SecureSense : AI/ML Based Anomaly Detection Tool

Cybersecurity threats continue to proliferate, presenting an ever-growing challenge for organizations striving to safeguard their digital assets. In response to this escalating threat landscape, research in the field of anomaly detection has gained significant momentum. This paper presents an in-depth exploration of cutting-edge research in Behavior-Based Anomaly Detection Systems (BBADS), focusing on their efficacy in proactively defending against evolving threats. Drawing upon advanced behavioral analysis techniques, machine learning algorithms, and artificial intelligence methodologies, BBADS offer a promising avenue for bolstering cybersecurity resilience. Through a comprehensive review of recent studies and experimental findings, this paper elucidates the key principles underpinning BBADS and their application in real-world scenarios. Furthermore, this research delves into the nuances of model selection, highlighting the critical factors that influence the effectiveness of anomaly detection algorithms. By analyzing the performance metrics and comparative studies of various BBADS architectures, this paper provides valuable insights into the strengths and limitations of different approaches. Moreover, the paper examines the challenges inherent in the implementation of BBADS, including data privacy concerns, computational complexity, and the need for continuous model refinement. By addressing these challenges, researchers can further enhance the practical applicability of BBADS in diverse organizational settings. Through a synthesis of empirical findings and theoretical frameworks, this paper offers a holistic perspective on the state-of-the-art in Behavior-Based Anomaly Detection research. By elucidating the latest advancements and identifying avenues for future exploration, this research aims to contribute to the ongoing evolution of cybersecurity defense mechanisms, ultimately fostering a safer digital environment for organizations and individuals alike.