Ant Colony Optimization based approach for efficient packet filtering in firewall
A firewall is a security guard placed at the point of entry between a private network and the outside network. The function of a firewall is to accept or discard the incoming packets passing through it based on the rules in a ruleset. Approaches employing Neural networks for packet filtering in firewall and packet classification using 2D filters have been proposed in the literature. These approaches suffer from the drawbacks of acceptance of packets from the IP address or ports not specified in the firewall rule set and a restricted search in the face of multiple occurrences of the same IP address or ports respectively. In this paper we propose an Ant Colony Optimization (ACO) based approach for packet filtering in the firewall rule set. Termed Ant Colony Optimization Packet Filtering algorithm (ACO-PF), the scheme unlike its predecessors, considers all multiple occurrences of the same IP address or ports in the firewall rule set during its search process. The other parameters of the rule matching with the compared IP address or ports in the firewall ruleset are retrieved and the firewall decides whether the packet has to be accepted or rejected. Also this scheme has a search space lesser than that of binary search in a worst case scenario. It also strictly filters the packets according to the filter rules in the firewall rule set. It is shown that ACO-PF performs well when compared to other existing packet filtering methods. Experimental results comparing the performance of the ACO-PF scheme with the binary search scheme, sequential search scheme and neural network based approaches are presented.
ant colony optimization packet filtering firewall neural network binary search sequential search