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IDSCI: An Advanced Machine Learning-Based Intrusion Detection System for Cloud Infrastructure
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Author(s): Arvind Mewada (Bennett University, India), Mohd Aquib Ansari (Galgotias University, India), Shahnawaz Ahmad (Bennett University, India), Sushil Kumar Maurya (Siksha 'O' Anushandhan, India)and Lalit Kumar (MCAET, India)
Copyright: 2026
Pages: 24
Source title:
Safe Data-Driven Control for Cyber-Physical Systems
Source Author(s)/Editor(s): Adnène Arbi (National Institute of Applied Sciences and Technology, University of Carthage, Tunisia & Laboratory of Mathematical Engineering, Tunisia Polytechnic School, University of Carthage, Tunisia)
DOI: 10.4018/979-8-3373-1832-5.ch009
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Abstract
The increase in cloud usage has created intricate security problems, highlighting a higher need for preventative measures against security breaches. Classic intrusion detection systems (IDS) are challenged in cloud settings because of the environment's moving and scattered nature, which leads to great rates of incorrect identification and poor optimisation. This research presents an IDSCI capable of deploying an advanced IDS, which IDSCI achieves by applying a hybrid deep learning model that uses convolutional neural networks (CNN) with bidirectional long short-term memory (BiLSTM). The model improves detection accuracy by using robust cloud support for intrusion detection optimisation, guaranteeing strong protection of the cloud-based infrastructure. IDSCI has effectively achieved 98.5% detection accuracy with NSL-KDD and CICIDS2017 datasets, considerably better than traditional IDS systems. These findings illustrate this system in increasing the effectiveness of cloud security through higher detection rates, lower misuse alarms, and real-time counteractive action against threats.
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