Unmasking the Shadows: Recognizing and Overcoming Hidden Pitfalls in UEBA

Kumrashan Indranil Iyer

Abstract: User and Entity Behavior Analytics (UEBA) has revolutionized cybersecurity by enabling dynamic, behavior-based threat detection. By modeling normal behavioral patterns, UEBA identifies insider threats, compromised credentials, and advanced persistent threats (APTs) that traditional signature-based and rule-based systems often overlook. However, several challenges such as data bias, model drift, false positives, and adversarial exploitation, pose significant risks to its efficacy. This paper examines these hidden pitfalls, their symptoms and proposes effective mitigation strategies. By addressing these challenges, organizations can enhance the resilience and accuracy of behavioral analytics, strengthening enterprise security.

Keywords: User and Entity Behavior Analytics (UEBA), cybersecurity, anomaly detection, machine learning, adversarial attacks, model drift, insider threats

How to Cite?: Kumrashan Indranil Iyer, "Unmasking the Shadows: Recognizing and Overcoming Hidden Pitfalls in UEBA", Volume 12 Issue 4, April 2023, International Journal of Science and Research (IJSR), Pages: 1988-1993, https://www.ijsr.net/getabstract.php?paperid=SR230413090718, DOI: https://dx.doi.org/10.21275/SR230413090718