Machine Learning-Powered Anomaly Detection: Enhancing Data Security and Integrity

Abstract

Anomaly detection is crucial for the integrity and security of data across various industries. The advent and evolution of machine learning (ML) has significantly enhanced the capabilities of anomaly detection systems, offering more effective, precise, and flexible methods for identifying data irregularities. This paper discusses the application of machine learning techniques in enhancing anomaly detection, particularly in private and governmental data systems. We begin by defining anomaly detection and its importance, followed by an examination of fundamental ML models used in anomaly detection, including supervised, unsupervised, and semi-supervised learning. The paper addresses the challenges in implementing these technologies in private and government sectors, emphasizing the need to balance detection accuracy with ethical concerns like data privacy. Through case studies in IT and financial technology, we illustrate the effectiveness of ML-driven anomaly detection in network security and fraud prevention. We discuss the advancements in algorithms, computing power, and big data, and their roles in improving anomaly detection systems. Looking forward, the paper explores the future of ML in anomaly detection, shifting towards proactive and predictive models. This includes integrating AI with current security systems, applying deep learning techniques, and adapting to emerging threats. The transformative potential of ML for anomaly detection is confirmed, advocating a proactive approach in its implementation while addressing ethical and practical challenges. Ultimately, this paper advocates for the creation of a secure, efficient, and resilient digital ecosystem in both private and government sectors through the intelligent application of ML in anomaly detection.