Utilizing Machine Learning for Anomaly Detection in Cybersecurity Systems - Fahnun, Budi Utami; Susilowati, Eel; Fadlillah, Hadyan Mardhi; Irawaty (ENDLESS: INTERNATIONAL JOURNAL OF FUTURE STUDIES, 2024)
Anomalies in cybersecurity systems are increasingly complex and sophisticated, making detection difficult using traditional rule-based and signature-based approaches. In facing these challenges, machine learning is crucial to improve real-time anomaly detection capabilities. This study aims to explore the role of machine learning in detecting anomalies in cybersecurity systems. The research method is carried out using a qualitative approach, collecting data from relevant literature and interviews with experts in the fields of cybersecurity and machine learning. The results of this study indicate that machine learning can effectively improve the ability of cybersecurity systems to detect and respond to threats more quickly and accurately. Implementing machine learning allows for deeper analysis of complex cybersecurity data, recognizing unexpected anomalous patterns, and adapting to new attacks. Despite challenges such as data variability and dynamic operational environments, the evaluation of model performance shows significant progress in protecting information systems from increasingly complex threats. The future of anomaly detection in cybersecurity promises the possibility of developing more sophisticated technologies, strengthening defenses against evolving threats, and improving overall security.