Adaptive statistical optimization techniques for firewall packet filtering

Packet filtering plays a critical role in the performance of many network devices such as firewalls, IPSec gateways, DiffServ and QoS routers. A tremendous amount of research was proposed to optimize packet filters. However, most of the related works use deterministic techniques and do not exploit the traffic characteristics in their optimization schemes. In addition, most packet classifiers give no specific consideration for optimizing packet rejection, which is important for many filtering devices like firewalls. Our contribution in this paper is twofold. First, we present a novel algorithm for maximizing early rejection of unwanted flows without impacting other flows significantly. Second, we present a new packet filtering optimization technique that uses adaptive statistical search trees to utilize important traffic characteristics and minimize the average packet matching time. The proposed techniques timely adapt to changes in the traffic conditions by performing simple calculations for optimizing the search data structure. Our techniques are practically attractive because they exhibit simple-to-implement and easy-to-deploy algorithms. Our extensive evaluation study using Internet traces shows that the proposed techniques can significantly minimize the packet filtering time with reasonable memory space requirements.