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CHAOS MULTIOBJECTIVE EVOLUTIONARY BASED TECHNIQUE TO OBTAIN ACCURATE SOLUTIONS IN INTRUSION DETECTION SYSTEMS

Due to the rapid increase in technology, it becomes necessary to safeguard the system against any sort of external attacks. Intrusion detection system (IDS) plays a vital role in protecting the system against any sort of anomalous behavior. Whenever the system is prone to attack, it becomes difficult to identify the type of attack, due to different security policies of the network. In our research, we have developed a chaos multiobjective framework that can be trained for different objectives and obtain solutions for each combination of objectives. This allows the end user to select the best solution among the broad range of solutions. Experiments were conducted on NSL-KDD, ISCX-2012 and CICIDS2017 datasets. The results obtained shows that Chaos-Nondominated Sorting Genetic Programming (chaosNSGP-II) algorithm produces better spread of solutions compared to the existing frameworks, Nondominated Sorting Genetic Programming (NSGP-II) and Multiobjective Evolutionary Algorithms Based on Decomposition (MOEA/D). Chaos theory was used to control the population size, improve the convergence speed and to avoid falling into local optima.