Detection and Prevention of Cyber Defense Attacks using Machine Learning Algorithms

Abstract

Recent advancements in computing power, memory capacities, and connectivity have led to a corresponding surge in the utilization of big data, online platforms' prevalence, and machine learning's sophistication. Concerns regarding safety and the need for state-of-the-art security tools and methods to counter evolving cybercrime are amplified by the swift digitization of the world. This study investigates defensive and offensive applications of machine learning in cybersecurity. Additionally, it explores potential strategies to mitigate cyberattacks on machine learning models. The focus is on how machine learning facilitates cyberattacks, including developing intelligent botnets, advanced phishing using spear techniques, and deploying stealthy malware. Furthermore, the paper highlights the significance of artificial intelligence in digital safety, emphasizing its role in malware analysis, network vulnerability assessment, and threat prediction.