Academic Journal
Towards Emergent Collective Cognitive Cyber Defense: Architectural Framework for Proactive Cybersecurity
| Title: | Towards Emergent Collective Cognitive Cyber Defense: Architectural Framework for Proactive Cybersecurity |
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| Authors: | Tkach, Volodymyr, Kebande, Victor R. |
| Source: | 2025 10th International Conference on Fog and Mobile Edge Computing (FMEC). :66-73 |
| Publisher Information: | IEEE, 2025. |
| Publication Year: | 2025 |
| Subject Terms: | Generative Adversarial Networks, Cybersecurity, Collective Intelligences, Architectural Frameworks, Cyber Security, Cyber-defense, Theory-of-mind, Learning Systems, Säkerhet, integritet och kryptologi, Computation Theory, Reinforcement Learning, Network Security, Adaptive Defense, Distributed Systems, Proactive Cybe Defense, Learning Algorithms, Theory Of Minds, Security, Privacy and Cryptography, Meta Cognitions, Meta-cognition, Emergent Collective Intelligence, Federated Learning, Cognitive Systems, Proactive Cyber Defense |
| Description: | The exponential growth and sophistication of cybersecurity threats have shown a rather an uncontrollable approaches, thus rendering traditional static defenses increasingly inadequate. This has necessitated the need for adaptive and intelligent reinforcements. In response, the authors of this paper propose a conceptual Emergent Collective Cognitive Defense (E-CCD) network-a novel architectural framework that unifies meta-cognition, theory-of-mind (ToM), and Emergent Collective Intelligence (ECI) into a cohesive, adaptive defense system. Unlike conventional systems that react solely to known threats, the E-CCD network endows individual agents with the ability to self-assess, continuously learn from experience, and anticipate adversarial behavior through probabilistic reasoning. The primary outcomes of this work include; a detailed architectural framework herein referred as E-CCD network, mathematical formulations for its core components, and a description of how the E-CCD framework enables proactive cyber defense, possible applicability, and key implementation considerations. In addition, the roles of federated learning and consensus algorithms, reinforcement learning, adversarial training are explored too. It is the authors’ opinion that this research lays the groundwork for next-generation cybersecurity systems capable of adapting to evolving threats in real-time. |
| Document Type: | Article Conference object |
| File Description: | application/pdf |
| DOI: | 10.1109/fmec65595.2025.11119380 |
| Access URL: | http://urn.kb.se/resolve?urn=urn:nbn:se:bth-28666 |
| Rights: | STM Policy #29 |
| Accession Number: | edsair.doi.dedup.....d7a3801a024cc606cca02c4d42364b02 |
| Database: | OpenAIRE |
| DOI: | 10.1109/fmec65595.2025.11119380 |
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