Cyberpsychology: A Longitudinal Analysis of Cyber Adversarial Tactics and Techniques

Abstract

The rapid proliferation of cyberthreats necessitates a robust understanding of their evolution and associated tactics, as found in this study. A longitudinal analysis of these threats was conducted, utilizing a six-year data set obtained from a deception network, which emphasized its significance in the study’s primary aim: the exhaustive exploration of the tactics and strategies utilized by cybercriminals and how these tactics and techniques evolved in sophistication and target specificity over time. Different cyberattack instances were dissected and interpreted, with the patterns behind target selection shown. The focus was on unveiling patterns behind target selection and highlighting recurring techniques and emerging trends. The study’s methodological design incorporated data preprocessing, exploratory data analysis, clustering and anomaly detection, temporal analysis, and cross-referencing. The validation process underscored the reliability and robustness of the findings, providing evidence of increasingly sophisticated, targeted cyberattacks. The work identified three distinct network traffic behavior clusters and temporal attack patterns. A validated scoring mechanism provided a benchmark for network anomalies, applicable for predictive analysis and facilitating comparative study of network behaviors. This benchmarking aids organizations in proactively identifying and responding to potential threats. The study significantly contributed to the cybersecurity discourse, offering insights that could guide the development of more effective defense strategies. The need for further investigation into the nature of detected anomalies was acknowledged, advocating for continuous research and proactive defense strategies in the face of the constantly evolving landscape of cyberthreats.

An Interdisciplinary Approach to Enhancing Cyber Threat Prediction Utilizing Forensic Cyberpsychology and Digital Forensics

“…Human Factors in Cybersecurity [3,17,26,28,30] Understanding human factors in cybersecurity is crucial. Tennakoon (2011) [28] advocated for a holistic approach, combining learnings from CBDFA to enhance predictive modeling.…”

“…Greitzer and Hohimer (2011) [17] supported this assertion, highlighting the importance of modeling human behavior to anticipate insider attacks. Incorporating human factors may significantly improve cyber threat prediction [3,26,30].…”

“…For instance, predictive models can forecast potential cyberattacks, and understanding the behavioral triggers, drivers, and patterns of cybercriminal behavior could significantly refine these predictions [24]. Similarly, integrating cyberpsychological profiles into digital forensics investigations could enhance accuracy regarding attribution, that is, identifying potential cybercriminals (lone and organized) and understanding respective modus operandi [1,25,26].…”

“…Cyber Behavioral Analysis Metric (CBAM): A metric used in cyber behavioral analysis to quantify and evaluate specific behaviors or trends in cyber environments [25,26].…”

“…Cyber Behavioral Score (CBS): A scoring system that quantifies cyber behavior, often used in predictive models and threat assessments [26]. 8.…”