Different Mechanisms of Machine Learning and Optimization Algorithms Utilized in Intrusion Detection Systems

Malicious software is an integral part of cybercrime defense. Due to the growing number of malicious attacks and their target sources, detecting and preventing the attack becomes more challenging due to the assault's changing behavior. The bulk of classic malware detection systems is based on statistics, analytic techniques, or machine learning. Virus signature methods are widely used to identify malware. The bulk of anti-malware systems categorizes malware using regular expressions and patterns. While antivirus software is less likely to update its databases to identify and block malware, file features must be updated to detect and prevent newly generated malware. Creating attack signatures requires practically all of a human being's work. The purpose of this study is to undertake a review of the current research on intrusion detection models and the datasets that support them. In this article, we discuss the state-of-the-art, focusing on the strategy that was devised and executed, the dataset that was utilized, the findings, and the assessment that was undertaken. Additionally, the surveyed articles undergo critical analysis and statements in order to give a thorough comparative review. Machine learning and deep learning methods, as well as new classification and feature selection methodologies, are studied and researched. Thus far, each technique has proved the capability of constructing very accurate intrusion detection models. The survey findings reveal that Clearly, the MultiTree and adaptive voting algorithms surpassed all other models in terms of persistency and performance, averaging 99.98 percent accuracy on average.