IMPOSTERS ANOMALY DETECTION
DOI:
https://doi.org/10.4314/jfas.v13i1.14Keywords:
Cyber Security; Intrusion Detection System; Software-based detection; Keystroke Dynamics; Network-based detection.Abstract
malicious users still find their way around these security measures and gain access to secure systems. This study consists of shedding some light on the security issues in the intrusion detection systems, their vulnerabilities and drawbacks. A hypothesis is proposed to help mitigate these issues and obtain a fast and a more precise method for the detection of different malicious intruders and imposters, study their behavior and make a statistical comparison of data from the used IDSs and throughout the process. This study will state the current available technologies of IDSs, site their challenges and implement a new software-based methodology to increase the detection and reduce false alarm rates for the IDS.
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References
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[2] Li W. Using genetic algorithm for network intrusion detection. Proceedings of the United States department of energy cyber security group, 2004. 1, 1-8.
[3] GrahamR .FAQ: Network intrusion detection systems. http://www. robertgraham. com/pubs/network-intrusion-detection. html. 2000.
[4] Vaccaro H S. Detection of anomalous computer session activity. Retrieved from. 1988.
[5] Tidjon L N, Frappier M, Mammar A. Intrusion Detection Systems: A Cross-Domain Overview. IEEE Communications Surveys & Tutorials, 2019. 21(4), 3639-3681.
[6] Davies R M. Firewalls, Intrusion Detection Systems and Vulnerability Assessment: A Superior Conjunction? Network security, 2002. 9(1), 8-11.
[7] Chandola V, Banerjee A, Kumar V. Anomaly detection: A survey. ACM computing surveys (CSUR), (2009). 41(3), 1-58.
[8]Chapelle O, Scholkopf B, Zien A. Semi-supervised learning (chapelle o. et al. eds.; 2006)[book reviews]. IEEE Transactions on Neural Networks, 2009. 20(3), 542-542.
[9]Jackson P. Introduction to expert systems: Addison-Wesley Longman Publishing Co., Inc. 1998.
[10] Dutta S. Knowledge processing and applied artificial intelligence: Elsevier. 2014.
[11]Russell S. ve Norvig, P. Artificial Intelligence: A Modern Approach. 1995.
[12]Stone P, Veloso M. Multiagent systems: A survey from a machine learning perspective. Autonomous Robots, 2000. 8(3), 345-383.
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Published
2020-10-23
How to Cite
TAZEROUTI, A.; IKRAM, A. IMPOSTERS ANOMALY DETECTION. Journal of Fundamental and Applied Sciences, [S. l.], v. 13, n. 1, p. 243–263, 2020. DOI: 10.4314/jfas.v13i1.14. Disponível em: https://jfas.info/index.php/JFAS/article/view/948. Acesso em: 30 jan. 2025.
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