PHAST: A Multi-Context Framework for Hybrid Threat Intelligence and Automated Detection Systems

Authors

Victor Obojo, CyberDome Nigeria, Nigeria

Richard Ikuesan, Zayed University, United Arab Emirates

Elias Iortom, CyberDome Nigeria, Nigeria

Published in

IST-Africa 2026 Conference Proceedings

ISSN: 2576-8581

ISBN: 978-1-905824-76-2

DOI: https://doi.org/10.67725/IST-Africa.2026.RJCB8290

Publisher

IST-Africa Institute and IIMC International Information Management Corporation Ltd

Published in Ireland

Abstract

Current threat intelligence practices rely heavily on indicators of compromise, which decay rapidly and provide limited insight into adversary intent, behavioural progression, and hybrid attack patterns. This paper introduces the Proactive Hybrid Adversary Simulation and Tracking framework, a behaviour-oriented threat intelligence model that maps adversarial activity through context-tactic-technique structures rather than static indicators. The framework incorporates localised and global procedural examples, actor profiling, detection sources, and a quantitative scoring system covering Impact, Likelihood, Exposure, Response Readiness, and Confidence. Using this multi-context approach, security analysts and automated systems can reason about threats, detect early behavioural patterns, predict potential escalations, and prioritise mitigation efforts. The paper positions this model as a foundational intelligence layer for AI-driven detection systems, next-generation Threat Intelligence Platforms, and SOC automation.

Keywords

Threat Intelligence, Hybrid Threats, Behavioural Detection, PHAST Framework, Adversary Modelling

Cite this paper

V. Obojo, R. Ikuesan and E. Iortom (2026) "PHAST: A Multi-Context Framework for Hybrid Threat Intelligence and Automated Detection Systems", IST-Africa 2026 Conference Proceedings, Miriam Cunningham and Paul Cunningham (Eds), IST-Africa Institute and IIMC, 2026, ISSN: 2576-8581, ISBN: 978-1-905824-76-2, https://doi.org/10.67725/IST-Africa.2026.RJCB8290

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