Behavioral Biometrics: A Powerful Defense against Social Engineering Attacks

Abstract

Behavioral biometrics is a paradigm shift in cybersecurity threat protection, as it is taking a back seat in battling infernally advanced social engineering, said to be unmatched by the traditional methods of authentication. This article details how behavioral biometric systems use patterns of the unique user-device interaction to develop a digital behavioral fingerprint, enabling constant and seamless authentication. Analyzing the keystroke dynamics, movements of the mouse pointer, swipe gestures, and the way of handling devices, these systems are able to detect minor anomalies characterizing fraudulent access attempts even when the attackers have valid sign-in credentials. It shows how well behavioral biometrics has stood against account takeovers, detecting remote access malware, and overcoming authorized push payment fraud. Complex machine learning algorithms, and more specifically, Multi-layer Perceptron architectures, have greatly boosted the correct and dependable operation of behavioral authentication solutions. This article assesses such performance measures as False Acceptance Rate, False Rejection Rate, and Equal Error Rate in order to identify the effectiveness of a certain system to unveil the behavioral biometrics levels of trade-offs between increased security and positive user experience. Social engineering attacks keep being innovated, and behavioral biometric solutions offer a dynamic layer of security that is predicated on user behavior rather than information known.