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.

翻译：恶意软件是网络犯罪防御的重要组成部分。由于恶意攻击数量及其目标来源的不断增长，攻击行为的动态变化使得检测和防御变得更具挑战性。传统恶意软件检测系统大多基于统计、分析技术或机器学习。病毒签名方法被广泛用于识别恶意软件。大多数反恶意软件系统通过正则表达式和模式对恶意软件进行分类。虽然杀毒软件更新数据库以识别和阻止恶意软件的频率较低，但文件特征必须更新以检测和防御新生成的恶意软件。创建攻击签名几乎需要全部人工工作。本研究旨在对当前入侵检测模型及其支持数据集的研究进行综述。本文探讨了最新技术进展，重点分析了已设计与实施的策略、所使用的数据集、研究结果以及开展的评估。此外，为提供全面的比较分析，所综述的文献均经过严格分析和论述。我们研究并探讨了机器学习与深度学习方法，以及新的分类和特征选择技术。迄今为止，每种方法都证明了构建高精度入侵检测模型的能力。调查结果显示，显然，MultiTree和自适应投票算法在持久性和性能方面超越了所有其他模型，平均准确率达99.98%。