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A Longitudinal Analysis of Cyber Adversarial Tactics and Techniques
Version 1
: Received: 30 June 2023 / Approved: 30 June 2023 / Online: 30 June 2023 (16:16:04 CEST)
A peer-reviewed article of this Preprint also exists.
Rich, M.S. Cyberpsychology: A Longitudinal Analysis of Cyber Adversarial Tactics and Techniques. Analytics 2023, 2, 618-655. Rich, M.S. Cyberpsychology: A Longitudinal Analysis of Cyber Adversarial Tactics and Techniques. Analytics 2023, 2, 618-655.
Abstract
In the face of escalating cybercriminal sophistication, an innovative approach to network anomaly detection has been pursued in this longitudinal study, integrating computational data analytics in a geographic, organizational, and behavioral context. A data-driven scoring mechanism was employed to systematically analyze and correlate source countries of IP addresses and organization-associated Autonomous System (AS) Numbers (ASN) with network anomalies. Significant correlations between certain countries, specific organizations, and high behavior scores were identified through the data analytics. An increase in connection requests was also found to be linked with elevated behavior scores. Validated by cross-validation techniques, these findings emphasize the necessity for continuous model recalibration. The transformative role of integrative data analytics in cybersecurity is underscored, paving the way for the development of more sophisticated, context-aware anomaly detection systems. Specifically, the analysis underscores the need for organizations to adopt a proactive and adaptive approach to cybersecurity that can keep pace with the evolving threat landscape.
Keywords
Behavioral Analysis; Behavioral Score; Cybersecurity; Data Analytics; Geographic Analysis; Longitudinal Study; Model Recalibration; Network Anomaly Detection; Organizational Analysis; Threat Intelligence
Subject
Computer Science and Mathematics, Information Systems
Copyright: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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