Information security technology for computer networks through classification of cyber-attacks using soft computing algorithms - Villaluna, Jason A.; Cruz, Febus Reidj G. (HNICEM : 2017 IEEE 9th International Conference on Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management : 1-3 December 2017, 2017)
The Internet is the global platform which revolutionized the computer and communications domain. Although it becomes one of the most useful tools in people's lives, the presence of cyber-attacks that can cause damage, modification, and theft of vital data and information over this platform has increased. Utilization of soft-computing based on the behavior of the network may detect new or modified old attacks. An information security system is developed for the recognition the network infrastructure's behavior. This is limited to Normal, DoS, Probe, U2R, and R2L. The packets on the network are processed in MATLAB and analyze using Fuzzy Logic, Artificial Neural Network, and Fuzzy-Neural Network. Different tests are done with different datasets of varied parameters. The best model for each algorithm, which is rendered from the tests, is used for the information security system. The cyber-attacks were identified within a short period: 51.64us for Fuzzy Logic, 1.34us for Artificial Neural Network, and 14.23us for the Fuzzy Neural Network. The detection rate and accuracy of the three algorithms are 94.84%, 98.51%, 98.60% and 89.74%, 96.09%, 96.19% respectively. The Fuzzy Neural Network has the best performance which used the advantage of Fuzzy Logic and Artificial Neural Network.