Improving System Security: Machine Learning Algorithm-Based Intrusion Detection System

In today's era, computer technology is now required in many areas of our daily lives, including banking, entertainment, education, and communication. System security is crucial in the digital age, and detecting intrusion threats is the most difficult problem. Cyber- attacks put data availability, confidentiality, and integrity at risk and make it more difficult to identify intrusions exactly. An intrusion detection system is a method that keeps an eye out for unusual activity on the network and sends out an alarm when it finds it. The paper compares Intrusion Detection System (IDS) taxonomy, an indepth analysis of intrusion detection methods, and widely used evaluation datasets. In order to decrease false positives, find new threats, and effectively detect intruders, researchers work to improve IDS. IDS systems use machine learning (ML) techniques because they have the ability to effectively detect network intrusions. The study examines the most recent developments and trends in machine learning-based hybrid intrusion detection systems (HIDS). Numerous methods have emerged for intrusion detection that make use of the machine learning methodology. Combining results from a comparison survey of different machine learning algorithms, the decision tree-which is renowned It is recommended as a model for discovering result abnormalities due to its quickness and simplicity of usage.