Data Science Approaches to Anomaly Detection in Cybersecurity: Challenges and Solutions

Abstract: Anomaly detection is a critical aspect of cybersecurity aimed at identifying unusual patterns that may signify potential security threats. This paper investigates various data science methodologies for anomaly detection, including statistical methods, machine learning algorithms, and hybrid approaches. We delve into the challenges faced in these methodologies, such as data quality issues, high false positive rates, and the evolving nature of threats. Furthermore, the paper proposes solutions to these challenges, including advanced preprocessing techniques, model optimization, and adaptive models.

Keywords: Data Science, Anomaly Detection, Cybersecurity, Statistical Methods, Predictive Analytics, Threat Detection.