Abstract
Nowadays, the cybersecurity issue involves new strategies to protect against advanced threats and unknown attacks. Intrusion detection system (IDS) is considered a robust system dealing with attacks detection, particularly unknown attacks and anomalies. Several IDS-based algorithms have been recently inspected in the literature, among them the well-known strengthen algorithms, i.e. Genetic algorithm (GA). Moreover, Epigenetic-based algorithm (EGA) is known as an improved version of GA ensuring high performance with reduced computational complexity. Its main goal is to converge within a short time towards an optimal solution by acting on genetic operators, namely mutation and crossover. In this article, we propose a new classifier based on EGA for IDS. Especially, based on a database of network traffics, EGA is applied to classify attacks. The results, performed through EGA simulation, show that the performance of the proposed technique outperforms the ones of GA classifier by obtaining a high detection rate up to 98% and a faster processing time than that of GA and other algorithms that we have compared in this article.
Highlights
I NTRUSION Detection Systems (IDS) is one of the main techniques used to ensure security in a network or computing environment
AND DISCUSSIONS we investigate the performance of the proposed IDS Epigenetic Algorithm (EGA)-based algorithm for various parameters to achieve the best classifier
An EGA-based detection technique for arbitrary attacks was presented and optimized. This EGA algorithm was applied to the IDS using the KDD-NSL dataset
Summary
I NTRUSION Detection Systems (IDS) is one of the main techniques used to ensure security in a network or computing environment. While signaturebased IDS technique matches the presented attack’s signature with a database of known attacks [5], the anomaly-based IDS can effectively identify unknown attacks whose signatures do not exist in database, by learning about certain normal behaviors in the network. To this end, it raises alerts or block traffics once an abnormal behavior in the network is detected [5]. EGA have attracted researchers’ attention and shown their effectiveness in solving some problems such as GSM mobile planning frequency [14] and Inverse Kinematics problem [15] It relies on the control of the randomness of gene activities. Such a list can be built relying on one of the feature selection methods such as correlation features selection (CFS) [18] [19], InfoGain [20], or gain ratio (GR) [21]
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