Biblio

Cyberspace is vulnerable to continuous malicious attacks. Traceability of network attacks is an effective defense means to curb and counter network attacks. In this paper, the evolutionary game model is used to analyze the network attack and defense behavior. On the basis of the quantification of attack and defense benefits, the replication dynamic learning mechanism is used to describe the change process of the selection probability of attack and defense strategies, and finally the evolutionary stability strategies and their solution curves of both sides are obtained. On this basis, the attack behavior is analyzed, and the probability curve of attack strategy and the optimal attack strategy are obtained, so as to realize the effective traceability of attack behavior.

The threat of DDOS and other cyberattacks has increased during the last decade. In addition to the radical increase in the number of attacks, they are also becoming more sophisticated with the targets ranging from ordinary users to service providers and even critical infrastructure. According to some resources, the sophistication of attacks is increasing faster than the mitigating actions against them. For example determining the location of the attack origin is becoming impossible as cyber attackers employ specific means to evade detection of the attack origin by default, such as using proxy services and source address spoofing. The purpose of this paper is to initiate discussion about effective Internet Protocol traceback mechanisms that are needed to overcome this problem. We propose an approach for traceback that is based on extensive use of security metrics before (proactive) and during (reactive) the attacks.