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From Anomaly Detection to AI-Optimized SOC Playbooks: A Unified Analytical Approach to Ransomware and Insider Threats

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Eleanor T. Brookstone 1 ,
4 Technical University of Munich, Germany

Abstract

The accelerating complexity of cyber threats has fundamentally altered the operational, analytical, and strategic requirements of contemporary cybersecurity ecosystems. Among these threats, ransomware has emerged as a particularly disruptive and adaptive phenomenon, intertwining technical exploitation with psychological coercion, organizational pressure, and economic extortion. Parallel to this development, insider threats, advanced persistent threats, and large-scale network intrusions have converged into a multifaceted risk landscape that challenges traditional rule-based and signature-driven defense mechanisms. This article develops a comprehensive, publication-ready research framework that integrates artificial intelligence–driven security operations center optimization, anomaly detection, topic modeling, graph-based behavioral analysis, and deep learning architectures into a unified analytical paradigm for advanced cyber threat detection and ransomware investigation. Grounded strictly in the provided scholarly references, the study positions AI-optimized SOC playbooks as an epistemic and operational bridge between reactive incident response and proactive threat intelligence, with particular emphasis on the ransomware investigation lifecycle as articulated by Rajgopal (2025).

The article advances three interlocking contributions. First, it reconstructs the theoretical lineage of cyber threat detection, tracing its evolution from statistical outlier analysis and pattern classification to contemporary deep learning and graph-based behavioral analytics. Second, it proposes a text-based methodological synthesis that conceptually integrates latent topic modeling, kernel-based learning, novelty detection, and user behavior analytics into SOC workflows without reliance on visual or mathematical formalism. Third, it delivers an interpretive results and discussion narrative that situates empirical-style findings within broader scholarly debates on explainability, scalability, class imbalance, and adversarial adaptation. Throughout the paper, ransomware is treated not merely as malware but as a socio-technical process embedded within organizational, psychological, and networked contexts.

By emphasizing theoretical elaboration, critical comparison, and interpretive depth, this work addresses a persistent literature gap: the absence of holistic, AI-driven investigative frameworks that unify ransomware response with insider threat detection and large-scale network analytics. The findings underscore that AI-optimized SOC playbooks, when grounded in rigorous data science principles and contextual awareness, can significantly enhance detection fidelity, investigative coherence, and strategic resilience against evolving cyber threats (Rajgopal, 2025; Chandola et al., 2009; Sommer & Paxson, 2010).

Keywords

Ransomware investigation, security operations center, anomaly detection, deep learning

References

📄 Sommer, R., & Paxson, V. (2010). Outside the closed world: On using machine learning for network intrusion detection. IEEE Symposium on Security and Privacy, 305–316.
📄 Blei, D. M., Ng, A. Y., & Jordan, M. I. (2003). Latent dirichlet allocation. Journal of Machine Learning Research, 3, 993–1022.
📄 Rajgopal, P. R. (2025). AI-optimized SOC playbook for ransomware investigation. International Journal of Data Science and Machine Learning, 5(02), 41–55.
📄 Wasserman, S., & Faust, K. (1994). Social network analysis: Methods and analysis. Cambridge University Press.
📄 Chandola, V., Banerjee, A., & Kumar, V. (2009). Anomaly detection: A survey. ACM Computing Surveys, 41, 1–58.
📄 Shone, N., Ngoc, T. N., Phai, V. D., & Shi, Q. (2018). A deep learning approach to network intrusion detection. IEEE Transactions on Emerging Topics in Computational Intelligence, 2(1), 41–50.
📄 Ali, A., Shamsuddin, S. M., & Ralescu, A. L. (2013). Classification with class imbalance problem. International Journal of Advanced Soft Computing Applications, 5, 1–38.
📄 Barnett, V., & Lewis, T. (1994). Outliers in statistical data. Wiley.
📄 Levandowsky, M., & Winter, D. (1971). Distance between sets. Nature, 234, 34–35.
📄 Wu, X., Zhu, X., Wu, G., & Ding, W. (2014). Data mining with big data. IEEE Transactions on Knowledge and Data Engineering, 26(1), 97–107.
📄 Schultz, E. E. (2002). A framework for understanding and predicting insider attacks. Computers and Security, 21(6), 526–531.
📄 Tan, S.-S., Duraisamy, S., & Na, J.-C. (2019). Unified psycholinguistic framework: An unobtrusive psychological analysis approach towards insider threat prevention and detection. Journal of Information Science Theory and Practice, 7(1), 52–71.
📄 Gamachchi, A., & Boztas, S. (2017). Insider threat detection through attributed graph clustering. Proceedings of the IEEE International Conference on Trust, Security and Privacy in Computing and Communications, 112–119.
📄 Brdiczka, O., Liu, J., Price, B., Shen, J., Patil, A., Chow, R., Bart, E., & Ducheneaut, N. (2012). Proactive insider threat detection through graph learning and psychological context. IEEE Symposium on Security and Privacy Workshops, 142–149.
📄 Markou, M., & Singh, S. (2003). Novelty detection: A review—Part 1: Statistical approaches. Signal Processing, 83, 2481–2497.
📄 Pantelidis, E., Bendiab, G., Shiaeles, S., & Kolokotronis, N. (2021). Insider threat detection using deep autoencoder and variational autoencoder neural networks. IEEE International Conference on Cyber Security and Resilience, 129–134.
📄 Sharma, B., Pokharel, P., & Joshi, B. (2020). User behavior analytics for anomaly detection using LSTM autoencoder. Proceedings of the IAIT Conference, 1–9.
📄 Sood, A. K., & Enbody, R. J. (2013). Targeted cyberattacks: A superset of advanced persistent threats. IEEE Security and Privacy, 11(1), 54–61.
📄 Stewart, J. M., Chapple, M., & Gibson, D. (2020). CISSP certified information systems security professional official study guide. Wiley.
📄 Zhang, Y., Dang, J., & Sun, L. (2022). Hybrid deep learning models for advanced threat detection in large-scale networks. ACM Transactions on Privacy and Security, 25(3), 1–28.

How to Cite

Eleanor T. Brookstone. (2025). From Anomaly Detection to AI-Optimized SOC Playbooks: A Unified Analytical Approach to Ransomware and Insider Threats. Global Multidisciplinary Journal, 4(12), 100-107. https://www.grpublishing.org/journals/index.php/gmj/article/view/294

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