Citation: Aldallal, A. Toward Efficient Intrusion Detection System Using Hybrid Deep Learning Approach. Symmetry 2022, 14, 1916. https://doi.org/10.3390/ sym14091916 Academic Editors: Lorentz Jäntschi and Jan Awrejcewicz Received: 27 June 2022 Accepted: 6 September 2022 Published: 13 September 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affil- iations. Copyright: © 2022 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). symmetry S S Article Toward Efficient Intrusion Detection System Using Hybrid Deep Learning Approach Ammar Aldallal Telecommunication Engineering Department, Ahlia University, Manama P.O. Box 10878, Bahrain; aaldallal@ahlia.edu.bh Abstract: The increased adoption of cloud computing resources produces major loopholes in cloud computing for cybersecurity attacks. An intrusion detection system (IDS) is one of the vital defenses against threats and attacks to cloud computing. Current IDSs encounter two challenges, namely, low accuracy and a high false alarm rate. Due to these challenges, additional efforts are required by network experts to respond to abnormal traffic alerts. To improve IDS efficiency in detecting abnormal network traffic, this work develops an IDS using a recurrent neural network based on gated recurrent units (GRUs) and improved long short-term memory (LSTM) through a computing unit to form Cu-LSTMGRU. The proposed system efficiently classifies the network flow instances as benign or malevolent. This system is examined using the most up-to-date dataset CICIDS2018. To further optimize computational complexity, the dataset is optimized through the Pearson correlation feature selection algorithm. The proposed model is evaluated using several metrics. The results show that the proposed model remarkably outperforms benchmarks by up to 12.045%. Therefore, the Cu-LSTMGRU model provides a high level of symmetry between cloud computing security and the detection of intrusions and malicious attacks. Keywords: intrusion detection system; deep learning; LSTM; GRU; RNN; feature selection; Pearson correlation 1. Introduction The ability to enact cloud-based threats and attacks has enabled a high-quality strategy for cyber intruders, attackers, and hackers worldwide, meaning that they can drastically affect the quality of the cloud environment. Cloud computing is vulnerable to several types of attacks. These include data loss, data breaches, insecure interfaces and APIs, malicious insiders, unknown risk profiles, and identity theft [1]. Cloud-based threats, such as DoS/DDoS, can rapidly deactivate a victim and initiate huge income losses. Regardless of the huge presence of available traditional solutions for threat detection, there remains significant and continuous growth in threats and attacks, with an extended volume and crit- icality. In cybersecurity, an intruder is an entity that seeks to exploit system vulnerabilities. Intrusion can be detected using signature-based or anomaly-based techniques. Outdated signature-based intrusion detection systems cannot respond to novel attacks, whereas the anomaly-based technique, which compares user patterns against known patterns, suffers from a high false positive rate of detection. However, this can be solved using an effective classification method. In many cases, it is not viable to test the efficiency of the developed IDS on a live dataset; hence, a predefined dataset that consists of real-time network traffic is used to examine IDS performance. The most well-known dataset of this kind is the KDD CUP 99 dataset, which has been considered by many researchers [1–4]. The optimized version of it is the NSL-KDD dataset, which has been employed by [5–11], among others. However, these datasets are vulnerable to a few types of attacks. In addition, these two datasets suffer from a limited number of features, which makes them unreliable when it comes to testing an IDS with new and emerging security threats and strategies used by Symmetry 2022, 14, 1916. https://doi.org/10.3390/sym14091916 https://www.mdpi.com/journal/symmetry