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Igarashi, Takeo, Shono, Naoyuki, Kin, Taichi, Saito, Toki.  2016.  Interactive Volume Segmentation with Threshold Field Painting. Proceedings of the 29th Annual Symposium on User Interface Software and Technology. :403–413.

An interactive method for segmentation and isosurface extraction of medical volume data is proposed. In conventional methods, users decompose a volume into multiple regions iteratively, segment each region using a threshold, and then manually clean the segmentation result by removing clutter in each region. However, this is tedious and requires many mouse operations from different camera views. We propose an alternative approach whereby the user simply applies painting operations to the volume using tools commonly seen in painting systems, such as flood fill and brushes. This significantly reduces the number of mouse and camera control operations. Our technical contribution is in the introduction of the threshold field, which assigns spatially-varying threshold values to individual voxels. This generalizes discrete decomposition of a volume into regions and segmentation using a constant threshold in each region, thereby offering a much more flexible and efficient workflow. This paper describes the details of the user interaction and its implementation. Furthermore, the results of a user study are discussed. The results indicate that the proposed method can be a few times faster than a conventional method.

Igbe, O., Saadawi, T..  2018.  Insider Threat Detection using an Artificial Immune system Algorithm. 2018 9th IEEE Annual Ubiquitous Computing, Electronics Mobile Communication Conference (UEMCON). :297—302.
Insider threats result from legitimate users abusing their privileges, causing tremendous damage or losses. Malicious insiders can be the main threats to an organization. This paper presents an anomaly detection system for detecting insider threat activities in an organization using an ensemble that consists of negative selection algorithms (NSA). The proposed system classifies a selected user activity into either of two classes: "normal" or "malicious." The effectiveness of our proposed detection system is evaluated using case studies from the computer emergency response team (CERT) synthetic insider threat dataset. Our results show that the proposed method is very effective in detecting insider threats.
Ignacio X. Dominguez, Alok Goel, David L. Roberts, Robert St. Amant.  2015.  Detecting Abnormal User Behavior Through Pattern-mining Input Device Analytics. Proceedings of the 2015 Symposium and Bootcamp on the Science of Security (HotSoS-15).
Ignacio X. Dominguez, Jayant Dhawan, Robert St. Amant, David L. Roberts.  In Press.  Exploring the Effects of Different Text Stimuli on Typing Behavior. International Conference on Cognitive Modeling.

In this work we explore how different cognitive processes af- fected typing patterns through a computer game we call The Typing Game. By manipulating the players’ familiarity with the words in our game through their similarity to dictionary words, and by allowing some players to replay rounds, we found that typing speed improves with familiarity with words, and also with practice, but that these are independent of the number of mistakes that are made when typing. We also found that users who had the opportunity to replay rounds exhibited different typing patterns even before replaying the rounds. 

Ignacio X. Dominguez, Prairie Rose Goodwin, David L. Roberts, Robert St. Amant.  2016.  Human Subtlety Proofs: Using Computer Games to Model Cognitive Processes for Cybersecurity. International Journal of Human–Computer Interaction. :null.

AbstractThis article describes an emerging direction in the intersection between human-computer interaction and cognitive science: the use of cognitive models to give insight into the challenges of cybersecurity. The article gives a brief overview of work in different areas of cybersecurity where cognitive modeling research plays a role, with regard to direct interaction between end users and computer systems and with regard to the needs of security analysts working behind the scenes. The problem of distinguishing between human users and automated agents (bots) interacting with computer systems is introduced, as well as ongoing efforts toward building Human Subtlety Proofs, persistent and unobtrusive windows into human cognition with direct application to cybersecurity. Two computer games are described, proxies to illustrate different ways in which cognitive modeling can potentially contribute to the development of HSPs and similar cybersecurity applications.

Ignacio X. Dominguez, Prairie Rose Goodwin,, David L. Roberts,, Robert St. Amant.  2016.  Human Subtlety Proofs: Using Computer Games to Model Cognitive Processes for Cybersecurity. International Journal of Human-Computer Interaction, special issue on Cognitive Foundations for Human-Computer Interaction.

This article describes an emerging direction in the intersection between human–computer interaction and cognitive science: the use of cognitive models to give insight into the challenges of cybersecurity (cyber-SA). The article gives a brief overview of work in different areas of cyber-SA where cognitive modeling research plays a role, with regard to direct interaction between end users and computer systems and with regard to the needs of security analysts working behind the scenes. The problem of distinguishing between human users and automated agents (bots) interacting with computer systems is introduced, as well as ongoing efforts toward building Human Subtlety Proofs (HSPs), persistent and unobtrusive windows into human cognition with direct application to cyber-SA. Two computer games are described, proxies to illustrate different ways in which cognitive modeling can potentially contribute to the development of HSPs and similar cyber-SA applications.

 

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Ignacio X. Dominguez, Jayant Dhawan, Robert St. Amant, David L. Roberts.  2016.  JIVUI: JavaScript Interface for Visualization of User Interaction. Proceedings of the International Conference on Cognitive Modeling (ICCM). :125–130.

In this paper we describe the JavaScript Interface for Visu- alization of User Interaction (JIVUI): a modular, Web-based, and customizable visualization tool that shows an animation of the trace of a user interaction with a graphical interface, or of predictions made by cognitive models of user interaction. Any combination of gaze, mouse, and keyboard data can be repro- duced within a user-provided interface. Although customiz- able, the tool includes a series of plug-ins to support common visualization tasks, including a timeline of input device events and perceptual and cognitive operators based on the Model Hu- man Processor and TYPIST. We talk about our use of this tool to support hypothesis generation, assumption validation, and to guide our modeling efforts. 

Ignacio X. Dominguez, Jayant Dhawan, Robert St. Amant, David L. Roberts.  2016.  Exploring the effects of different text stimuli on typing behavior. Proceedings of the International Conference on Cognitive Modeling {(ICCM)}. :175–181.
Ignacio X. Dominguez, Prairie Rose Goodwin, David L. Roberts, Robert St. Amant.  2016.  Human Subtlety Proofs: Using Computer Games to Model Cognitive Processes for Cybersecurity. International Journal of Human–Computer Interaction.

This article describes an emerging direction in the intersection between human-computer interaction and cognitive science: the use of cognitive models to give insight into the challenges of cybersecurity. The article gives a brief overview of work in different areas of cybersecurity where cognitive modeling research plays a role, with regard to direct interaction between end users and computer systems and with regard to the needs of security analysts working behind the scenes. The problem of distinguishing between human users and automated agents (bots) interacting with computer systems is introduced, as well as ongoing efforts toward building Human Subtlety Proofs, persistent and unobtrusive windows into human cognition with direct application to cybersecurity. Two computer games are described, proxies to illustrate different ways in which cognitive modeling can potentially contribute to the development of HSPs and similar cybersecurity applications.

Ijaz, M., Durad, M. H., Ismail, M..  2019.  Static and Dynamic Malware Analysis Using Machine Learning. 2019 16th International Bhurban Conference on Applied Sciences and Technology (IBCAST). :687–691.

Malware detection is an indispensable factor in security of internet oriented machines. The combinations of different features are used for dynamic malware analysis. The different combinations are generated from APIs, Summary Information, DLLs and Registry Keys Changed. Cuckoo sandbox is used for dynamic malware analysis, which is customizable, and provide good accuracy. More than 2300 features are extracted from dynamic analysis of malware and 92 features are extracted statically from binary malware using PEFILE. Static features are extracted from 39000 malicious binaries and 10000 benign files. Dynamically 800 benign files and 2200 malware files are analyzed in Cuckoo Sandbox and 2300 features are extracted. The accuracy of dynamic malware analysis is 94.64% while static analysis accuracy is 99.36%. The dynamic malware analysis is not effective due to tricky and intelligent behaviours of malwares. The dynamic analysis has some limitations due to controlled network behavior and it cannot be analyzed completely due to limited access of network.

Ikany, Joris, Jazri, Husin.  2019.  A Symptomatic Framework to Predict the Risk of Insider Threats. 2019 International Conference on Advances in Big Data, Computing and Data Communication Systems (icABCD). :1–5.
The constant changing of technologies have brought to critical infrastructure organisations numerous information security threats such as insider threat. Critical infrastructure organisations have difficulties to early detect and capture the possible vital signs of insider threats due sometimes to lack of effective methodologies or frameworks. It is from this viewpoint that, this paper proposes a symptomatic insider threat risk assessments framework known as Insider Threat Framework for Namibia Critical Infrastructure Organization (ITFNACIO), aimed to predict the probable signs of insider threat based on Symptomatic Analysis (SA), and develop a prototype as a proof of concept. A case study was successfully used to validate and implement the proposed framework; hence, qualitative methodology was employed throughout the whole research process where two (2) insider threats were captured. The proposed insider threat framework can be further developed in multiple cases and a more automated system able to trigger an early warning system of possible insider threat events.
Ikhsan, Mukhammad Gufron, Ramli, Kalamullah.  2019.  Measuring the Information Security Awareness Level of Government Employees Through Phishing Assessment. 2019 34th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC). :1—4.

As an important institutional element, government information security is not only related to technical issues but also to human resources. Various types of information security instruments in an institution cannot provide maximum protection as long as employees still have a low level of information security awareness. This study aims to measure the level of information security awareness of government employees through case studies at the Directorate General of ABC (DG ABC) in Indonesia. This study used two methods, behavior approach through phishing simulation and knowledge approach through a questionnaire on a Likert scale. The simulation results were analyzed on a percentage scale and compared to the results of the questionnaire to determine the level of employees' information security awareness and determine which method was the best. Results show a significant relationship between the simulation results and the questionnaire results. Among the employees who opened the email, 69% clicked on the link that led to the camouflage page and through the questionnaire, it was found that the information security awareness level of DG ABC employees was at the level of 79.32% which was the lower limit of the GOOD category.

Ikram, Muhammad, Vallina-Rodriguez, Narseo, Seneviratne, Suranga, Kaafar, Mohamed Ali, Paxson, Vern.  2016.  An Analysis of the Privacy and Security Risks of Android VPN Permission-enabled Apps. Proceedings of the 2016 Internet Measurement Conference. :349–364.

Millions of users worldwide resort to mobile VPN clients to either circumvent censorship or to access geo-blocked content, and more generally for privacy and security purposes. In practice, however, users have little if any guarantees about the corresponding security and privacy settings, and perhaps no practical knowledge about the entities accessing their mobile traffic. In this paper we provide a first comprehensive analysis of 283 Android apps that use the Android VPN permission, which we extracted from a corpus of more than 1.4 million apps on the Google Play store. We perform a number of passive and active measurements designed to investigate a wide range of security and privacy features and to study the behavior of each VPN-based app. Our analysis includes investigation of possible malware presence, third-party library embedding, and traffic manipulation, as well as gauging user perception of the security and privacy of such apps. Our experiments reveal several instances of VPN apps that expose users to serious privacy and security vulnerabilities, such as use of insecure VPN tunneling protocols, as well as IPv6 and DNS traffic leakage. We also report on a number of apps actively performing TLS interception. Of particular concern are instances of apps that inject JavaScript programs for tracking, advertising, and for redirecting e-commerce traffic to external partners.

Ikuesan, A. R., Venter, H. S..  2017.  Digital Forensic Readiness Framework Based on Behavioral-Biometrics for User Attribution. 2017 IEEE Conference on Application, Information and Network Security (AINS). :54–59.

Whilst the fundamental composition of digital forensic readiness have been expounded by myriad literature, the integration of behavioral modalities have not been considered. Behavioral modalities such as keystroke and mouse dynamics are key components of human behavior that have been widely used in complementing security in an organization. However, these modalities present better forensic properties, thus more relevant in investigation/incident response, than its deployment in security. This study, therefore, proposes a forensic framework which encompasses a step-by-step guide on how to integrate behavioral biometrics into digital forensic readiness process. The proposed framework, behavioral biometrics-based digital forensics readiness framework (BBDFRF) comprised four phases which include data acquisition, preservation, user-authentication, and user pattern attribution phase. The proposed BBDFRF is evaluated in line with the ISO/IEC 27043 standard for proactive forensics, to address the gap on the integration of the behavioral biometrics into proactive forensics. BBDFRF thus extends the body of literature on the forensic capability of behavioral biometrics. The implementation of this framework can be used to also strengthen the security mechanism of an organization, particularly on continuous authentication.

Iliou, C., Kalpakis, G., Tsikrika, T., Vrochidis, S., Kompatsiaris, I..  2016.  Hybrid Focused Crawling for Homemade Explosives Discovery on Surface and Dark Web. 2016 11th International Conference on Availability, Reliability and Security (ARES). :229–234.
This work proposes a generic focused crawling framework for discovering resources on any given topic that reside on the Surface or the Dark Web. The proposed crawler is able to seamlessly traverse the Surface Web and several darknets present in the Dark Web (i.e. Tor, I2P and Freenet) during a single crawl by automatically adapting its crawling behavior and its classifier-guided hyperlink selection strategy based on the network type. This hybrid focused crawler is demonstrated for the discovery of Web resources containing recipes for producing homemade explosives. The evaluation experiments indicate the effectiveness of the proposed ap-proach both for the Surface and the Dark Web.
Illi, Elmehdi, Bouanani, Faissal El, da Costa, Daniel Benevides, Sofotasios, Paschalis C., Ayoub, Fouad, Mezher, Kahtan, Muhaidat, Sami.  2019.  On the Physical Layer Security of a Regenerative Relay-Based mixed RF/UOWC. 2019 International Conference on Advanced Communication Technologies and Networking (CommNet). :1–7.
This paper investigates the secrecy outage performance of a dual-hop decode-and-forward (DF) mixed radio-frequency/underwater optical wireless communication (RF/UOWC) system. We consider a one-antenna source node ( S), communicating with one legitimate destination node (D) via a multi-antenna DF relay (R) node. In this context, the relay node receives the incoming signal from S via an RF link, which is subject to Rayleigh fading, then performes selection-combining (SC) followed by decoding and then re-encoding for transmission to the destination over a UOWC link, subject to mixture Exponential-Gamma fading. Under the assumption of eavesdroppers attempting to intercept the S-R (RF side), a closed-form expression for the secrecy outage probability is derived. Our analytical results are corroborated through computer simulations, which verifies their validity.
Illi, Elmehdi, Bouanani, Faissal El, Ayoub, Fouad.  2019.  Physical Layer Security of an Amplify-and-Forward Energy Harvesting-Based Mixed RF/UOW System. 2019 International Conference on Advanced Communication Technologies and Networking (CommNet). :1–8.
This paper investigates the secrecy outage performance of an energy harvesting-based dual-hop amplify-and-forward (AF) mixed radio-frequency/underwater optical wireless communication (RF/UOWC) system. A single-antenna source node (S) is considered, communicating with one legitimate destination node (D) with the aid of a multi-antenna AF relay (R) device. In this setup, the relay node receives the incoming signal from S via an RF link, which is subject to Nakagami-m fading, then performs maximal-ratio-combining (MRC) followed by a fixed-gain amplification, before transmitting it to the destination via a UOWC link, subject to mixture Exponential-Gamma fading. Assuming the presence of a malicious eavesdropper attempting to intercept the S- R hop, a tight approximate expression for the secrecy outage probability is retrieved. The derived results provide useful insights into the influence of key system parameters on the secrecy outage performance. Our analytical results are corroborated through computer simulations, which verifies their validity.
Illing, B., Westhoven, M., Gaspers, B., Smets, N., Brüggemann, B., Mathew, T..  2020.  Evaluation of Immersive Teleoperation Systems using Standardized Tasks and Measurements. 2020 29th IEEE International Conference on Robot and Human Interactive Communication (RO-MAN). :278—285.

Despite advances regarding autonomous functionality for robots, teleoperation remains a means for performing delicate tasks in safety critical contexts like explosive ordnance disposal (EOD) and ambiguous environments. Immersive stereoscopic displays have been proposed and developed in this regard, but bring about their own specific problems, e.g., simulator sickness. This work builds upon standardized test environments to yield reproducible comparisons between different robotic platforms. The focus was placed on testing three optronic systems of differing degrees of immersion: (1) A laptop display showing multiple monoscopic camera views, (2) an off-the-shelf virtual reality headset coupled with a pantilt-based stereoscopic camera, and (3) a so-called Telepresence Unit, providing fast pan, tilt, yaw rotation, stereoscopic view, and spatial audio. Stereoscopic systems yielded significant faster task completion only for the maneuvering task. As expected, they also induced Simulator Sickness among other results. However, the amount of Simulator Sickness varied between both stereoscopic systems. Collected data suggests that a higher degree of immersion combined with careful system design can reduce the to-be-expected increase of Simulator Sickness compared to the monoscopic camera baseline while making the interface subjectively more effective for certain tasks.

Ilsenstein, Lisa, Koch, Manfred, Steinhart, Heinrich.  2020.  Definition of Attack Vectors to detect possible Cyber-Attacks on Electrical Machines. PCIM Europe digital days 2020; International Exhibition and Conference for Power Electronics, Intelligent Motion, Renewable Energy and Energy Management. :1—7.
System safety and cyber security have a great effect on the availability of devices that are interconnected. With the rising interconnection of critical infrastructures new risks occur, which have to be detected and warded. Therefore, attack vectors are defined to determine deviations to the nominal values of a cyber-physical system in this paper. Through an elaborated cyber security concept, the tasks of a simple motor protecting switch and additional tasks to detect cyber-attacks can be implemented. The simulative result of an exemplary overvoltage shows the impact on the RMS and phase voltages of a monitored drive.
Iltaf, Naima, Ghafoor, Abdul, Zia, Usman, Hussain, Mukhtar.  2014.  An Effective Model for Indirect Trust Computation in Pervasive Computing Environment. Wirel. Pers. Commun.. 75:1689–1713.

The performance of indirect trust computation models (based on recommendations) can be easily compromised due to the subjective and social-based prejudice of the provided recommendations. Eradicating the influence of such recommendation remains an important and challenging issue in indirect trust computation models. An effective model for indirect trust computation is proposed which is capable of identifying dishonest recommendations. Dishonest recommendations are identified by using deviation based detecting technique. The concept of measuring the credibility of recommendation (rather than credibility of recommender) using fuzzy inference engine is also proposed to determine the influence of each honest recommendation. The proposed model has been compared with other existing evolutionary recommendation models in this field, and it is shown that the model is more accurate in measuring the trustworthiness of unknown entity.

Im, Jong-Hyuk, Choi, JinChun, Nyang, DaeHun, Lee, Mun-Kyu.  2016.  Privacy-Preserving Palm Print Authentication Using Homomorphic Encryption. :878–881.

Biometric verification systems have security issues regarding the storage of biometric data in that a user's biometric features cannot be changed into other ones even when a system is compromised. To address this issue, it may be safe to store the biometrics data on a reliable remote server instead of storing them in a local device. However, this approach may raise a privacy issue. In this paper, we propose a biometric verification system where the biometric data are stored in a remote server in an encrypted form and the similarity of the user input to the registered biometric data is computed in an encrypted domain using a homomorphic encryption. We evaluated the performance of the proposed system through an implementation on an Android-based smartphone and an i7-based server.

Imai, H., Hanaoka, G., Shikata, J., Otsuka, A., Nascimento, A. C..  2002.  Cryptography with information theoretic security. Proceedings of the IEEE Information Theory Workshop. :73–.
Summary form only given. We discuss information-theoretic methods to prove the security of cryptosystems. We study what is called, unconditionally secure (or information-theoretically secure) cryptographic schemes in search for a system that can provide long-term security and that does not impose limits on the adversary's computational power.
Imajo, Tomoaki, Sumiya, Kazutoshi, Ushiama, Taketoshi.  2016.  An SNS Based on Implicit Beneficial Social Relations in A Regional Community. Proceedings of the 10th International Conference on Ubiquitous Information Management and Communication. :47:1–47:7.

In this paper, we propose a novel Social Networking Service (SNS) for a regional community. The purpose of the SNS is to support and encourage people by making them aware beneficial social relations in the real world. The conventional SNSs can hardly deal with beneficial social relations, because they are implicit and dynamic. The proposed SNS is designed to provide positive information for two types of people: people who does community voluntary works, such as cleaning, as contributors, and people who receives benefit from them as beneficiary. This paper introduces the basic scheme based on the SNS for beneficial social relations, and evaluates the effectiveness of our scheme based on the result of the experimental studies. The experimental result shows the users of our SNS tend to consider the information about the voluntary works valuable if they have been performed in their living area, and it suggests that our proposed SNS system would work well in a regional community.

Imani, Mohsen, Gupta, Saransh, Rosing, Tajana.  2017.  Ultra-Efficient Processing In-Memory for Data Intensive Applications. Proceedings of the 54th Annual Design Automation Conference 2017. :6:1–6:6.

Recent years have witnessed a rapid growth in the domain of Internet of Things (IoT). This network of billions of devices generates and exchanges huge amount of data. The limited cache capacity and memory bandwidth make transferring and processing such data on traditional CPUs and GPUs highly inefficient, both in terms of energy consumption and delay. However, many IoT applications are statistical at heart and can accept a part of inaccuracy in their computation. This enables the designers to reduce complexity of processing by approximating the results for a desired accuracy. In this paper, we propose an ultra-efficient approximate processing in-memory architecture, called APIM, which exploits the analog characteristics of non-volatile memories to support addition and multiplication inside the crossbar memory, while storing the data. The proposed design eliminates the overhead involved in transferring data to processor by virtually bringing the processor inside memory. APIM dynamically configures the precision of computation for each application in order to tune the level of accuracy during runtime. Our experimental evaluation running six general OpenCL applications shows that the proposed design achieves up to 20x performance improvement and provides 480x improvement in energy-delay product, ensuring acceptable quality of service. In exact mode, it achieves 28x energy savings and 4.8x speed up compared to the state-of-the-art GPU cores.

Imeri, Adnan, Feltus, Christophe, Khadraoui, Djamel, Agoulmine, Nazim, Nicolas, Damien.  2018.  Solving the Trust Issues in the Process of Transportation of Dangerous Goods by Using Blockchain Technology. Proceedings of the 11th International Conference on Security of Information and Networks. :25:1–25:2.
The issues of trust in the area of supply chain management are an immense concern among the stakeholders cooperating in the supply chain. For a sustainable process of transportation, efficient information sharing is considered crucial. The models that serve as a base for the current operations have several drawbacks in terms of data security and trust among stakeholders, who share information as part of their cooperation. Information is shared in a paper-based or semi-digitalized way due to the lack of trust or risk of competitive disadvantages in the current systems. This paper aims to analyze the trust issues in supply chain management and propose new ways of improving trust by considering these issues at the design level.