Research Article
Cyber Regulatory Networks: Towards a Bio-inspired Auto-resilient Framework for Cyber-Defense
@INPROCEEDINGS{10.1007/978-3-030-24202-2_12, author={Usman Rauf and Mujahid Mohsin and Wojciech Mazurczyk}, title={Cyber Regulatory Networks: Towards a Bio-inspired Auto-resilient Framework for Cyber-Defense}, proceedings={Bio-inspired Information and Communication Technologies. 11th EAI International Conference, BICT 2019, Pittsburgh, PA, USA, March 13--14, 2019, Proceedings}, proceedings_a={BICT}, year={2019}, month={7}, keywords={}, doi={10.1007/978-3-030-24202-2_12} }- Usman Rauf
Mujahid Mohsin
Wojciech Mazurczyk
Year: 2019
Cyber Regulatory Networks: Towards a Bio-inspired Auto-resilient Framework for Cyber-Defense
BICT
Springer
DOI: 10.1007/978-3-030-24202-2_12
Abstract
After decades of deploying cyber-security systems, it has become a well-known fact that the existing cyber-security architecture has numerous inherent limitations that make the maintenance of the current network security devices unscalable and provide the adversary with asymmetric advantages. These limitations include: (1) difficulty in obtaining the global network picture due to lack of mutual interactions among heterogeneous network devices, (2) poor device self-awareness in current architectures, (3) error-prone and time consuming manual configuration which is not effective in real-time attack mitigation, (4) inability to diagnose misconfiguration and conflict resolution due to multi-party management of security infrastructure. In this paper, as an initial step to deal with these issues, we present a novel bio-inspired auto-resilient architecture. The main contribution of this paper includes: (1) investigation of laws governing the dynamics of correct feedback control in Biological Regulatory Networks (BRNs), (2) studying their applicability for synthesizing correct models for bio-inspired communication networks, i.e. Firewall Regulatory Networks (FRNs), (3) verification of the formal models of real network scenarios, to prove the correctness of the proposed approach through model checking techniques.
