An Adaptive and Performance-Aware Firewall Rule Reconfiguration Mechanism for Network Traffic Patterns
As the modern network infrastructure continue to become more and more complex, massive set of firewall rules have been produced, which causes large package classification latency and poor resource utilization. Firewall rule ordering is an NP-hard mathematical programming problem in which the wrong sequencing of firewall rules maximizes the cost of matching, as well as presents the possibility of policy violation. The current meta-heuristic algorithms are said to be constrained by predetermined traffic patterns and unresponsive to real time. In this paper, an adaptive Reinforcement Learning (RL) framework is introduced to the dynamic optimization of firewall rules. The issue is presented as a Markov Decision Process (MDP) and Deep Q-Network (DQN) agent can observe the frequency of rule hits and dependency restrictions and propose the best reorganization. In order to solve the scalability issue, A state-space dimensionality reduction algorithm is adopted, where we target high-traffic rule sets. The Directed Acyclic Graph (DAG) is used to make sure that the reordering of rules does not compromise the semantics of security policies. The experimental results prove that the throughput is increased and the cost of rule comparisons are decreased as compared to fixed configurations. Our framework is better in changing traffic dynamics.