Parallel Model to Detect Attacks Using Evolutionary Based Technique

Abstract

Evolutionary-based algorithms emerged due to their flexibility and effectiveness in solving different varieties of problems. Optimization-based techniques are used in finding solutions that involve multiple conflicting objectives. Parallel evolutionary-based algorithms are used to overcome the time-consuming job of finding solutions to these types of problems. In this paper, we present a parallel genetic programming-based model that runs parallelly and obtains solutions in a minimal amount of time. The model also allows the user to select the best set of objectives based on the requirements of the users. An island model is used which runs the operations on different islands parallelly. This not only decreases the execution time of the process but also increases the diversity of the population. The results obtained in different islands are fed to an ensemble classifier to get the required result. The model was trained and tested using the state-of-the-art ISCX-2012 and CICIDS2017 datasets. In our work, we have mainly focused on detecting the attacks in a system in a short duration of time. The model developed gave significant performance improvement compared to the results obtained using the normal CPU implementation.