A UNIFIED FRAMEWORK FOR ADVERSARIAL THREAT DETECTION AND ZERO-DAY MITIGATION IN ENTERPRISE NETWORKS
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The resilience of enterprise cybersecurity infrastruc- tures depends on the ability to detect and neutralize adversarial threats across heterogeneous networks. This paper presents a structured analysis of two distinct cyberattack categories: Network-level Intrusion Events (NIE), encompassing discrete, high-volume attacks; and Behavioral Exploitation Chains (BEC), involving multi-stage, low-footprint lateral movement and persis- tent compromise. We develop analytical threat models grounded in graph-theoretic propagation and evaluate architectural limits on adversarial evasion. Furthermore, we propose a multi-layered detection architecture that integrates ensemble machine learning, LSTM-based behavioral sequence modeling, graph-theoretic lat- eral movement analysis, and cryptographic provenance tracking. Our analysis explores detection complexity bounds and the convergence properties of behavioral baselines. Ultimately, this work contributes a structured taxonomy and an analytically grounded framework to advance resilient enterprise network defense.
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Iqbal, D. (2026). A Unified Framework for Adversarial Threat Detection and Zero-Day Mitigation in Enterprise Networks. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.356
Iqbal, Danish. "A Unified Framework for Adversarial Threat Detection and Zero-Day Mitigation in Enterprise Networks." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.356.
Iqbal, Danish. "A Unified Framework for Adversarial Threat Detection and Zero-Day Mitigation in Enterprise Networks." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.356.
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