Game-Theoretic Approach to Malicious Controller Detection in Software Defined Networks

Sridharan, Vignesh; Gurusamy, Mohan

doi:10.1109/icc.2018.8422403

Cited by 6 publications

(1 citation statement)

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“…In a multi-controller architecture, the detection of fraudulent rules and malicious controller in [52,53] is achieved by replicating each rule setup request to multiple controllers simultaneously in order to check for rule consistency, which incurs a high overhead. In order to deal with the issue, requests are randomly replicated in [54].…”

Section: Related Workmentioning

confidence: 99%

Blockchain and Random Subspace Learning-Based IDS for SDN-Enabled Industrial IoT Security

Derhab

Gabsi

Gumaei

et al. 2019

Sensors

134

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The industrial control systems are facing an increasing number of sophisticated cyber attacks that can have very dangerous consequences on humans and their environments. In order to deal with these issues, novel technologies and approaches should be adopted. In this paper, we focus on the security of commands in industrial IoT against forged commands and misrouting of commands. To this end, we propose a security architecture that integrates the Blockchain and the Software-defined network (SDN) technologies. The proposed security architecture is composed of: (a) an intrusion detection system, namely RSL-KNN, which combines the Random Subspace Learning (RSL) and K-Nearest Neighbor (KNN) to defend against the forged commands, which target the industrial control process, and (b) a Blockchain-based Integrity Checking System (BICS), which can prevent the misrouting attack, which tampers with the OpenFlow rules of the SDN-enabled industrial IoT systems. We test the proposed security solution on an Industrial Control System Cyber attack Dataset and on an experimental platform combining software-defined networking and blockchain technologies. The evaluation results demonstrate the effectiveness and efficiency of the proposed security solution.

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Section: Related Workmentioning

confidence: 99%