Machine Learning in Cybersecurity: Techniques and Challenges

In the computer world, data science is force behind recent dramatic changes in cybersecurity's operations and technologies. The secret to making a security system automated intelligent extract patterns or insights related incidents from cybersecurity construct appropriate data-driven models. Data science, also known as diverse scientific approaches, machine learning techniques, processes, systems, study of actual occurrences via use data. Due its distinctive qualities, such flexibility, scalability, capability quickly adapt new unknowable obstacles, techniques have been used many fields. notable advancements social networks, cloud web technologies, online banking, mobile environments, smart grids, etc., cyber rapidly expanding sector that requires lot attention. Such broad range issues effectively addressed by various techniques. This article covers several machine-learning applications security. Phishing detection, network intrusion keystroke dynamics authentication, cryptography, human interaction proofs, spam detection meter energy consumption profiling, concerns with themselves are all covered this study. methodology involves collecting large dataset phishing legitimate instances, extracting relevant features email headers, content, URLs, training model using supervised algorithms. Machine models can identify emails websites high accuracy low false positive rates. To enhance it recommended continuously update include employ ensemble methods combine multiple for better performance.