Pembelajaran Mesin untuk Sistem Keamanan - Literatur Review

Security systems are one of the crucial topics in the era of digital transformation. In the use of digital technology, security systems are used to ensure the confidentiality, integrity, and availability of data. Machine learning techniques can be applied to support the system's adaptability to the environment, so that prevention, detection and recovery can be carried out. Given the importance of these things, it is necessary to review the literature to find out how machine learning is applied to security systems. This paper presents a summary of 31 research papers to determine what machine learning techniques or methods are the most promising for prevention, detection and recovery. The research stages in this paper consist of 6 stages, namely: formulating research questions, searching for articles, documenting search strategies, selecting studies, assessing article quality, and extracting data obtained from articles. Based on the results of the study, it was found that the K-means method was the most promising for prevention, while for detection, SVM could be used, and for security recovery, machine learning could be implemented using NLP-based features.