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Publication Type : Journal Article
Thematic Areas : Amrita Center for Cybersecurity Systems and Networks
Publisher : Journal of Ambient Intelligence and Humanized Computing (2020)
Source : Journal of Ambient Intelligence and Humanized Computing (2020)
Campus : Amritapuri
School : Centre for Cybersecurity Systems and Networks
Center : Cyber Security
Department : cyber Security
Year : 2020
Abstract : Cyber-Physical Systems (CPSs) integrate the interdisciplinary fields of computing, networking and control to perform tasks in the real world. CPSs have recently found applications in many battery-powered devices with stringent energy consumption requirements. To ensure secure operation, CPS necessitates sufficient security mechanisms to be incorporated against cyber attacks. However, maximizing energy efficiency and improving security are desirable but contrasting requirements. Towards reducing energy consumption, the optimal strategy for CPS is to initialize the security mechanism dynamically, at the onset of cyberattacks. In the absence of attacks, CPS can deactivate the security mechanism to minimize energy consumption. In the case of CPS, this approach is novel and contrary to the traditional approach of long-term, continual operation of the security mechanism. Towards this goal, we use a decision-centric approach based on Markov Decision Process (MDP) to estimate a threshold upon which the system initiates its security mechanism. We evaluate our proposed mechanism using MATLAB based TrueTime simulator. Evaluation shows that our proposed MDP-based approach achieves maximum energy-savings of 8.26 and 11.05% in defending against Denial-of-Service and Deception attacks, respectively. Further, our approach can be used to develop sustainable CPS designs that balance the trade-off between energy-efficiency and security.
Cite this Research Publication : Sriram Sankaran, Jithish J., and Achuthan, K., “A Decision-centric approach for secure and energy-efficient cyber-physical systems”, Journal of Ambient Intelligence and Humanized Computing, 2020.