Maximizing Network Resilience against Malicious Attacks

Abstract

The threat of a malicious attack is one of the major security problems in complex networks. Resilience is the system-level self-adjusting ability of a complex network to retain its basic functionality and recover rapidly from major disruptions. Despite numerous heuristic enhancement methods, there is a research gap in maximizing network resilience: current heuristic methods are designed to immunize vital nodes or modify a network to a specific onion-like structure and cannot maximize resilience theoretically via network structure. Here we map complex networks onto a physical elastic system to introduce indices of network resilience, and propose a unified theoretical framework and general approach, which can address the optimal problem of network resilience by slightly modifying network structures (i.e., by adding a set of structural edges). We demonstrate the high efficiency of this approach on three realistic networks as well as two artificial random networks. Case studies show that the proposed approach can maximize the resilience of complex networks while maintaining their topological functionality. This approach helps to unveil hitherto hidden functions of some inconspicuous components, which in turn, can be used to guide the design of resilient systems, offer an effective and efficient approach for mitigating malicious attacks, and furnish self-healing to reconstruct failed infrastructure systems.

Power System Resilience: Current Practices, Challenges, and Future Directions

“…Planning-based resilience enhancement methods have also been widely studied [3], [5], [6], [32], [33], [46], [51], [52], [59], [73], [83], [84], [86], [108]- [114], [118], [121]- [123]. These methods are classified as follows: (i) undergrounding distribution and transmission lines, building redundant transmission/distribution routes, and upgrading poles and other structure with stronger materials [43], [85], [90], [95], [96], [105], [124]; (ii) elevating substation and facilities, adding backup generators, and installing remote control switches [91], [92]; (iii) enhancing vegetation management [125], [126]; (iv) determining optimal locations and sizes of battery energy storage units and renewable energy sources (RES) [75], [103], optimal placement of separation relays, and optimal allocation of black start resources; and (v) developing protocols for encrypted communication of critical data [80]; and integrated electricity and natural gas transportation system planning [66], [127].…”

“…Although physical-attacks may not impact a large part of the grid as compared to cyber-attacks, if an attacker identify and attack a critical component in the grid, the damage can be significant. In [59], two different types of malicious attacks have been studied for resilience enhancement strategy which are: high degree adaptive (HDA) and optimal collective influence (CI). Both models rely on identifying and attacking the most critical edges in a graph-based network.…”

Edge Rewiring Goes Neural: Boosting Network Resilience without Rich Features

“…In many previous works, despite that the resilience could be improved, the utility may dramatically drop at the same time (Li et al, 2019;Carchiolo et al, 2019;Wang et al, 2014;Schneider et al, 2011;Chan & Akoglu, 2016;Buesser et al, 2011). This is contradicting with the idea behind improving network resilience and will be infeasible in real-world applications.…”

“…To defend against potential attacks, various defense strategies have been proposed to protect the network functionality from crashing and preserve some of its topologies. The commonly used defense manipulations include adding additional edges (Li et al, 2019;Carchiolo et al, 2019), protecting vulnerable edges (Wang et al, 2014) and rewiring two edges (Schneider et al, 2011;Chan & Akoglu, 2016;Buesser et al, 2011). Among these manipulations, edge rewiring fits well to real-world applications as it induces less functionality changes (e.g.…”

Computational network biology: Data, models, and applications

“…The network resilience is critically important for reducing the functional losses on the inevitable network failures. To cope with network failures, the correlation between network features (topology and dynamics) and network resilience is deeply studied from ecological [312][313][314], biological to social [315], and economic systems [316], to find and design principles to enhance network resilience and prevent system collapse [317,318].…”