Paper Title | : | Fuzzy zero day exploits detector system |
Author Name | : | Adnan Shaout and Cameron Smyth |
Abstract | : | Intrusion detection systems today are relatively capable of detecting network intrusions by attackers. Unfortunately, these systems operate on a network level and not on a system level. Meanwhile, antivirus software is typically capable of detecting known viruses but cannot easily stop zero day exploits. The paper will propose a fuzzy inference system to detect exploitation of a system using system metrics such as CPU, memory usage and network connections. This system is implemented using the MATLAB fuzzy logic toolbox. The design was tested and provided reasonable results. |
Keywords | : | Intrusion detection system, Fuzzy exploit monitor, Fuzzy inference system, Computer security, Zero day exploits. |
Cite this article | : | Adnan Shaout and Cameron Smyth .Fuzzy zero day exploits detector system. International Journal of Advanced Computer Research. 2017;7(31):154-163. DOI:10.19101/IJACR.2017.730022 |
References | : |
[1]Chen WW. Statistical methods in computer security. CRC Press; 2004. [2]Jesdanun A. School prank starts 25 years of security woes. http://www.nbcnews.com/id/20534084/#.V5bI8GXZpg1. Accessed 4 April 2016. [3]Anderson JP. Computer security threat monitoring and surveillance. Technical Report, James P. Anderson Company, Fort Washington, Pennsylvania; 1980. [4]Denning DE. An intrusion-detection model. IEEE Transactions on Software Engineering. 1987; SE-13(2):222-32. [5]Vaccaro HS, Liepins GE. Detection of anomalous computer session activity. In IEEE symposium on security and privacy, proceedings 1989 (pp. 280-9). IEEE. [6]Abadeh MS, Habibi J, Lucas C. Intrusion detection using a fuzzy genetics-based learning algorithm. Journal of Network and Computer Applications. 2007; 30(1):414-28. [7]Wang G, Hao J, Ma J, Huang L. A new approach to intrusion detection using artificial neural networks and fuzzy clustering. Expert Systems with Applications. 2010; 37(9):6225-32. [8]Mkuzangwe NN, Nelwamondo FV. A fuzzy logic based network intrusion detection system for predicting the TCP SYN flooding attack. In Asian conference on intelligent information and database systems 2017 (pp. 14-22). Springer, Cham. [9]Shanmugavadivu R, Nagarajan N. Network intrusion detection system using fuzzy logic. Indian Journal of Computer Science and Engineering. 2011; 2(1):101-11. [10]Kudłacik P, Porwik P, Wesołowski T. Fuzzy approach for intrusion detection based on users commands. Soft Computing. 2016; 20(7):2705-19. [11]Azad C, Jha VK. Fuzzy min–max neural network and particle swarm optimization based intrusion detection system. Microsystem Technologies. 2017; 23(4):907-18. [12]Ramakrishnan S, Devaraju S. Attack’s feature selection-based network intrusion detection system using fuzzy control language. International Journal of Fuzzy Systems. 2017; 19(2):316-28. [13]http://www.unixtop.org. Accessed 4 April 2016. |