A Review on Utilization of Machine Learning Algorithms for Real-Time Intrusion Detection and Classification

Abstract

Information systems are being challenged with increasingly complex difficulties. New tactics are used to frame and launch threats and attacks. The tactics used by cybercriminals have become increasingly complex and sophisticated, making it challenging for organizations to protect their sensitive data from potential breaches. Information constantly changes as it passes across subtle domains, depending on the users, system administrators, and other people who need access. This dynamic nature of information makes it even more challenging to secure these networks against potential threats. Information system security is crucial against threats like intrusions and denial-of-service attacks. Intrusion is one of the most significant threats to network security. Intruders use a variety of methods to gain access to networks, such as exploiting back doors and holes in the system or using the identities of authorized users to gain access to sensitive data. Once inside the network, they can cause severe damage by stealing data or launching denial-of-service attacks. Using the identities of authorized users or any back doors and holes in the network, the intrusion is a serious threat to unauthorized data or lawful networks. Intrusion Detection Systems are mechanisms created to identify intrusions at different levels.