A Differential Game Approach to Decentralized Virus-Resistant Weight Adaptation Policy Over Complex Networks

Abstract

Increasing connectivity of communication networks enables large-scale distributed processing over networks and improves the efficiency for information exchange. However, malware and virus can take advantage of the high connectivity to spread over the network and take control of devices and servers for illicit purposes. In this paper, we use an SIS epidemic model to capture the virus spreading process and develop a virus-resistant weight adaptation scheme to mitigate the spreading over the network. We propose a differential game framework to provide a theoretic underpinning for decentralized mitigation in which nodes of the network cannot fully coordinate, and each node determines its own control policy based on local interactions with neighboring nodes. We characterize and examine the structure of the Nash equilibrium, and discuss the inefficiency of the Nash equilibrium in terms of minimizing the total cost of the whole network. A mechanism design through a penalty scheme is proposed to reduce the inefficiency of the Nash equilibrium and allow the decentralized policy to achieve social welfare for the whole network. We corroborate our results using numerical experiments and show that virus-resistance can be achieved by a distributed weight adaptation scheme.

Predicting Spread Probability of Learning‐Effect Computer Virus

“…In realistic industrial processes, the operation time is reduced because the workers' skill or the flow process improves steadily, and this phenomenon is also called the learning effect. In many real-world applications [7,28,29,40,[43][44][45][46][47][48][49], the learning effect is pragmatic. Hence, the learning effect is introduced in this work to study the proposed computer-virus spread area prediction problem and offers a defense mechanism (antivirus protection) in the scale-free model to prevent or slow down virus propagation.…”

“…In the past five years, research on computer viruses has focused on defense against viruses [27][28][29]. e research on computer viruses can be classified into the following major research directions: the description of the computer virus, the detection of computer viruses, and protection against computer viruses:…”

Modelling and Analysis of the Epidemic Model under Pulse Charging in Wireless Rechargeable Sensor Networks

“…Since wireless links are used for data transmission among nodes, it is difficult to construct complex protection mechanisms. In order to curb the spread of malware among nodes, a research team has proposed the weight adaptation scheme [1]. The weight adaptation scheme can block the transmission of malware by reducing the transmission efficiency among nodes.…”

Optimal Network Defense Strategy Selection Method: A Stochastic Differential Game Model

“…As a theoretical method to study conflict confrontations and decision-making processes in continuous time [18], differential game can model and analyze the rapid and continuous change of the network attack-defense process. Huang and Zhu [19] used a differential game framework to analyze antivirus mechanisms on complex networks and proposed a distributed antivirus algorithm to minimize the overall network cost. M. Li and S. Li [20] established a differential game model in wireless sensor networks, studied network IP defense problems, and proposed an optimal defense strategy based on limited resources.…”