Open Access

ARTICLE

Probe Attack Detection Using an Improved Intrusion Detection System

Abdulaziz Almazyad, Laila Halman, Alaa Alsaeed^*

Department of Computer Engineering, College of Computer Science, King Saud University, Riyadh, 11421, Saudi Arabia

* Corresponding Author: Alaa Alsaeed. Email:

Computers, Materials & Continua 2023, 74(3), 4769-4784. https://doi.org/10.32604/cmc.2023.033382

Received 15 June 2022; Accepted 15 September 2022; Issue published 28 December 2022

Abstract

The novel Software Defined Networking (SDN) architecture potentially resolves specific challenges arising from rapid internet growth of and the static nature of conventional networks to manage organizational business requirements with distinctive features. Nevertheless, such benefits lead to a more adverse environment entailing network breakdown, systems paralysis, and online banking fraudulence and robbery. As one of the most common and dangerous threats in SDN, probe attack occurs when the attacker scans SDN devices to collect the necessary knowledge on system susceptibilities, which is then manipulated to undermine the entire system. Precision, high performance, and real-time systems prove pivotal in successful goal attainment through feature selection to minimize computation time, optimize prediction performance, and provide a holistic understanding of machine learning data. As the extension of astute machine learning algorithms into an Intrusion Detection System (IDS) through SDN has garnered much scholarly attention within the past decade, this study recommended an effective IDS under the Grey-wolf optimizer (GWO) and Light Gradient Boosting Machine (LightGBM) classifier for probe attack identification. The InSDN dataset was employed to train and test the proposed IDS, which is deemed to be a novel benchmarking dataset in SDN. The proposed IDS assessment demonstrated an optimized performance against that of peer IDSs in probe attack detection within SDN. The results revealed that the proposed IDS outperforms the state-of-the-art IDSs, as it achieved 99.8% accuracy, 99.7% recall, 99.99% precision, and 99.8% F-measure.

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Keywords

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