Machine Learning in Cyber Security Operations

Abstract

The defense of computational devices as well as computer networks against information leaks, theft, and damage to their electronic data, software, hardware, or other components, as well as against interruption or misrepresenting the services they offer, is defined as cyber security by securitystudio.com. In recent years, there has been an unparalleled increase in public interest in machine learning (ML) research. People's learning and working styles are changing as the Internet and social life become more intertwined, yet this also exposes them to major security risks. Protecting confidential data, networks, and computer-connected systems against illegal
cyberattacks is a difficult challenge. Effective cyber security is crucial for this. To solve this issue, recent technologies like machine learning and deep learning are combined with cyberattacks. The write-up covers machine learning technology in cyber security, explores the benefits and limitations of employing them, and offers recommendations for future research. The world of today is highly network-interconnected due to the prevalence of both small personal devices (like smartphones) and large computing devices or services (like cloud computing or online banking). As a result, millions of data bytes are generated, processed, exchanged,
shared, and used every minute to produce results in specific applications. As a result, protecting user privacy, machine (device) security, and data in cyberspace has become a top priority for private citizens, corporate entities, and national governments. Machine learning (ML) has often been used in cybersecurity in recent years, including for biometric-based user authentication and intrusion or virus detection. But ML algorithms are vulnerable to intrusions during both the training and testing phases, which often lead to noticeable performance decreases and security vulnerabilities. Comparatively little studies have been conducted to ascertain the type,
extent, and defense mechanisms of ML methods' vulnerabilities against security threats. Systematizing recent cybersecurity-related initiatives leveraging ML is vital to garner the interest of researchers, scientists, and engineers