| dc.description.abstract | 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 ever-increasingly amount of sensitive information
will be stored on various networks. A good and effi-
cient 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.
In this regard, we propose a simple condition for performing
the swapping of the firewall’s rules, and by satisfying
this condition, we can guarantee that apart from preserving
the firewall’s consistency and integrity, we can also
ensure a greedy reduction in the matching time. It turns out
that though our proposed novel solution is relatively simple,
it can be perceived to be a generalization of the algorithm
proposed by Fulp [1]. However, as opposed to Fulp’s solution,
our swapping condition considers rules that are not necessarily
consecutive. It rather invokes a novel concept that
we refer to as the “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) proposed by Oommen and
Rueda [2].
The paper contains the formal properties of this 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 | language |
