AI Driven Intrusion Detection: Enhancing Cyber Security with Machine Learning

The prevalence and severity of cyberattacks have grown in tandem with the expansion of internet use and online services. IDS are crucial in preventing cyberattacks by seeing suspicious activity in a network. The vast majority of traditional IDS rely signature-based detection, which only works against previously identified threats. A research-oriented investigation on the capability of AI and ML to improve IDS performance is presented in this paper. With an eye on intrusion detection, this article compares and contrasts various supervised ML techniques, such as decision tree (DT), random forest, and support vector machine (SVM). We describe the NSL-KDD dataset and how to employ feature selection approaches to improve the accuracy and efficiency of our models. This dataset is commonly used to evaluate the performance of intrusion detection systems. The focus of the research is on improving the classification of legitimate and harmful communications through the learning. Classical measures considered while measuring performance. The purpose of this research is to provide some ground rules for AI-driven intrusion detection systems, with an eye towards the future and the possibilities presented by cutting-edge techniques like deep learning and real-time data analytics.