Intelligent deception techniques against adversarial attack on the industrial system. Issue 5 (25th February 2021)

• Record Type: Journal Article
• Authors: Kumari, Suchi; Yadav, Riteshkumar Jayprakash; Namasudra, Suyel; Hsu, Ching‐Hsien
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 5(2021)
• Page Start: 2412
• Page End: 2437
• Publication Date: 2021-02-25
• Subjects: community deception -- constant communities -- persistence -- safeness; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22384
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: Community detection algorithms (CDAs) are aiming to group nodes based on their connections and play an essential role in the complex system analysis. However, for privacy reasons, we may want to prevent communities or a group of nodes in the complex industrial network from being discovered in some instances, leading to the topics on community deception. In this paper, we introduce and formalize two intelligent community deception methods to conceal the nodes from various CDAs. We used node‐based matrices, persistence and safeness scores, to formalize the optimization problems to confound the CDAs. The persistence score is used to destabilize the constant communities in the network while the safeness score is used to assess the level of hiding of a node from CDAs. The objective functions aim to minimize the persistence score and maximize the safeness score of the nodes in the network. From the simulation results, it can be analyzed that the proposed strategies are intelligently concealing the community information in the complex industrial system.