Implement Edge pruning to Enhance attack graph generation using Naïve approach algorithm

Abstract

The use of network technologies has increased in recent years. Although the network is beneficial for individuals to work and live in, it does have security challenges that should be rectified. One of these issues is cyberattacks. The attack surface for hackers is growing as more devices are linked to the internet. The next-generation cyber defense concentrating on predictive analysis seems more proactive than existing technologies based on intrusion detection. Recently, many approaches have been proposed to detect and predict attacks; one of these approaches is attack graphs. The main reason for designing the attack graph is to predict the attack as well as to predict the attack's next step in the network. The attack graph depicts the many paths an attacker may attempt to get around a security policy by leveraging interdependencies between disclosed vulnerabilities. The attack graph is categorized into three sections: generation, analysis, and use of attack graph. However, current attack graphs are suffering from a few issues. Scalability is the main issue the attack graph generation is facing. The reason for this issue is that the increase in the usage of devices connected to the network leads to increased vulnerabilities in the network, which leads to an increment in the complexity as well as generation time of the attack graph. For this issue, this study proposes use the naïve approach prune algorithm and using Personal agents to reduce the reachability time in calculating between the nodes and to remove unnecessary edges, minimizing the attack graph's complexity. For the results, the proposed attack graph performs better than the existing attack graph by using a naïve approach and a personal agent. The proposed attack graph reduced the generation time by 20% and the attack graph complexity.

Keywords

• Attack Graph
• Fog computing
• Cloud computing
• Edge Computing
• Edge Pruning

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