behavior patterns and the illegitimate user's behavior patterns likely being consistent with normal behavior patterns in short time, we use a windowed smooth approach to avoid such problems affecting the result when the decision value is calculated. Meanwhile, we calculate the IDF-value of every pattern in the normal user behavior pattern database, and then the pattern whose IDF-value is below the threshold would be ignored in the detection stage (The lower the IDF-value, the lower the degree of recognition). Experimental results show that our algorithm can detect anomaly in real time effectively, and could meet the needs of real-time process both in accuracy and speed.
Keywords: electronic commerce; human computer interaction; security of data; e-commerce; online anomaly detection method; system security; variable-length behavior sequence; Anomaly Detection; Electronic Commerce;  IDF; User Behavior; Variable-Length Sequence (ID#: 16-9670)


S. J. Elliott, K. O’Connor, E. Bartlow, J. J. Robertson and R. M. Guest, “Expanding the Human-Biometric Sensor Interaction Model to Identity Claim Scenarios,” Identity, Security and Behavior Analysis (ISBA), 2015 IEEE International Conference on, Hong Kong, 2015, pp. 1-6. doi: 10.1109/ISBA.2015.7126362
Abstract: Biometric technologies represent a significant component of comprehensive digital identity solutions, and play an important role in crucial security tasks. These technologies support identification and authentication of individuals based on their physiological and behavioral characteristics. This has led many governmental agencies to choose biometrics as a supplement to existing identification schemes, most prominently ID cards and passports. Studies have shown that the success of biometric systems relies, in part, on how humans interact and accept such systems. In this paper, the authors build on previous work related to the Human-Biometric Sensor Interaction (HBSI) model and examine it with respect to the introduction of a token (e.g. an electronic passport or identity card) into the biometric system. The role of the imposter within an Identity Claim scenario has been integrated to expand the HBSI model into a full version, which is able to categorise potential False Claims and Attack Presentations.
Keywords: biometrics (access control); sensors; HBSI model; ID cards; attack presentations; behavioral characteristics; biometric technologies; digital identity solutions; false claims; governmental agencies; human-biometric sensor interaction model; identity claim scenarios; individual authentication; individual identification; passports; physiological characteristics; security task; token; Adaptation models; Authentication; Biological system modeling; Fingerprint recognition; Measurement; Usability (ID#: 16-9671)


A. Al-Nemrat and C. Benzaid, “Cybercrime Profiling: Decision-Tree Induction, Examining Perceptions of Internet Risk and Cybercrime Victimisation,” Trustcom/BigDataSE/ISPA, 2015 IEEE, Helsinki, 2015, pp. 1380-1385. doi: 10.1109/Trustcom.2015.534
Abstract: The Internet can be a double-edged sword. While offering a range of benefits, it also provides an opportunity for criminals to extend their work to areas previously unimagined. Every country faces the same challenges regarding the fight against cybercrime and how to effectively promote security for its citizens and organisations. The main aim of this study is to introduce and apply a data-mining technique (decision-tree) to cybercrime profiling. This paper also aims to draw attention to the growing number of cybercrime victims, and the relationship between online behaviour and computer victimisation. This study used secondhand data collected for a study was carried out using Jordan a s a case study to investigate whether or not individuals effectively protect themselves against cybercrime, and to examine how perception of law influences actions towards incidents of cybercrime. In Jordan, cybercafes have become culturally acceptable alternatives for individuals wishing to access the Internet in private, away from the prying eyes of society.
Keywords: Internet; computer crime; data mining; decision trees; human computer interaction; law; Internet risk perceptions; Jordan; computer victimisation; cybercrime profiling; cybercrime victimisation; data-mining technique; decision-tree induction; law perception; online behaviour; Additives; Complexity theory; Computer crime; Decision trees; Electronic mail; Classification tree; Cybercrime profiling; Data mining; Digital forensics (ID#: 16-9672)


J. S. Wu, W. C. Lin, C. T. Lin and T. E. Wei, “Smartphone Continuous Authentication Based on Keystroke and Gesture Profiling,” Security Technology (ICCST), 2015 International Carnahan Conference on, Taipei, 2015, pp. 191-197. doi: 10.1109/CCST.2015.7389681
Abstract: Recently, smartphones have become increasingly popular, whereas data leakage continues to be a serious problem for many large organizations. Consequently, smartphone applications containing sensitive personal or company data are at risk when targeted by attackers. Continuous and passive authentication is a popular scheme for secretly classifying users' identities based on their own unique touch motions (i.e., keystrokes and gestures). However, previous methods are inadequate when classifying users' singular touch motions. In this paper, we propose a novel continuous authentication method. The proposed method not only profiles behavioral biometrics from keystrokes and gestures, it also acquires the specific properties of a one-touch motion during the user's interaction with the smartphone. We demonstrate that the manner by which a user uses the touchscreen - that is, the specific location touched on the screen, the drift from when a finger moves up and down, the area touched, and the pressure used - reflects unique physical and behavioral biometrics. Moreover, the speed of the Gesture Segment (GS) is defined to extract a meaningful velocity segment. Experiments conducted to evaluate the proposed method for combining keystroke and gesture behavior demonstrate its effectiveness and accuracy.
Keywords: gesture recognition; human computer interaction; message authentication; pattern classification; smart phones; touch sensitive screens; GS; gesture profiling; gesture segment; keystroke profiling; one-touch motion; smart phone continuous authentication; touch screen; user identity classification; user interaction; Authentication; Feature extraction; Iris recognition; Mobile communication; Mobile handsets; data leakage prevention; machine learning; sensitive data protection; smartphone movement behavior (ID#: 16-9673)


W. R. Flores, H. Holm, M. Ekstedt and M. Nohlberg, “Investigating the Correlation Between Intention and Action in the Context of Social Engineering in Two Different National Cultures,” System Sciences (HICSS), 2015 48th Hawaii International Conference on, Kauai, HI, 2015, pp. 3508-3517. doi: 10.1109/HICSS.2015.422
Abstract: In this paper, we shed a light on the intention-action relationship in the context of external behavioral information security threats. Specifically, external threats caused by employees' social engineering security actions were examined. This was done by examining the correlation between employees' reported intention to resist social engineering and their self-reported actions of hypothetical scenarios as well as observed action in a phishing experiment. Empirical studies including 1787 employees pertaining to six different organizations located in Sweden and USA laid the foundation for the statistical analysis. The results suggest that employees' intention to resist social engineering has a significant positive correlation of low to medium strength with both self-reported action and observed action. Furthermore, a significant positive correlation between social engineering actions captured through written scenarios and a phishing experiment was identified. Due to data being collected from employees from two different national cultures, an exploration of potential moderating effect based on national culture was also performed. Based on this analysis we identified that the examined correlations differ between Swedish, and US employees. The findings have methodological contribution to survey studies in the information security field, showing that intention and self-reported behavior using written scenarios can be used as proxies of observed behavior under certain cultural contexts rather than others. Hence, the results support managers operating in a global environment when assessing external behavioral information security threats in their organization.
Keywords: behavioural sciences computing; cultural aspects; human factors; personnel; security of data; social sciences computing; statistical analysis; Sweden; Swedish employees; US employees; USA; employee intention; employee social engineering security actions; external behavioral information security threats; information security field; intention-action correlation; intention-action relationship; national cultures; phishing experiment; self-reported action; self-reported behavior; Context; Correlation; Cultural differences; Information security; Organizations; Resists (ID#: 16-9674)


G. Shikkenawis and S. K. Mitra, “Locality Preserving Discriminant Projection,” Identity, Security and Behavior Analysis (ISBA), 2015 IEEE International Conference on, Hong Kong, 2015, pp. 1-6. doi: 10.1109/ISBA.2015.7126365
Abstract: Face is the most powerful biometric as far as human recognition system is concerned which is not the case for machine vision. Face recognition by machine is yet incomplete due to adverse, unconstrained environment. Out of several attempts made in past few decades, subspace based methods appeared to be more accurate and robust. In the present proposal, a new subspace based method is developed. It preserves the local geometry of data points, here face images. In particular, it keeps the neighboring points which are from the same class close to each other and those from different classes far apart in the subspace. The first part can be seen as a variant of locality preserving projection (LPP) and the combination of both the parts is mentioned as locality preserving discriminant projection (LPDP). The performance of the proposed subspace based approach is compared with a few other contemporary approaches on some benchmark databases for face recognition. The current method seems to perform significantly better.
Keywords: biometrics (access control); face recognition; geometry; visual databases; LPDP; LPP; benchmark databases; contemporary approach; data point local geometry; face image; human recognition system; locality preserving discriminant projection; subspace based method; Benchmark testing; Databases; Error analysis; Face; Face recognition; Lighting; Training (ID#: 16-9675)


A. Farooq, J. Isoaho, S. Virtanen and J. Isoaho, “Information Security Awareness in Educational Institution: An Analysis of Students’ Individual Factors,” Trustcom/BigDataSE/ISPA, 2015 IEEE, Helsinki, 2015, pp. 352-359. doi: 10.1109/Trustcom.2015.394
Abstract: The purpose of this paper is to study information security awareness (ISA) among university students and further analyze how different individual factors impact it. Through descriptive survey approach, a questionnaire consisting of 30 items was circulated in our university, resulting in 614 usable responses. Here the ISA is considered as a combination of knowledge and behavior. Factors such as age, gender, level of education, field of study, nationality, area of living, working experience and ISA training are considered as individual factors. Perceived ISA level among the students is also examined. For the overall study, arithmetic mean and standard deviation are used. For analyzing the effect of different individual factors, Pearson's coefficient of correlation is computed. Gender, living place and information security related training have statistically significant correlation with attained ISA level, whereas, factors such as age, nationality, discipline and level of education have statistically insignificant correlation with attained ISA level. Furthermore, gender and training have statistical significant correlation with the perceived ISA as well as the dimensions of ISA, that is, knowledge and behavior. Factors such as age and experience have significant correlation with perceived ISA, whereas, living area correlates with knowledge only.
Keywords: age issues; educational administrative data processing; educational institutions; gender issues; human factors; security of data; statistical analysis; training; ISA training factor; age factor; area-of-living factor; education level factor; educational institution; field-of-study factor; gender factor; information assets; information security awareness; nationality factor; statistical significant correlation; students individual factors analysis; working experience factor; Context; Correlation; Information security; Information technology; Training; Age; Behavior; Demographic Factors; Educational Disciplines; Gender; Information Security Awareness; Knowledge; Miscellaneous Security Issues; Security; Threats (ID#: 16-9676)


F. Yao, S. Y. Yerima, B. Kang and S. Sezer, “Event-Driven Implicit Authentication for Mobile Access Control,” Next Generation Mobile Applications, Services and Technologies, 2015 9th International Conference on, Cambridge, 2015, pp. 248-255. doi: 10.1109/NGMAST.2015.47
Abstract: In order to protect user privacy on mobile devices, an event-driven implicit authentication scheme is proposed in this paper. Several methods of utilizing the scheme for recognizing legitimate user behavior are investigated. The investigated methods compute an aggregate score and a threshold in real-time to determine the trust level of the current user using real data derived from user interaction with the device. The proposed scheme is designed to: operate completely in the background, require minimal training period, enable high user recognition rate for implicit authentication, and prompt detection of abnormal activity that can be used to trigger explicitly authenticated access control. In this paper, we investigate threshold computation through standard deviation and EWMA (exponentially weighted moving average) based algorithms. The result of extensive experiments on user data collected over a period of several weeks from an Android phone indicates that our proposed approach is feasible and effective for lightweight real-time implicit authentication on mobile smartphones.
Keywords: authorisation; data privacy; human computer interaction; message authentication; mobile computing; mobile radio; moving average processes; telecommunication security; trusted computing; EWMA; abnormal activity detection; aggregate score; event-driven implicit authentication scheme; explicitly authenticated access control; exponentially weighted moving average based algorithms; legitimate user behavior recognition; mobile access control; mobile devices; standard deviation; threshold computation; trust level; user interaction; user privacy protection; user recognition rate; Aggregates; Authentication; Browsers; Context; History; Mobile handsets; Training; behavior-based authentication; implict authentication; mobile access control (ID#: 16-9677)


A. K. Lim and C. Thuemmler, “Opportunities and Challenges of Internet-Based Health Interventions in the Future Internet,” Information Technology - New Generations (ITNG), 2015 12th International Conference on, Las Vegas, NV, 2015, pp. 567-573. doi: 10.1109/ITNG.2015.95
Abstract: Internet-based health interventions are behavioral treatments aim at changing behaviors to promote healthy living and prevent diseases and illness. This paper first discusses the benefits and effectiveness of Internet-based health interventions. It continues to explore the opportunities and challenges of Internet-based health interventions made possible by the Future Internet and emerging technologies. Identifying the psychological and social barriers can help to improve the delivery of healthcare interventions in a number of ways, including assuring privacy and security, building trust and promoting equal access. Addressing these barriers can ultimately lead to greater acceptance of new technologies and improved health outcomes.
Keywords: Internet; health care; human factors; Internet-based health interventions; behavioral treatments; future Internet; psychological barriers; social barriers; technology acceptance; Data mining; Diseases; Mobile communication; Privacy; Psychology; Future Internet; Internet of Everything; Internet of Things; Psychological and Social Barriers; eHealth (ID#: 16-9678)


Z. Sahnoune, E. Aïmeur, G. E. Haddad and R. Sokoudjou, “Watch Your Mobile Payment: An Empirical Study of Privacy Disclosure,” Trustcom/BigDataSE/ISPA, 2015 IEEE, Helsinki, 2015, pp. 934-941. doi: 10.1109/Trustcom.2015.467
Abstract: Using a smartphone as payment device has become a highly attractive feature that is increasingly influencing user acceptance. Electronic wallets, near field communication, and mobile shopping applications, are all incentives that push users to adopt m-payment. Hence, this makes the sensitive data that already exists on everyone's smartphone easily collated to their financial transaction details. In fact, misusing m-payment can be a real privacy threat. The existing privacy issues regarding m-payment are already numerous, and can be caused by different factors. We investigate, through an empirical survey-based study, the different factors and their potential correlations and regression values. We identify three factors that influence directly privacy disclosure: the user's privacy concerns, his risk perception, and the protection measure appropriateness. These factors are impacted by indirect ones, which are linked to the users' and the technology's characteristics, and the behaviour of institutions and companies. In order to analyse the impact of each factor, we define a new research model for privacy disclosure based on several hypotheses. The study is mainly based on a five-item scale survey, and on the modelling of structural equations. In addition to the impact estimations for each factor, our study results indicate that the privacy disclosure in m-payment is primarily caused by the “protection measure appropriateness”, which, in its turn, impacted by “the m-payment convenience.” We discuss in this paper the research model, the methodology, the findings and their significance.
Keywords: Internet; data privacy; human factors; mobile commerce; near-field communication; regression analysis; risk analysis; smart phones; electronic wallets; financial transaction details; m-payment; mobile payments; mobile shopping applications; near field communication; payment device; privacy disclosure; privacy threat; regression values; risk perception; smartphone; structural equation modelling; technology characteristics; user acceptance; user privacy concerns; Context; Data privacy; Mobile communication; Mobile handsets; Privacy; Security; Software; privacy concerns; privacy perception; privacy policies; structural equation modeling
(ID#: 16-9679)


D. Rissacher and D. Galy, “Cardiac Radar for Biometric Identification Using Nearest Neighbour of Continuous Wavelet Transform Peaks,” Identity, Security and Behavior Analysis (ISBA), 2015 IEEE International Conference on, Hong Kong, 2015, pp. 1-6. doi: 10.1109/ISBA.2015.7126356
Abstract: This work explores the use of cardiac data acquired by a 2.4 GHz radar system as a potential biometric identification tool. Monostatic and bistatic systems are used to record data from human subjects over two visits. Cardiac data is extracted from the radar recordings and an ensemble average is computed using ECG as a time reference. The Continuous Wavelet Transform is then computed to provide time-frequency analysis of the average radar cardiac cycle and a nearest neighbor technique is applied to demonstrate that a cardiac radar system has some promise as a biometric identification technology currently producing Rank-1 accuracy of 19% and Rank-5 accuracy of 42% over 26 subjects.
Keywords: biometrics (access control); electrocardiography; medical signal processing; time-frequency analysis; wavelet transforms; ECG; biometric identification; biometric identification tool; cardiac radar system; continuous wavelet transform; continuous wavelet transform peaks; nearest neighbour; radar system; time reference; time-frequency analysis; Accuracy; Continuous wavelet transforms; Electrocardiography; Feature extraction; Radar (ID#: 16-9680)


A. Aggarwal and P. Kumaraguru, “What They Do in Shadows: Twitter Underground Follower Market,” Privacy, Security and Trust (PST), 2015 13th Annual Conference on, Izmir, 2015, pp. 93-100. doi: 10.1109/PST.2015.7232959
Abstract: Internet users and businesses are increasingly using online social networks (OSN) to drive audience traffic and increase their popularity. In order to boost social presence, OSN users need to increase the visibility and reach of their online profile, like - Facebook likes, Twitter followers, Instagram comments and Yelp reviews. For example, an increase in Twitter followers not only improves the audience reach of the user but also boosts the perceived social reputation and popularity. This has led to a scope for an underground market that provides followers, likes, comments, etc. via a network of fraudulent and compromised accounts and various collusion techniques. In this paper, we landscape the underground markets that provide Twitter followers by studying their basic building blocks - merchants, customers and phony followers. We charecterize the services provided by merchants to understand their operational structure and market hierarchy. Twitter underground markets can operationalize using a premium monetary scheme or other incentivized freemium schemes. We find out that freemium market has an oligopoly structure with few merchants being the market leaders. We also show that merchant popularity does not have any correlation with the quality of service provided by the merchant to its customers. Our findings also shed light on the characteristics and quality of market customers and the phony followers provided by underground market. We draw comparison between legitimate users and phony followers, and find out key identifiers to separate such users. With the help of these differentiating features, we build a supervised learning model to predict suspicious following behaviour with an accuracy of 89.2%.
Keywords: human factors; learning (artificial intelligence); oligopoly; social networking (online); Facebook likes; Instagram comments; OSN users; Twitter followers; Yelp reviews; customers; fraudulent network; incentivized freemium schemes; market hierarchy; market leaders; merchant popularity; oligopoly structure; online profile; online social networks; operational structure; perceived social popularity; perceived social reputation; phony followers; premium monetary scheme; quality of service; social presence; supervised learning model; suspicious following behaviour prediction; underground follower market; Business; Data collection; Facebook; Measurement; Media; Quality of service; Twitter (ID#: 16-9681)


R. Subramanian et al., “Orientation Invariant Gait Matching Algorithm Based on the Kabsch Alignment,” Identity, Security and Behavior Analysis (ISBA), 2015 IEEE International Conference on, Hong Kong, 2015, pp. 1-8. doi: 10.1109/ISBA.2015.7126347
Abstract: Accelerometer and gyroscope sensors in smart phones capture the dynamics of human gait that can be matched to arrive at identity authentication measures of the person carrying the phone. Any such matching method has to take into account the reality that the phone may be placed at uncontrolled orientations with respect to the human body. In this paper, we present a novel orientation invariant gaitmatching algorithm based on the Kabsch alignment. The algorithm consists of simple, intuitive, yet robust methods for cycle splitting, aligning orientation, and comparing gait signals. We demonstrate the effectiveness of the method using a dataset from 101 subjects, with the phone placed in uncontrolled orientations in the holster and in the pocket, and collected on different days. We find that the orientation invariant gait algorithm results in a significant reduction in error: up to a 9% reduction in equal error rate, from 30.4% to 21.5% when comparing data captured on different days. On the McGill dataset from 20 subjects, which is the other dataset with orientation variation, we find a more pronounced effect; the identification rate increased from 67.5% to 96.5%. On the OU-ISIR data, which has data from 745 subjects, the equal error rates are as low as 6.3%, which is among the best reported in the literature.
Keywords: accelerometers; gait analysis; gyroscopes; image matching; smart phones; Kabsch alignment; McGill dataset; OU-ISIR data; accelerometer; cycle splitting; gait signal comparison; gyroscope sensors; human gait dynamics; identity authentication measures; orientation alignment; orientation invariant gait matching algorithm; Acceleration; Gravity; Gyroscopes; Intelligent sensors; Legged locomotion; Probes; Distribution Statement A: Approved for Public release; Distribution Unlimited (ID#: 16-9682)


S. Intarasothonchun and W. Srimuang, “Improving Performance of Classification Intrusion Detection Model by Weighted Extreme Learning Using Behavior Analysis of the Attack,” 2015 International Computer Science and Engineering Conference (ICSEC), Chiang Mai, 2015, pp. 1-5. doi: 10.1109/ICSEC.2015.7401431
Abstract: This research was aimed to develop classification intrusion detection model by Weighted ELM which presented in [8], bringing analysis of 42 attributes to find the ones related to each format of attack, remaining only 13 attributes which were chosen to use in Weighted ELM working system in order to classify various attack formats and compared to experimental result with SVM+GA [7] and Weighted ELM techniques [8]. The result showed that New Weighted ELM was quite accurate in classifying every format of attack, which the presented working system of the method used RBF Kernel Activation Function and defined Trade-off Constant C value at 22 = 4, giving validity value to be Normal = 99.21%, DoS = 99.97%, U2R = 99.59%, R2L - 99.04% and Probing Attack = 99.13%, average validity value was at 99.39% Comparing to Weighted ELM in [8], found that, the presented method could improve the effectiveness of the former method enable to more classify R2L from 93.94% to 99.04%, and from 96.94% to 99.13% for Probing Attack meanwhile DoS and U2R had lower effectiveness, yet there was resemble effectiveness.
Keywords: learning (artificial intelligence); pattern classification; security of data; RBF kernel activation function; SVM+GA; behavior analysis; classification intrusion detection model; probing attack; weighted ELM; weighted extreme learning method; High definition video; Intrusion Detection; Trade-off Constant C; Weighted ELM; behavior analysis (ID#: 16-9683)


M. A. E. Fadl, B. Abbey and K. S. Choi, “Effect of IT Trading Platform on Financial Risk-Taking and Portfolio Performance,” System Sciences (HICSS), 2015 48th Hawaii International Conference on, Kauai, HI, 2015, pp. 3298-3306. doi: 10.1109/HICSS.2015.398
Abstract: As a fast growing area in Finance, Information technology (IT) plays an important role in how traders trade online. Investigating whether online trading has a significant effect on financial returns and risks is central to this inquiry. This study, using perceived usefulness and satisfaction categories, addresses how the IT trading platform affects the trader's trading risk-taking behavior and stock portfolio performance. We examined two unique data sets: 2,726 proprietary online trading accounts and 178 professional investors' field survey. The results revealed that while the perceived usefulness category presented significant differences between the risk-taking groups and significant impact on stock portfolio performance, the satisfaction category showed no significant results.
Keywords: electronic commerce; human factors; investment; risk management; IT trading platform; financial returns; financial risk-taking; information technology; online trading; perceived usefulness category; professional investors; risk-taking groups; satisfaction category; stock portfolio performance; trading risk-taking behavior; Biological system modeling; Computers; Customer satisfaction; Finance; Portfolios; Security (ID#: 16-9684)


J. B. Fernando and K. Morikawa, “Improvement of Human Identification Accuracy by Wavelet of Peak-Aligned ECG,” Identity, Security and Behavior Analysis (ISBA), 2015 IEEE International Conference on, Hong Kong, 2015, pp. 1-6. doi: 10.1109/ISBA.2015.7126358
Abstract: In this paper, a novel method of human identification using electrocardiogram (ECG) is proposed. In the method, while normalizing RR interval, in addition to normalized signal where time interval of P wave, Q wave, R wave, S wave relatively to R wave is unaligned, normalized signal where time interval of those peaks is aligned is also generated. Wavelet transform is then applied to both normalized signals and feature vector is extracted from their wavelet coefficients. ECG data are collected from 10 subjects using a pair of dry electrodes which are held by two fingers. Experiment results show that adding wavelet of peak-aligned ECG improves the classification accuracy, where the maximum accuracy is 100%, 97%, and 90% for data measured in more than 20 seconds, 5 seconds, and 3 seconds respectively.
Keywords: electrocardiography; feature extraction; medical signal processing; signal classification; wavelet transforms; P wave time interval; Q wave time interval; R wave time interval; RR interval normalization; S wave time interval; classification accuracy improvement; dry electrodes; electrocardiogram; feature vector; human identification accuracy; normalized signal; peak-aligned ECG wavelets; unaligned R wave; wavelet coefficients; wavelet transform; Accuracy; Electrocardiography; Electrodes; Feature extraction; Time measurement; Wavelet transforms (ID#: 16-9685)


Nai-Wei Lo, Chi- Kai Yu and Chao Yang Hsu, “Intelligent Display Auto-Lock Scheme for Mobile Devices,” Information Security (AsiaJCIS), 2015 10th Asia Joint Conference on, Kaohsiung, 2015, pp. 48-54. doi: 10.1109/AsiaJCIS.2015.30
Abstract: In recent years people in modern societies have heavily relied on their own intelligent mobile devices such as smartphones and tablets to get personal services and improve work efficiency. In consequence, quick and simple authentication mechanisms along with energy saving consideration are generally adopted by these smart handheld devices such as screen auto-lock schemes. When a smart device activates its screen lock mode to protect user privacy and data security on this device, its screen auto-lock scheme will be executed at the same time. Device user can setup the length of time period to control when to activate the screen lock mode of a smart device. However, it causes inconvenience for device users when a short time period is set for invoking screen auto-lock. How to get balance between security and convenience for individual users to use their own smart devices has become an interesting issue. In this paper, an intelligent display (screen) auto-lock scheme is proposed for mobile users. It can dynamically adjust the unlock time period setting of an auto-lock scheme based on derived knowledge from past user behaviors.
Keywords: authorisation; data protection; display devices; human factors; mobile computing; smart phones; authentication mechanisms; data security; energy saving; intelligent display auto-lock scheme; intelligent mobile devices; mobile users; personal services; screen auto-lock schemes; smart handheld devices;  tablets; unlock time period; user behaviors; user convenience; user privacy protection; user security; work efficiency improvement; Authentication; IEEE 802.11 Standards; Mathematical model; Smart phones; Time-frequency analysis; Android platform; display auto-lock; smartphone (ID#: 16-9686)




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