Applications of Machine Learning Techniques in the Realm of Cybersecurity

Machine learning (ML) is the latest buzzword growing rapidly across the world, and ML possesses massive potential in numerous domains. ML technology is a subset of Artificial Intelligence (AI) and empowers digital machines with the ability to learn without being explicitly programmed, i.e., the capability to learn from past experiences. Since the last decade, ML technology has been used in various domains because it possesses numerous interesting characteristics such as adaptability, robustness, learnability, and its ability to take instant actions against unexpected challenges. The traditional cybersecurity systems are built on rules, attack signatures, and fixed algorithms. Thus, the systems can act only upon the ‘knowledge' fed to them and human intervention is continually required for the proper functioning of traditional cybersecurity systems. On the other hand, ML technology can recognize various patterns from past experiences and is capable of predicting or detecting future attacks based on seen or unseen data. The ML technology is capable of handling massive real‐time network data which allows various issues present in conventional cybersecurity systems to be overcome. In the present chapter, various issues related to the applications of ML in cybersecurity have been discussed. The effectiveness of applying ML technology in cybersecurity affairs has been thoroughly investigated. The contemporary challenges being faced by researchers in the realm have been identified and discussed. The current chapter presents available datasets and algorithms for the successful implementation of ML technology in the domain of cybersecurity. The datasets are also compared across various parameters. Finally, applications of ML practices by three renowned businesses, Facebook, Microsoft, and Google are explored.