International Journal of Computer Science & Network Security

국제컴퓨터통신보호논문지학회 (International Journal of Computer Science & Network Security)
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FLORA: Fuzzy Logic - Objective Risk Analysis for Intrusion Detection and Prevention

  • 투고 : 2023.05.05
  • 발행 : 2023.05.30
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초록

The widespread use of Cloud Computing, Internet of Things (IoT), and social media in the Information Communication Technology (ICT) field has resulted in continuous and unavoidable cyber-attacks on users and critical infrastructures worldwide. Traditional security measures such as firewalls and encryption systems are not effective in countering these sophisticated cyber-attacks. Therefore, Intrusion Detection and Prevention Systems (IDPS) are necessary to reduce the risk to an absolute minimum. Although IDPSs can detect various types of cyber-attacks with high accuracy, their performance is limited by a high false alarm rate. This study proposes a new technique called Fuzzy Logic - Objective Risk Analysis (FLORA) that can significantly reduce false positive alarm rates and maintain a high level of security against serious cyber-attacks. The FLORA model has a high fuzzy accuracy rate of 90.11% and can predict vulnerabilities with a high level of certainty. It also has a mechanism for monitoring and recording digital forensic evidence which can be used in legal prosecution proceedings in different jurisdictions.

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과제정보

The author sincerely wishes to thank Dr. Qais Qassim and Professor Ahmed Patel for allowing the use of data and technical content from their previous research initiatives and published works.

참고문헌

  1. Patel, A., Qassim, Q., Wills, C. A survey of intrusion detection and prevention systems, Information Management & Computer Security. 18:277-290. (2010).  https://doi.org/10.1108/09685221011079199
  2. Gupta, B. B., Srinivasagopalan, S. Handbook of Research on Intrusion Detection Systems. IGI Global. ISBN10: 1799822427. (2020). 
  3. Shamshirband, S., Anuar, N. B., Kiah, M. L. M., Patel, A. An appraisal and design of a multi-agent system based cooperative wireless intrusion detection computational intelligence technique. Journal of Engineering Applications of Artificial Intelligence. 26:2105-2127. (2013).  https://doi.org/10.1016/j.engappai.2013.04.010
  4. Huebscher, M. C., McCann, J. A. A survey of autonomic computing-degrees, models, and applications. ACM Computing Surveys (CSUR). 40(3):1-28. (2008).  https://doi.org/10.1145/1380584.1380585
  5. Amin, S. O., Siddiqui, M. S., Hong, C. S., Lee, S. RIDES: Robust intrusion detection system for IP-based ubiquitous sensor networks. Sensors. 9:3447-3468. (2009).  https://doi.org/10.3390/s90503447
  6. Patel, A., Taghavi, M., Bakhtiyari, K., Junior, J. C. An intrusion detection and prevention system in cloud computing: A systematic review. Journal of Network and Computer Applications. 36:25-41. (2013).  https://doi.org/10.1016/j.jnca.2012.08.007
  7. Tjhai, G. C., Furnell, S. M., Papadaki, M., Clarke, N. L. A preliminary two-stage alarm correlation and filtering system using SOM neural network and K-means algorithm. Computers & Security. 29:712-723. (2010).  https://doi.org/10.1016/j.cose.2010.02.001
  8. Mansour, N., Chehab, M., Faour, A. Filtering intrusion detection alarms. Cluster Computer. 13:19-29. (2010).  https://doi.org/10.1007/s10586-009-0096-9
  9. Spathoulas, G. P., Katsikas, S. Reducing false positives in intrusion detection systems. Computers & Security. 29:35-44. (2010).  https://doi.org/10.1016/j.cose.2009.07.008
  10. Zeng J., Li, T., Li, G., Li, H. A New Intrusion Detection Method Based on Antibody Concentration. In: D. S. Huang, K. H. Jo, H. H. Lee, H. J. Kang, V. Bevilacqua (Eds.) Emerging Intelligent Computing Technology and Applications. With Aspects of Artificial Intelligence. Springer Berlin Heidelberg, 5755:500-509. (2009). 
  11. Anuar N. B., Papadaki, M., Furnell, S., Clarke, N. Incident prioritisation using analytic hierarchy process (AHP): Risk Index Model (RIM). Security and Communication Networks. 6:1087-1116. (2013).  https://doi.org/10.1002/sec.673
  12. Qassim, Q.S., Jamil, N., Daud, M., Patel, A., Ja'affar, N. A review of security assessment methodologies in industrial control systems. Information and Computer Security. 27(1):47-61. (2019).  https://doi.org/10.1108/ICS-04-2018-0048
  13. Bringas, P., Penya, Y. Next-Generation Misuse and Anomaly Prevention System. In: J. Filipe, J. Cordeiro (Eds.) Enterprise Information Systems. Springer Berlin Heidelberg. 19:117-129. (2009). 
  14. Qassim, Q., Patel, A. and Mohd-Zin, A. Strategy to Reduce False Alarms in Intrusion Detection and Prevention Systems. International Arab Journal of Information Technology (IAJIT), 11(5):500-506. (2014). 
  15. Zhou, Y. P., Fang, J. A. Intrusion detection model based on hierarchical fuzzy inference system. In: Information and Computing Science, 2009. ICIC'09. Second International Conference on, IEEE. pp. 144-147. (2009).
  16. Chen, P. Y., Kataria, G., Krishnan, R. Correlated failures, diversification, and information security risk management. MIS Quarterly. 35:397-422. (2011).  https://doi.org/10.2307/23044049
  17. Patel, A., Alhussian, H., Pedersen, J.M., Bounabat, B., Junior, J.C., Katsikas, S. A nifty collaborative intrusion detection and prevention architecture for Smart Grid ecosystems. Computer Security. 64:92-109. (2016).  https://doi.org/10.1016/j.cose.2016.07.002
  18. Bajpai, S., Sachdeva, A., Gupta, J. P. Security risk assessment: Applying the concepts of fuzzy logic, Journal of Hazardous Materials, 173: 258-264. (2010).  https://doi.org/10.1016/j.jhazmat.2009.08.078
  19. Joint Task Force Transformation Initiative, Guide for Conducting Risk Assessments. NIST special publication 800-30, Revision 1. (2012). 
  20. Brotsis, S., Kolokotronis, N., Limniotis, K., Shiaeles, S., Kavallieros, D., Bellini, E. and Pavue, C. Blockchain solutions for forensic evidence preservation in IoT environments. In 2019 IEEE Conference on Network Softwarization (NetSoft), Paris, France. IEEE. pp. 110-114. (2019). 
  21. Talpur, N., Abdulkadir S. J., Alhussian, H., et al. Deep Neuro-Fuzzy System application trends, challenges, and future perspectives: a systematic survey. Artificial Intelligence Revew. 13:1-49. (2022).