Big Data Cyber Security Using Machine Learning

Abstract

It is well-known that cyber protection in the context of big data is a crucial issue and also presents a significant challenge to the research study field. The use of artificial intelligence algorithms has been suggested as a potential solution for complex information security issues. Support vector machines (SVMs), one of these techniques, have shown outstanding performance on several category problems. However, in order to construct an effective SVM, the user must first determine the correct SVM configuration, which is a difficult task that necessitates expert knowledge as well as a substantial amount of hands-on initiative for trial and error. In this study, precision and design intricacy are treated as two conflicting goals in our formulation of the SVM arrangement technique as a bi-objective optimisation problem. We propose a brand-new, issue-domain-independent hyper-heuristic framework for bi-objective optimisation. This is the first time a hyper-heuristic for this issue has been developed. The suggested hyper-heuristic framework consists of both high-level and low-level heuristics. The high-level strategy makes decisions on which low-level heuristics to employ in order to create a fresh SVM setup based on the search performance. Each of the low-level heuristics successfully locates the SVM layout search region using a distinct set of rules. The suggested structure adaptively integrates Pareto-based and disintegration-based strategy toughness to approximate the Pareto collection of SVM configurations in order to handle bi-objective optimisation. The effectiveness of the suggested structure has been evaluated in relation to two cyber security concerns: the detection of anomalous intrusions and Microsoft malware's large information category. The obtained results show that the suggested framework is quite effective, if not superior, when compared to its competitors and other formulas.