- Paper2.pdf (494k)
International Journal of Communication Systems;Volume 31, Issue 15 October 2018
Designing and implementing efﬁcient ﬁrewall strategies in the age of the Internet of Things (IoT) 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 everincreasingly 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 IoT. This paper deals with two fundamental challenges/problems encountered in such ﬁrewalls. The ﬁrst problem is associated with the so-called “Rule Matching” (RM) time problem. Here, we propose a simple condition for performing the swapping of the ﬁrewall’s rules, using which, we can guarantee that 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 (SLWE). 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.
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