Deakin University

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A Differential Privacy Mechanism for Deceiving Cyber Attacks in IoT Networks

conference contribution
posted on 2023-01-30, 23:31 authored by Guizhen YangGuizhen Yang, M Ge, Shang GaoShang Gao, Xuequan Lu, Leo ZhangLeo Zhang, Robin Ram Mohan DossRobin Ram Mohan Doss
Protecting Internet of Things (IoT) network from private data breach is a grand challenge. Data breach may occur when networks’ statistical information is disclosed due to network scanning or data stored on the IoT devices is accessed by attackers because of lack of protection on IoT devices. To protect IoT networks, effective proactive cyber defence technologies (e.g., Moving Target Defence (MTD) and deception) have been proposed. They defend against attacks by dynamically changing attack surface or hiding true network information. However, little work considered the protection of statistical information of IoT network, such as the number of VLANs or the number of devices across VLANs. This type of information may leak the network’s operational information to attackers (e.g., functional information of VLANs). To address this problem, we propose a differential privacy (DP)-based defence method to mitigate its leakage. In this paper, we strategically obfuscate VLANs’ statistical information by integrating DP with MTD and deception technologies. Software-defined networking technology is leveraged to manage data flows among devices and support shuffling-based MTD. Two strategies (random and intelligent) are considered for defence deployment. A greedy algorithm is designed to explore the trade-off between defence cost and privacy protection level. We theoretically prove that the proposed method meets the definition of DP, thus offering solid privacy protection to the operational information of an IoT network. Extensive experimental results further demonstrate that, for a given defence budget, there exists a trade-off between protection level and cost. Moreover, the intelligent deployment strategy is more cost-effective than the random one under the same settings.



13787 LNCS









Publication classification

E1 Full written paper - refereed

Title of proceedings

Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)


Springer Nature Switzerland

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