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Shang, Jiacheng, Wu, Jie.  2019.  A Usable Authentication System Using Wrist-Worn Photoplethysmography Sensors on Smartwatches. 2019 IEEE Conference on Communications and Network Security (CNS). :1–9.
Smartwatches are expected to become the world's best-selling electronic product after smartphones. Various smart-watches have been released to the private consumer market, but the data on smartwatches is not well protected. In this paper, we show for the first time that photoplethysmography (PPG)signals influenced by hand gestures can be used to authenticate users on smartwatches. The insight is that muscle and tendon movements caused by hand gestures compress the arterial geometry with different degrees, which has a significant impact on the blood flow. Based on this insight, novel approaches are proposed to detect the starting point and ending point of the hand gesture from raw PPG signals and determine if these PPG signals are from a normal user or an attacker. Different from existing solutions, our approach leverages the PPG sensors that are available on most smartwatches and does not need to collect training data from attackers. Also, our system can be used in more general scenarios wherever users can perform hand gestures and is robust against shoulder surfing attacks. We conduct various experiments to evaluate the performance of our system and show that our system achieves an average authentication accuracy of 96.31 % and an average true rejection rate of at least 91.64% against two types of attacks.
Arzhakov, A. V..  2018.  Usage of game theory in the internet wide scan. 2018 IEEE Conference of Russian Young Researchers in Electrical and Electronic Engineering (EIConRus). :5–8.

This article examines Usage of Game Theory in The Internet Wide Scan. There is compiled model of “Network Scanning” game. There is described process of players interaction in the coalition antagonistic and network games. The concept of target system's cost is suggested. Moreover, there is suggested its application in network scanning, particularly, when detecting honeypot/honeynet systems.

Nathan Malkin, Primal Wijesekera, Serge Egelman, David Wagner.  2018.  Use Case: Passively Listening Personal Assistants. Symposium on Applications of Contextual Integrity. :26-27.
Wehbe, Taimour, Mooney, Vincent J., Keezer, David, Inan, Omer T., Javaid, Abdul Qadir.  2017.  Use of Analog Signatures for Hardware Trojan Detection. Proceedings of the 14th FPGAworld Conference. :15–22.
Malicious Hardware Trojans can corrupt data which if undetected may cause serious harm. We propose a technique where characteristics of the data itself are used to detect Hardware Trojan (HT) attacks. In particular, we use a two-chip approach where we generate a data "signature" in analog and test for the signature in a partially reconfigurable digital microchip where the HT may attack. This paper presents an overall signature-based HT detection architecture and case study for cardiovascular signals used in medical device technology. Our results show that with minimal performance and area overhead, the proposed architecture is able to detect HT attacks on primary data inputs as well as on multiple modules of the design.
Hajdu, Gergo, Minoso, Yaclaudes, Lopez, Rafael, Acosta, Miguel, Elleithy, Abdelrahman.  2019.  Use of Artificial Neural Networks to Identify Fake Profiles. 2019 IEEE Long Island Systems, Applications and Technology Conference (LISAT). :1–4.
In this paper, we use machine learning, namely an artificial neural network to determine what are the chances that Facebook friend request is authentic or not. We also outline the classes and libraries involved. Furthermore, we discuss the sigmoid function and how the weights are determined and used. Finally, we consider the parameters of the social network page which are utmost important in the provided solution.
Parmar, Manisha, Domingo, Alberto.  2019.  On the Use of Cyber Threat Intelligence (CTI) in Support of Developing the Commander's Understanding of the Adversary. MILCOM 2019 - 2019 IEEE Military Communications Conference (MILCOM). :1–6.
Cyber Threat Intelligence (CTI) is a rapidly developing field which has evolved in direct response to exponential growth in cyber related crimes and attacks. CTI supports Communication and Information System (CIS)Security in order to bolster defenses and aids in the development of threat models that inform an organization's decision making process. In a military organization like NATO, CTI additionally supports Cyberspace Operations by providing the Commander with essential intelligence about the adversary, their capabilities and objectives while operating in and through cyberspace. There have been many contributions to the CTI field; a noteworthy contribution is the ATT&CK® framework by the Mitre Corporation. ATT&CK® contains a comprehensive list of adversary tactics and techniques linked to custom or publicly known Advanced Persistent Threats (APT) which aids an analyst in the characterization of Indicators of Compromise (IOCs). The ATT&CK® framework also demonstrates possibility of supporting an organization with linking observed tactics and techniques to specific APT behavior, which may assist with adversary characterization and identification, necessary steps towards attribution. The NATO Allied Command Transformation (ACT) and the NATO Communication and Information Agency (NCI Agency) have been experimenting with the use of deception techniques (including decoys) to increase the collection of adversary related data. The collected data is mapped to the tactics and techniques described in the ATT&CK® framework, in order to derive evidence to support adversary characterization; this intelligence is pivotal for the Commander to support mission planning and determine the best possible multi-domain courses of action. This paper describes the approach, methodology, outcomes and next steps for the conducted experiments.
Siser, Anton, Maris, Ladislav, Rehák, David, Pellowski, Witalis.  2018.  The Use of Expert Judgement as the Method to Obtain Delay Time Values of Passive Barriers in the Context of the Physical Protection System. 2018 International Carnahan Conference on Security Technology (ICCST). :1–5.

Due to its costly and time-consuming nature and a wide range of passive barrier elements and tools for their breaching, testing the delay time of passive barriers is only possible as an experimental tool to verify expert judgements of said delay times. The article focuses on the possibility of creating and utilizing a new method of acquiring values of delay time for various passive barrier elements using expert judgements which could add to the creation of charts where interactions between the used elements of mechanical barriers and the potential tools for their bypassing would be assigned a temporal value. The article consists of basic description of methods of expert judgements previously applied for making prognoses of socio-economic development and in other societal areas, which are called soft system. In terms of the problem of delay time, this method needed to be modified in such a way that the prospective output would be expressible by a specific quantitative value. To achieve this goal, each stage of the expert judgements was adjusted to the use of suitable scientific methods to select appropriate experts and then to achieve and process the expert data. High emphasis was placed on evaluation of quality and reliability of the expert judgements, which takes into account the specifics of expert selection such as their low numbers, specialization and practical experience.

Hou, Size, Huang, Xin.  2019.  Use of Machine Learning in Detecting Network Security of Edge Computing System. 2019 IEEE 4th International Conference on Big Data Analytics (ICBDA). :252–256.

This study has built a simulation of a smart home system by the Alibaba ECS. The architecture of hardware was based on edge computing technology. The whole method would design a clear classifier to find the boundary between regular and mutation codes. It could be applied in the detection of the mutation code of network. The project has used the dataset vector to divide them into positive and negative type, and the final result has shown the RBF-function SVM method perform best in this mission. This research has got a good network security detection in the IoT systems and increased the applications of machine learning.

Weining Yang, Aiping Xiong, Jing Chen, Robert W. Proctor, Ninghui Li.  2017.  Use of Phishing Training to Improve Security Warning Compliance: Evidence from a Field Experiment.

The current approach to protect users from phishing attacks is to display a warning when the webpage is considered suspicious. We hypothesize that users are capable of making correct informed decisions when the warning also conveys the reasons why it is displayed. We chose to use traffic rankings of domains, which can be easily described to users, as a warning trigger and evaluated the effect of the phishing warning message and phishing training. The evaluation was conducted in a field experiment. We found that knowledge gained from the training enhances the effectiveness of phishing warnings, as the number of participants being phished was reduced. However, the knowledge by itself was not sufficient to provide phishing protection. We suggest that integrating training in the warning interface, involving traffic ranking in phishing detection, and explaining why warnings are generated will improve current phishing defense.

Aiping Xiong, Robert W. Proctor, Ninghui Li, Weining Yang.  2016.  Use of Warnings for Instructing Users How to Detect Phishing Webpages. 46th Annual Meeting of the Society for Computers in Psychology.

The ineffectiveness of phishing warnings has been attributed to users' poor comprehension of the warning. However, the effectiveness of a phishing warning is typically evaluated at the time when users interact with a suspected phishing webpage, which we call the effect with phishing warning. Nevertheless, users' improved phishing detection when the warning is absent—or the effect of the warning—is the ultimate goal to prevent users from falling for phishing scams. We conducted an online study to evaluate the effect with and of several phishing warning variations, varying the point at which the warning was presented and whether procedural knowledge instruction was included in the warning interface. The current Chrome phishing warning was also included as a control. 360 Amazon Mechanical-Turk workers made submission; 500¬ word maximum for symposia) decisions about 10 login webpages (8 authentic, 2 fraudulent) with the aid of warning (first phase). After a short distracting task, the workers made the same decisions about 10 different login webpages (8 authentic, 2 fraudulent) without warning. In phase one, the compliance rates with two proposed warning interfaces (98% and 94%) were similar to those of the Chrome warning (98%), regardless of when the warning was presented. In phase two (without warning), performance was better for the condition in which warning with procedural knowledge instruction was presented before the phishing webpage in phase one, suggesting a better of effect than for the other conditions. With the procedural knowledge of how to determine a webpage’s legitimacy, users identified phishing webpages more accurately even without the warning being presented.

Aiping Xiong, Robert W. Proctor, Ninghui Li, Weining Yang.  2016.  Use of Warnings for Instructing Users How to Detect Phishing Webpages. 46th Annual Meeting of the Society for Computers in Psychology.

The ineffectiveness of phishing warnings has been attributed to users' poor comprehension of the warning. However, the effectiveness of a phishing warning is typically evaluated at the time when users interact with a suspected phishing webpage, which we call the effect with phishing warning. Nevertheless, users' improved phishing detection when the warning is absent—or the effect of the warning—is the ultimate goal to prevent users from falling for phishing scams. We conducted an online study to evaluate the effect with and of several phishing warning variations, varying the point at which the warning was presented and whether procedural knowledge instruction was included in the warning interface. The current Chrome phishing warning was also included as a control. 360 Amazon Mechanical-Turk workers made submission; 500¬ word maximum for symposia) decisions about 10 login webpages (8 authentic, 2 fraudulent) with the aid of warning (first phase). After a short distracting task, the workers made the same decisions about 10 different login webpages (8 authentic, 2 fraudulent) without warning. In phase one, the compliance rates with two proposed warning interfaces (98% and 94%) were similar to those of the Chrome warning (98%), regardless of when the warning was presented. In phase two (without warning), performance was better for the condition in which warning with procedural knowledge instruction was presented before the phishing webpage in phase one, suggesting a better of effect than for the other conditions. With the procedural knowledge of how to determine a webpage’s legitimacy, users identified phishing webpages more accurately even without the warning being presented.

Datta, Anupam, Fredrikson, Matthew, Ko, Gihyuk, Mardziel, Piotr, Sen, Shayak.  2017.  Use Privacy in Data-Driven Systems: Theory and Experiments with Machine Learnt Programs. Proceedings of the 2017 ACM SIGSAC Conference on Computer and Communications Security. :1193–1210.

This paper presents an approach to formalizing and enforcing a class of use privacy properties in data-driven systems. In contrast to prior work, we focus on use restrictions on proxies (i.e. strong predictors) of protected information types. Our definition relates proxy use to intermediate computations that occur in a program, and identify two essential properties that characterize this behavior: 1) its result is strongly associated with the protected information type in question, and 2) it is likely to causally affect the final output of the program. For a specific instantiation of this definition, we present a program analysis technique that detects instances of proxy use in a model, and provides a witness that identifies which parts of the corresponding program exhibit the behavior. Recognizing that not all instances of proxy use of a protected information type are inappropriate, we make use of a normative judgment oracle that makes this inappropriateness determination for a given witness. Our repair algorithm uses the witness of an inappropriate proxy use to transform the model into one that provably does not exhibit proxy use, while avoiding changes that unduly affect classification accuracy. Using a corpus of social datasets, our evaluation shows that these algorithms are able to detect proxy use instances that would be difficult to find using existing techniques, and subsequently remove them while maintaining acceptable classification performance.

Ahmad, Kashif, Conci, Nicola, Boato, Giulia, De Natale, Francesco G. B..  2016.  USED: A Large-scale Social Event Detection Dataset. Proceedings of the 7th International Conference on Multimedia Systems. :50:1–50:6.

Event discovery from single pictures is a challenging problem that has raised significant interest in the last decade. During this time, a number of interesting solutions have been proposed to tackle event discovery in still images. However, a large scale benchmarking image dataset for the evaluation and comparison of event discovery algorithms from single images is still lagging behind. To this aim, in this paper we provide a large-scale properly annotated and balanced dataset of 490,000 images, covering every aspect of 14 different types of social events, selected among the most shared ones in the social network. Such a large scale collection of event-related images is intended to become a powerful support tool for the research community in multimedia analysis by providing a common benchmark for training, testing, validation and comparison of existing and novel algorithms. In this paper, we provide a detailed description of how the dataset is collected, organized and how it can be beneficial for the researchers in the multimedia analysis domain. Moreover, a deep learning based approach is introduced into event discovery from single images as one of the possible applications of this dataset with a belief that deep learning can prove to be a breakthrough also in this research area. By providing this dataset, we hope to gather research community in the multimedia and signal processing domains to advance this application.

Mohlala, M., Ikuesan, A. R., Venter, H. S..  2017.  User Attribution Based on Keystroke Dynamics in Digital Forensic Readiness Process. 2017 IEEE Conference on Application, Information and Network Security (AINS). :124–129.

As the development of technology increases, the security risk also increases. This has affected most organizations, irrespective of size, as they depend on the increasingly pervasive technology to perform their daily tasks. However, the dependency on technology has introduced diverse security vulnerabilities in organizations which requires a reliable preparedness for probable forensic investigation of the unauthorized incident. Keystroke dynamics is one of the cost-effective methods for collecting potential digital evidence. This paper presents a keystroke pattern analysis technique suitable for the collection of complementary potential digital evidence for forensic readiness. The proposition introduced a technique that relies on the extraction of reliable behavioral signature from user activity. Experimental validation of the proposition demonstrates the effectiveness of proposition using a multi-scheme classifier. The overall goal is to have forensically sound and admissible keystroke evidence that could be presented during the forensic investigation to minimize the costs and time of the investigation.

Najafabadi, M. M., Khoshgoftaar, T. M., Calvert, C., Kemp, C..  2017.  User Behavior Anomaly Detection for Application Layer DDoS Attacks. 2017 IEEE International Conference on Information Reuse and Integration (IRI). :154–161.

Distributed Denial of Service (DDoS) attacks are a popular and inexpensive form of cyber attacks. Application layer DDoS attacks utilize legitimate application layer requests to overwhelm a web server. These attacks are a major threat to Internet applications and web services. The main goal of these attacks is to make the services unavailable to legitimate users by overwhelming the resources on a web server. They look valid in connection and protocol characteristics, which makes them difficult to detect. In this paper, we propose a detection method for the application layer DDoS attacks, which is based on user behavior anomaly detection. We extract instances of user behaviors requesting resources from HTTP web server logs. We apply the Principle Component Analysis (PCA) subspace anomaly detection method for the detection of anomalous behavior instances. Web server logs from a web server hosting a student resource portal were collected as experimental data. We also generated nine different HTTP DDoS attacks through penetration testing. Our performance results on the collected data show that using PCAsubspace anomaly detection on user behavior data can detect application layer DDoS attacks, even if they are trying to mimic a normal user's behavior at some level.

Malek, Z. S., Trivedi, B., Shah, A..  2020.  User behavior Pattern -Signature based Intrusion Detection. 2020 Fourth World Conference on Smart Trends in Systems, Security and Sustainability (WorldS4). :549—552.

Technology advancement also increases the risk of a computer's security. As we can have various mechanisms to ensure safety but still there have flaws. The main concerned area is user authentication. For authentication, various biometric applications are used but once authentication is done in the begging there was no guarantee that the computer system is used by the authentic user or not. The intrusion detection system (IDS) is a particular procedure that is used to identify intruders by analyzing user behavior in the system after the user logged in. Host-based IDS monitors user behavior in the computer and identify user suspicious behavior as an intrusion or normal behavior. This paper discusses how an expert system detects intrusions using a set of rules as a pattern recognized engine. We propose a PIDE (Pattern Based Intrusion Detection) model, which is verified previously implemented SBID (Statistical Based Intrusion Detection) model. Experiment results indicate that integration of SBID and PBID approach provides an extensive system to detect intrusion.

Singh, Malvika, Mehtre, B.M., Sangeetha, S..  2019.  User Behavior Profiling Using Ensemble Approach for Insider Threat Detection. 2019 IEEE 5th International Conference on Identity, Security, and Behavior Analysis (ISBA). :1–8.

The greatest threat towards securing the organization and its assets are no longer the attackers attacking beyond the network walls of the organization but the insiders present within the organization with malicious intent. Existing approaches helps to monitor, detect and prevent any malicious activities within an organization's network while ignoring the human behavior impact on security. In this paper we have focused on user behavior profiling approach to monitor and analyze user behavior action sequence to detect insider threats. We present an ensemble hybrid machine learning approach using Multi State Long Short Term Memory (MSLSTM) and Convolution Neural Networks (CNN) based time series anomaly detection to detect the additive outliers in the behavior patterns based on their spatial-temporal behavior features. We find that using Multistate LSTM is better than basic single state LSTM. The proposed method with Multistate LSTM can successfully detect the insider threats providing the AUC of 0.9042 on train data and AUC of 0.9047 on test data when trained with publically available dataset for insider threats.

Alruwaythi, M., Kambampaty, K., Nygard, K..  2018.  User Behavior Trust Modeling in Cloud Security. 2018 International Conference on Computational Science and Computational Intelligence (CSCI). :1336–1339.
Evaluating user behavior in cloud computing infrastructure is important for both Cloud Users and Cloud Service Providers. The service providers must ensure the safety of users who access the cloud. User behavior can be modeled and employed to help assess trust and play a role in ensuring authenticity and safety of the user. In this paper, we propose a User Behavior Trust Model based on Fuzzy Logic (UBTMFL). In this model, we develop user history patterns and compare them current user behavior. The outcome of the comparison is sent to a trust computation center to calculate a user trust value. This model considers three types of trust: direct, history and comprehensive. Simulation results are included.
Cepheli, O., Buyukcorak, S., Kurt, G.K..  2014.  User behaviour modelling based DDoS attack detection. Signal Processing and Communications Applications Conference (SIU), 2014 22nd. :2186-2189.

Distributed Denial of Service (DDoS) attacks are one of the most important threads in network systems. Due to the distributed nature, DDoS attacks are very hard to detect, while they also have the destructive potential of classical denial of service attacks. In this study, a novel 2-step system is proposed for the detection of DDoS attacks. In the first step an anomaly detection is performed on the destination IP traffic. If an anomaly is detected on the network, the system proceeds into the second step where a decision on every user is made due to the behaviour models. Hence, it is possible to detect attacks in the network that diverges from users' behavior model.

van Thuan, D., Butkus, P., van Thanh, D..  2014.  A User Centric Identity Management for Internet of Things. IT Convergence and Security (ICITCS), 2014 International Conference on. :1-4.

In the future Internet of Things, it is envisioned that things are collaborating to serve people. Unfortunately, this vision could not be realised without relations between things and people. To solve the problem this paper proposes a user centric identity management system that incorporates user identity, device identity and the relations between them. The proposed IDM system is user centric and allows device authentication and authorization based on the user identity. A typical compelling use case of the proposed solution is also given.

Wang, W., Xuan, S., Yang, W., Chen, Y..  2019.  User Credibility Assessment Based on Trust Propagation in Microblog. 2019 Computing, Communications and IoT Applications (ComComAp). :270—275.

Nowadays, Microblog has become an important online social networking platform, and a large number of users share information through Microblog. Many malicious users have released various false news driven by various interests, which seriously affects the availability of Microblog platform. Therefore, the evaluation of Microblog user credibility has become an important research issue. This paper proposes a microblog user credibility evaluation algorithm based on trust propagation. In view of the high consumption and low precision caused by malicious users' attacking algorithms and manual selection of seed sets by establishing false social relationships, this paper proposes two optimization strategies: pruning algorithm based on social activity and similarity and based on The seed node selection algorithm of clustering. The pruning algorithm can trim off the attack edges established by malicious users and normal users. The seed node selection algorithm can efficiently select the highly available seed node set, and finally use the user social relationship graph to perform the two-way propagation trust scoring, so that the low trusted user has a lower trusted score and thus identifies the malicious user. The related experiments verify the effectiveness of the trustworthiness-based user credibility evaluation algorithm in the evaluation of Microblog user credibility.

Ben-Yaakov, Y., Meyer, J., Wang, X., An, B..  2020.  User detection of threats with different security measures. 2020 IEEE International Conference on Human-Machine Systems (ICHMS). :1—6.

Cyber attacks and the associated costs made cybersecurity a vital part of any system. User behavior and decisions are still a major part in the coping with these risks. We developed a model of optimal investment and human decisions with security measures, given that the effectiveness of each measure depends partly on the performance of the others. In an online experiment, participants classified events as malicious or non-malicious, based on the value of an observed variable. Prior to making the decisions, they had invested in three security measures - a firewall, an IDS or insurance. In three experimental conditions, maximal investment in only one of the measures was optimal, while in a fourth condition, participants should not have invested in any of the measures. A previous paper presents the analysis of the investment decisions. This paper reports users' classifications of events when interacting with these systems. The use of security mechanisms helped participants gain higher scores. Participants benefited in particular from purchasing IDS and/or Cyber Insurance. Participants also showed higher sensitivity and compliance with the alerting system when they could benefit from investing in the IDS. Participants, however, did not adjust their behavior optimally to the security settings they had chosen. The results demonstrate the complex nature of risk-related behaviors and the need to consider human abilities and biases when designing cyber security systems.

Gambino, Andrew, Kim, Jinyoung, Sundar, S. Shyam, Ge, Jun, Rosson, Mary Beth.  2016.  User Disbelief in Privacy Paradox: Heuristics That Determine Disclosure. Proceedings of the 2016 CHI Conference Extended Abstracts on Human Factors in Computing Systems. :2837–2843.
We conducted a series of in-depth focus groups wherein users provided rationales for their own online privacy behaviors. Our data suggest that individuals often take action with little thought or evaluation, even showing surprise when confronted with their own behaviors. Our analysis yielded a battery of cognitive heuristics, i.e., mental shortcuts / rules of thumb, that users seem to employ when they disclose or withhold information at the spur of the moment. A total of 4 positive heuristics (promoting disclosure) and 4 negative heuristics (inhibiting disclosure) were discovered. An understanding of these heuristics can be valuable for designing interfaces that promote secure and trustworthy computing.
Tao Xie, University of Illinois at Urbana-Champaign.  2016.  User Expectations in Mobile App Security.

Maintaining the security and privacy hygiene of mobile apps is a critical challenge. Unfortunately, no program analysis algorithm can determine that an application is “secure” or “malware-free.” For example, if an application records audio during a phone call, it may be malware. However, the user may want to use such an application to record phone calls for archival and benign purposes. A key challenge for automated program analysis tools is determining whether or not that behavior is actually desired by the user (i.e., user expectation). This talk presents recent research progress in exploring user expectations in mobile app security.

Presented at the ITI Joint Trust and Security/Science of Security Seminar, January 26, 2016.

Khosmood, F., Nico, P.L., Woolery, J..  2014.  User identification through command history analysis. Computational Intelligence in Cyber Security (CICS), 2014 IEEE Symposium on. :1-7.

As any veteran of the editor wars can attest, Unix users can be fiercely and irrationally attached to the commands they use and the manner in which they use them. In this work, we investigate the problem of identifying users out of a large set of candidates (25-97) through their command-line histories. Using standard algorithms and feature sets inspired by natural language authorship attribution literature, we demonstrate conclusively that individual users can be identified with a high degree of accuracy through their command-line behavior. Further, we report on the best performing feature combinations, from the many thousands that are possible, both in terms of accuracy and generality. We validate our work by experimenting on three user corpora comprising data gathered over three decades at three distinct locations. These are the Greenberg user profile corpus (168 users), Schonlau masquerading corpus (50 users) and Cal Poly command history corpus (97 users). The first two are well known corpora published in 1991 and 2001 respectively. The last is developed by the authors in a year-long study in 2014 and represents the most recent corpus of its kind. For a 50 user configuration, we find feature sets that can successfully identify users with over 90% accuracy on the Cal Poly, Greenberg and one variant of the Schonlau corpus, and over 87% on the other Schonlau variant.