On optimizing firewall performance in dynamic networks by invoking a novel swapping window–based paradigm

Abstract

SummaryDesigning and implementing efficient firewall strategies in the age of the internet of things is far from trivial. This is because, as time proceeds, an increasing number of devices will be connected, accessed, and controlled on the internet. Additionally, an ever‐increasingly amount of sensitive information will be stored on various networks. A good and efficient firewall strategy will attempt to secure this information and to also manage the large amount of inevitable network traffic that these devices create. The goal of this paper is to propose a framework for designing optimized firewalls for the internet of things. This paper deals with 2 fundamental challenges/problems encountered in such firewalls. The first problem is associated with the so‐called rule matching time problem. Here, we propose a simple condition for performing the swapping of the firewall's rules; using which, we can guarantee the firewall's consistency and integrity and also ensure a greedy reduction in the matching time. Unlike the state of the art, our swapping condition considers rules that are not necessarily consecutive, using a novel concept referred to as a “swapping window.” The second contribution of our paper is a novel “batch”‐based traffic estimator that provides network statistics to the firewall placement optimizer. The traffic estimator is a subtle but modified batch‐based embodiment of the Stochastic Learning Weak Estimator. Further, by performing a rigorous suite of experiments, we demonstrate that both algorithms are capable of optimizing the constraints imposed for obtaining an efficient firewall.