Multiplex network disintegration strategy inference based on deep network representation learning.

Multiplex networks have attracted more and more attention because they can model the coupling of network nodes between layers more accurately. The interaction of nodes between layers makes the attack effect on multiplex networks not simply a linear superposition of the attack effect on single-layer networks, and the disintegration of multiplex networks has become a research hotspot and difficult. Traditional multiplex network disintegration methods generally adopt approximate and heuristic strategies. However, these two methods have a number of drawbacks and fail to meet our requirements in terms of effectiveness and timeliness. In this paper, we develop a novel deep learning framework, called MINER (Multiplex network disintegration strategy Inference based on deep NEtwork Representation learning), which transforms the disintegration strategy inference of multiplex networks into the encoding and decoding process based on deep network representation learning. In the encoding process, the attention mechanism encodes the coupling relationship of corresponding nodes between layers, and reinforcement learning is adopted to evaluate the disintegration action in the decoding process. Experiments indicate that the trained MINER model can be directly transferred and applied to the disintegration of multiplex networks with different scales. We extend it to scenarios that consider node attack cost constraints and also achieve excellent performance. This framework provides a new way to understand and employ multiplex networks.

Enhancing Edge Attack Strategy via an OWA Operator-Based Ensemble Design in Real-World Networks.

Network disintegration has been an important research hotspot in complex networks for a long time. From the perspective of node attack, researchers have devoted to this field and carried out numerous works. In contrast, the research on edge attack strategy is insufficient. This paper comprehensively evaluates the disintegration effect of each structural similarity index when they are applied to the weighted-edge attacks model. Experimental results show that the edge attack strategy based on a single similarity index will appear limited stability and adaptability. Thus, motivated by obtaining a stable disintegration effect, this paper designs an edge attack strategy based on the ordered weighted averaging (OWA) operator. Through final experimental results, we found that the edge attack strategy proposed in this paper not only achieves a more stable disintegration effect on eight real-world networks, but also significantly improves the disintegration effect when applied on a single network in comparison with the original similarity index.

Robustness of Internet of Battlefield Things (IoBT): A Directed Network Perspective.

Through the combination of various intelligent devices and the Internet to form a large-scale network, the Internet of Things (IoT) realizes real-time information exchange and communication between devices. IoT technology is expected to play an essential role in improving the combat effectiveness and situation awareness ability of armies. The interconnection between combat equipment and other battlefield resources is referred to as the Internet of Battlefield Things (IoBT). Battlefield real-time data sharing and the cooperative decision-making among commanders are highly dependent on the connectivity between different combat units in the network. However, due to the wireless characteristics of communication, a large number of communication links are directly exposed in the complex battlefield environment, and various cyber or physical attacks threaten network connectivity. Therefore, the ability to maintain network connectivity under adversary attacks is a critical property for the IoBT. In this work, we propose a directed network model and connectivity measurement of the IoBT network. Then, we develop an optimal attack strategy optimization model to simulate the optimal attack behavior of the enemy. By comparing with the disintegration effect of some benchmark strategies, we verify the optimality of the model solution and find that the robustness of the IoBT network decreases rapidly with an increase of the unidirectional communication links in the network. The results show that the adversary will change the attack mode according to the parameter settings of attack resources and network communication link density. In order to enhance the network robustness, we need to adjust the defense strategy in time to deal with this change. Finally, we validated the model and theoretical analysis proposed in this paper through experiments on a real military network.

Stackelberg game under asymmetric information in critical infrastructure system: From a complex network perspective.

Modern society is highly dependent on critical infrastructures. Since many infrastructures have network functions, it is necessary to study them from the perspective of network science. Game theory provides a suitable framework to model the confrontations in critical infrastructures. Previous models that combine network science with game theory only consider the condition of complete information. However, in the real world, complete information about the target network is not always available to the attacker. In this paper, we achieve active defense by revealing the disguised network to the attacker rather than changing the structure of the target network. We first introduce a false network generation method and investigate the transformation of the node degree in the created network. Furthermore, we propose a Stackelberg game under asymmetric information named the active deception game, in which the cost constraint is considered. Experiments based on the synthetic scale-free network indicate that disclosing false information to the attacker yields a higher equilibrium payoff than revealing complete information. We analyze the equilibrium strategies and show an interesting but counterintuitive finding that the attacker tends to choose high-degree nodes, but the defender prefers selecting low-degree nodes when the attacker does not have a global dominance strategy. Our work provides a new approach to the proactive defense of infrastructure networks using information asymmetry between attack and defense sides, which deserves further study.