MACHINE LEARNING IN SAFETY CRITICAL CYBER SECURITY SYSTEM

MACHINE LEARNING IN SAFETY CRITICAL CYBER SECURITY SYSTEM Abstract: Cyber-security is a fast-changing topic of debate owing to new technology that have given way for numerous cybercriminals and attacks in recent years. Cloud computing and elevated levels of machine-to-machine communications also provided possible loopholes for invasion. Therefore, it is necessary to protect the user's privacy, files, and devices in cyberspace. Recent years have seen considerable shift and rise in machine learning (ML) algorithms that are solving four major cyber-security problems, such as IDS, Android malware identification, spam detection, and malware analysis. The thesis studied these algorithms using a knowledge base method and data mining of information obtained from different sources. The outcomes have demonstrated that ML is an effective way to handle cyber-security threats. Keywords: cyber-security issues, machine learning, algorithms, detections Fig.1.Block Diagram m~-j %w- now ma-*teine L *"powow Fig. 2. ML algorithms