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Towards Insider Threat Detection Using Psychophysiological Signals

Published: 16 October 2015 Publication History

Abstract

Insider threat is one of the greatest concerns for the information security system that could cause greater financial losses and damages than any other attacks. Recently many studies have been proposed to monitor and detect the insider attacks. However, implementing an effective detection system is a very challenging task. In this paper, we investigate the usability of human bio-signals to detect the malicious insiders in real time. We present an insider threat monitoring and detection framework based on the electroencephalography (EEG) signals to distinguish between normal and malicious activities. We describe the framework and its components. We then evaluate the proposed framework using several real world scenarios. The results show that the detection accuracy of the malicious activities is up to 90% and demonstrate that electroencephalography (EEG) can reveal valuable knowledge about the user behaviors and could be a very effective solution for detecting insider threats.

References

[1]
Bertino, E., & Ghinita, G. (2011). Towards mechanisms for detection and prevention of data exfiltration by insiders: keynote talk paper. In Proceedings of the 6th ACM Symposium on Information, Computer and Communications Security (pp. 10--19). ACM.
[2]
Glasser, J., & Lindauer, B. (2013). Bridging the gap: A pragmatic approach to generating insider threat data. In Security and Privacy Workshops (SPW), 2013 IEEE (pp. 98--104). IEEE
[3]
Cybercrime: Protecting against the growing threat - Events and Trends, vol. 256, 2012.
[4]
AlgoSec. The State of Network Security 2013: Attitudes and Opinions. AlgoSec, Inc., 2013. http://www.algosec.com/resources/files/Specials/Surveyfiles/StateofNetworkSecurity2013_FinalReport.pdf
[5]
New Market Research - SolarWinds Survey Investigates Insider Threats to Federal Cybersecurity." Federal and Government Discussions. N.p., n.d. Web. 25 Mar. 2015. https://thwack.solarwinds.com
[6]
Salem, M. B., Hershkop, S., & Stolfo, S. J. (2008). A survey of insider attack detection research. In Insider Attack and Cyber Security (pp. 69--90). Springer US.
[7]
Hunker, J., & Probst, C. W. (2011). Insiders and insider threats an overview of definitions and mitigation techniques. Journal of Wireless Mobile Network,Ubiquitous Computing, and Dependable Applications, 2(1), 4--27.
[8]
Greitzer, F. L., Kangas, L. J., Noonan, C. F., Dalton, A. C., & Hohimer, R. E. (2012, January). Identifying at-risk employees: Modeling psychosocial precursors of potential insider threats. In System Science (HICSS), 2012 45th Hawaii International Conference on (pp. 2392--2401). IEEE.
[9]
"Emotiv Systems," https://emotiv.com/ {last accessed: July 20, 2015}.
[10]
Jolliffe, I. (2002). Principal component analysis. John Wiley & Sons, Ltd.
[11]
Vapnik, V. (2013). The nature of statistical learning theory. Springer Science & Business Media.

Cited By

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  • (2024)Tracking user trust and mental states during cyber-attacks: A survey of existing methods and future research directions on AI-enabled decision-making for the Royal Canadian Navy2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)10.1109/ICHMS59971.2024.10555658(1-4)Online publication date: 15-May-2024
  • (2024)VISTAInformation and Management10.1016/j.im.2023.10387761:1Online publication date: 14-Mar-2024
  • (2024)Machine learning approaches to detect, prevent and mitigate malicious insider threats: State-of-the-art reviewMultimedia Tools and Applications10.1007/s11042-024-20273-0Online publication date: 4-Oct-2024
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Published In

cover image ACM Conferences
MIST '15: Proceedings of the 7th ACM CCS International Workshop on Managing Insider Security Threats
October 2015
90 pages
ISBN:9781450338240
DOI:10.1145/2808783
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Publication History

Published: 16 October 2015

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Author Tags

  1. brain computer interface
  2. electroencephalograph
  3. insider threat detection
  4. physiological indicators

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CCS'15
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MIST '15 Paper Acceptance Rate 6 of 14 submissions, 43%;
Overall Acceptance Rate 21 of 54 submissions, 39%

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CCS '25

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Cited By

View all
  • (2024)Tracking user trust and mental states during cyber-attacks: A survey of existing methods and future research directions on AI-enabled decision-making for the Royal Canadian Navy2024 IEEE 4th International Conference on Human-Machine Systems (ICHMS)10.1109/ICHMS59971.2024.10555658(1-4)Online publication date: 15-May-2024
  • (2024)VISTAInformation and Management10.1016/j.im.2023.10387761:1Online publication date: 14-Mar-2024
  • (2024)Machine learning approaches to detect, prevent and mitigate malicious insider threats: State-of-the-art reviewMultimedia Tools and Applications10.1007/s11042-024-20273-0Online publication date: 4-Oct-2024
  • (2024)An Insider Threat Resilient Framework Based on Honey Traps in a Function-Based Access Control EnvironmentAdvanced Network Technologies and Intelligent Computing10.1007/978-3-031-64064-3_1(3-16)Online publication date: 8-Aug-2024
  • (2023)Human-centered Behavioral and Physiological SecurityProceedings of the 2023 New Security Paradigms Workshop10.1145/3633500.3633504(48-61)Online publication date: 18-Sep-2023
  • (2023)Prediction and Detection of Insider Threat Detection using Emails: A Comparision2023 Second International Conference on Electrical, Electronics, Information and Communication Technologies (ICEEICT)10.1109/ICEEICT56924.2023.10157297(01-06)Online publication date: 5-Apr-2023
  • (2023)Insider Intrusion Detection Techniques: A State-of-the-Art ReviewJournal of Computer Information Systems10.1080/08874417.2023.217533764:1(106-123)Online publication date: 14-Feb-2023
  • (2023)Insider Threat Detection on an Imbalanced Dataset Using Balancing MethodsIntelligent Computing10.1007/978-3-031-37717-4_80(1216-1226)Online publication date: 1-Sep-2023
  • (2023)Pivoting Human Resource Policy Around Emerging Invasive and Non-invasive NeurotechnologyCybersecurity for Smart Cities10.1007/978-3-031-24946-4_3(31-46)Online publication date: 30-Mar-2023
  • (2022)Deep Learning-Based Approach for Emotion Recognition Using Electroencephalography (EEG) Signals Using Bi-Directional Long Short-Term Memory (Bi-LSTM)Sensors10.3390/s2208297622:8(2976)Online publication date: 13-Apr-2022
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