ML in cyber security

Machine learning techniques have been applied in many areas of science due to their unique properties like adaptability, scalability, and potential to rapidly adjust to new and unknown challenges. Cyber security is a fast growing field demanding a great deal of attention because of remarkable progresses in social networks, cloud and web technologies, online banking, mobile environment, smart grid, etc. Diverse machine learning methods have been successfully deployed to address such wide-ranging problems in computer security. This paper discusses and highlights different applications of machine learning in cyber security. This study covers phishing detection, network intrusion detection, testing security properties of protocols, authentication with keystroke dynamics, cryptography, human interaction proofs, spam detection in social network, smart meter energy consumption profiling, and issues in security of machine learning techniques itself.

Machine learning is an effective tool that can be employed in many areas of information security. There exist some robust anti-phishing algorithms and network intrusion detection systems. Machine learning can be successfully used for developing authentication systems, evaluating the protocol implementation, assessing the security of human interaction proofs, smart meter data profiling, etc. Although machine learning facilitates keeping various systems safe, the machine learning classifiers themselves are vulnerable to malicious attacks. There has been some work directed to improving the effectiveness of machine learning algorithms and protecting them from diverse attacks. There are many opportunities in information security to apply machine learning to address various challenges in such complex domain. Spam detection, virus detection, and surveillance camera robbery detection are only some examples