  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.
