Machine Learning Techniques for Network Security

Abstract

Machine learning can be used to identify security risks in networks by continuously observing network behavior and looking for irregularities. Massive volumes of data are processed in almost real time by machine learning engines, which then identify crucial incidents. These methods enable the identification of unknown malware, insider threats, and policy infractions. By identifying “bad neighborhoods” online, machine learning can also assist users in avoiding connecting to dangerous websites. Every Internet user needs online security. Ensuring online security is critical for all internet users, as most cyber attackers are opportunistic, targeting widespread weaknesses rather than specific websites or organizations. Machine learning (ML) may educate robots to spot patterns and detect harmful or anomalous activity more efficiently than people or traditional software.