A resilient distributed optimization algorithm based on consensus of multi-agent system against two attack scenarios

Resilient Distributed Optimization Against Cyber-Attacks

“…where δ ij (t) is the malicious signal injected into the communication link (j, i) ∈ E. In contrast to prior works [8], [9], [13], [14] which limit the number of attacks while leaving their magnitude unrestricted, we do not impose any restrictions on the number of attacks. However, we assume that the attacks have a bounded magnitude, i.e., the injections satisfy the following assumption.…”

“…In contrast to the work in [11], [12], which focus on denial-ofservice attacks on the communication network, this paper aims to design a resilient distributed optimization algorithm against false data injection (FDI) attacks. A novel filtering strategy is proposed in [13], ensuring that the local estimates of the agents converge to the original optimization problem under FDI attacks on the communication network. However, this strategy comes with certain limitations, including restrictions on the maximum number of links that can be attacked and constraints on the network's connectivity.…”

“…• In contrast to the settings in [13]- [15], we consider a scenario where both the agent's update of its local estimate and communication channel are subjected to FDI attacks. Moreover, the adversary is able to insert different injections into each communication link.…”

“…• Unlike the strategies in [8], [9], the proposed strategy ensures that all local estimates converge closely to the optimal solution of the optimization problem. • In contrast to the strategies in [8], [9], [13], [14], the proposed strategy imposes no restrictions on the maximum number of tolerable adversarial edges and agents. Moreover, it requires neither high network connectivity nor the existence of trusted/secured agents or edges.…”