Combating temporal composition inference by high-order camouflaged network topology obfuscation

Abstract

Topology inference driven by non-collaborative or incomplete prior knowledge is widely used in pivotal target network sieving and completion. However, perceivable topology also allows attackers to identify the fragile bottlenecks and perform efficacious attacks that are difficult to defend against by injecting indistinguishable low-volume attacks. Most existing countermeasures are proposed to obfuscate network data or set up honeypots with adversarial examples. However, there are two challenges when adding perturbations to live network links or nodes. Firstly, the perturbations imposed on the network cannot be conveniently projected to the original network with poor scalability. Secondly, applying significant changes to network information is laborious and impractical. In short, making a good trade-off between concealment and complexity is challenging. To address the above issues, we propose a fraudulent proactive defending tactic, namely HBB-TSP, to protect live network privacy by combating attacks of temporal network inference. Specifically, to penetrate the critical network structures, HBB-TSP first brings in the Statistical Validation of Hypergraph (SVH) method to identify the pivotal connection information of the network and extract the deep backbone structure. Then, the Temporal Simple Decomposition Weighting (TSDW) strategy is introduced, which can predict the backbone network with evolution rules and add highly obfuscated features at a minimized overhead. Finally, a discriminator with multiple centrality models is used to evaluate the deceptiveness and, in turn, affect the TSDW prediction. The entire process ensures the consistency and robustness of network changes while ensuring effective adversarial resistance. Experimental results on two scale real-world datasets demonstrate the effectiveness and generalization of adversarial perturbations. In particular, it is encouraging that our proposed defending scheme outperforms the advanced countermeasures. It ensures the realization of a deceptive obfuscated network at minimum overhead and is suitable for widespread deployment in scenarios of different scales.