Emotion-Inspired Intrusion Detection: Affective Computing for Adaptive Cyber Defense
Keywords:
Affective Computing, Intrusion Detection Systems, Cyber Defense, Emotional Intelligence, Adaptive Security, Human-Computer InteractionAbstract
Because cyber threats keep evolving, new solutions must be intelligent, aware of their surroundings, and able to adapt. Traditional IDS solutions are suitable for recognizing known cyber attacks, but they find dealing with new, unidentified threats and unusual behavior patterns challenging. The use of affective computing, which is combined with intrusion detection, is suggested for the first time in this paper. The Emotion-Inspired Intrusion Detection System (EIDS) model applies algorithms based on emotions to answer the threat in ways people might respond to different severity levels of a cyber attack.
Following neurological and psychological models of emotion, EIDS studies the system’s behavior and traffic using classifiers and feedback loops. The system changes its protection level based on how these emotions are evaluated. This model identifies and learns harmful activity patterns through decision trees, support vector machines, and self-organizing maps.
The experiments used important benchmark datasets NSL-KDD and CICIDS2017 for evaluation. Contrary to standard IDS approaches, EIDS had higher detection accuracy, fewer false positives, and a better ability to adjust to fast attack changes. System statistics, feature use, and threat classification were represented using bar charts, pie charts, and tables.
With this research, cybersecurity becomes more flexible by making it possible for systems to sense and respond to feelings. The results demonstrate how affective computing can help improve a cyber defender’s ability to detect threats faster and make more accurate decisions on the spot. Based on the findings, it seems likely that future emotional machine learning could help intelligently respond to cyber intrusions.
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References
Bickmore, T., & Picard, R. (2005). Establishing and maintaining long-term human-computer relationships. ACM Transactions on Computer-Human Interaction (TOCHI), 12(2), 293–327. https://doi.org/10.1145/1067860.1067867
Calvo, R. A., &D’Mello, S. K. (2010). Affect detection: An interdisciplinary review of models, methods, and their applications. IEEE Transactions on Affective Computing, 1(1), 18–37. https://doi.org/10.1109/T-AFFC.2010.1
Crawford, K., &Calo, R. (2016). There is a blind spot in AI research. Nature, 538(7625), 311–313. https://doi.org/10.1038/538311a
Ekman, P. (1992). An argument for basic emotions. Cognition & Emotion, 6(3-4), 169–200. https://doi.org/10.1080/02699939208411068
Kołakowska, A., Landowska, A., Szwoch, M., Szwoch, W., &Wrobel, M. (2013). Emotion recognition and its application in software engineering. In Proceedings of the 6th International Conference on Human System Interactions (HSI) (pp. 532–539). IEEE. https://doi.org/10.1109/HSI.2013.6577865
Lee, W., &Stolfo, S. J. (2000). A framework for constructing features and models for intrusion detection systems. ACM Transactions on Information and System Security (TISSEC), 3(4), 227–261. https://doi.org/10.1145/382912.382914
Lundberg, S. M., & Lee, S. I. (2017). A unified approach to interpreting model predictions. In Advances in Neural Information Processing Systems (NeurIPS 2017), 4765–4774. https://doi.org/10.48550/arXiv.1705.07874
Mehrabian, A. (1996). Pleasure-arousal-dominance: A general framework for describing and measuring individual differences in temperament. Current Psychology, 14(4), 261–292. https://doi.org/10.1007/BF02686918
Myers, B. A. (1998). A brief history of human-computer interaction technology. interactions, 5(2), 44-54. https://doi.org/10.1145/274430.274436
Myers, B., Hollan, J., Cruz, I., Bryson, S., Bulterman, D., Catarci, T., ... & Ioannidis, Y. (1996). Strategic directions in human-computer interaction. ACM Computing Surveys (CSUR), 28(4), 794-809. https://doi.org/10.1145/242223.246855
Mühl, C., Chanel, G., Kierkels, J. J., & Pun, T. (2008). A survey of affective brain computer interfaces: Principles, state-of-the-art, and challenges. Brain-Computer Interfaces, 1(1), 66–84. https://doi.org/10.1080/2326263X.2013.869803
Picard, R. W. (1997). Affective computing. MIT Press.
Picard, R. W. (2003). Affective computing: challenges. International journal of human-computer studies, 59(1-2), 55-64. https://doi.org/10.1016/S1071-5819(03)00052-1
Reuderink, B., Mühl, C., &Poel, M. (2013). Valence, arousal and dominance in the EEG during game play. International Journal of Autonomous and Adaptive Communications Systems, 6(1), 45–62. https://doi.org/10.1504/IJAACS.2013.050691
Sommer, R., &Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. In 2010 IEEE Symposium on Security and Privacy (pp. 305–316). IEEE. https://doi.org/10.1109/SP.2010.25
Sinha, G., Shahi, R., & Shankar, M. (2010, November). Human computer interaction. In 2010 3rd International Conference on Emerging Trends in Engineering and Technology (pp. 1-4). IEEE.https://doi.org/10.1109/ICETET.2010.85
Stiawan, D., Idris, M. Y. I. B., Yusof, R., Budiarto, R., &Anuar, N. B. (2016). Intrusion detection system: A systematic survey of machine learning techniques. International Journal of Advanced Computer Science and Applications (IJACSA), 7(7), 78–85. https://doi.org/10.14569/IJACSA.2016.070711
Tao, J., & Tan, T. (2005, October). Affective computing: A review. In International Conference on Affective computing and intelligent interaction (pp. 981-995). Berlin, Heidelberg: Springer Berlin Heidelberg. https://doi.org/10.1007/11573548_125
Tavallaee, M., Bagheri, E., Lu, W., &Ghorbani, A. A. (2009). A detailed analysis of the KDD CUP 99 data set. In 2009 IEEE Symposium on Computational Intelligence for Security and Defense Applications (pp. 1–6). IEEE. https://doi.org/10.1109/CISDA.2009.5356528
Wang, W., Zhu, M., Zeng, X., Ye, X., & Sheng, Y. (2017). Malware traffic classification using convolutional neural network for representation learning. In 2017 International Conference on Information Networking (ICOIN) (pp. 712–717). IEEE. https://doi.org/10.1109/ICOIN.2017.7899588
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