Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 385-392· 0 citations· 12 references
Abstract
With the widespread adoption of cloud computing, securing enterprise networks against cyber threats has become increasingly important. Cloud environments are highly dynamic and constantly changing, making them susceptible to sophisticated cyberattacks that traditional Intrusion Detection Systems (IDS) often fail to detect. This study focuses on Intelligent Intrusion Detection Systems (IIDS) and their critical role in strengthening cloud security. Unlike conventional signature-based IDS that rely on fixed attack patterns, IIDS employ advanced Machine Learning (ML) and Artificial Intelligence (AI) techniques including deep learning, decision trees, and ensemble models to identify both known and emerging threats with greater accuracy. The paper proposes an integrated framework that combines real-time anomaly detection with automated response capabilities for cloud networks. Key architectural elements of IIDS are examined, alongside major deployment challenges such as scalability, false-positive rates, and computational requirements. Additionally, practical case studies and performance evaluations illustrate how IIDS enhance threat detection by improving accuracy, adaptability, and efficiency. Finally, the paper outlines future research directions to further advance IIDS capabilities and address the evolving security needs of modern cloud infrastructures.
In Cloud Computing, Artificial Intelligence (AI)-driven intrusion detection focuses on identifying anomalies, unauthorized access, and malicious activities across dynamic cloud environments. Besides, the Intrusion Detection System (IDS) is also crucial in strengthening the overall cybersecurity defenses, thereby assisting institutions or organizations in detecting malicious activities to improve their security and preserve sensitive data. Conventional security approaches often fail to handle constantly evolving attack patterns in the cloud. Prevailing signature-based schemes do not have the ability to identify unknown threats, thereby generating false alarms. Besides, they require large labelled databases for adapting to changing workloads. This survey examines several intrusion detection approaches in the Cloud Computing Environment. The methods are categorized as Machine Learning (ML), Federated Learning (FL), Deep Learning (DL), and Big Databased models. Further, to provide a comprehensive assessment, 25 research papers on intrusion detection are collected and reviewed. Further, a general outline for detecting intrusions is explained, and then the literature review of each technique with its pros and cons is elaborated. The research gaps that are encountered by the existing techniques are also presented. In addition, this survey also highlights the analysis on the basis of various factors, like publication year, methodology, tools, indicators, and databases utilized.
P. Raja, J. Sathiamoorthy· 2026 4th International Confe...· 0 citations
This review presents a comprehensive analysis of machine learning-based intrusion detection systems, covering a wide range of techniques including supervised learning, unsupervised learning, ensemble learning, and deep learning models, and discusses critical challenges affecting the deployment of ML-based IDS.
Ranobir Hasan, H. Jamal, Kamal Kamal et al.· The Eastasouth Journal of In...· 0 citations
Highly accurate systems for detecting threats in real time are needed urgently owing to the exponential growth in cloud-network systems and increasingly sophisticated attacks. The conventional security systems using rules and signatures are inadequate in the changing environment of cloud computing because of evolving attacks.The suggested framework represents an intelligent solution for detecting and classifying threats in cloud computing by using smart machine learning algorithms. An intelligent system will collect data related to cloud network traffic and extract the features, and then it will use the supervisory learning algorithm to classify the threats. The experimental assessment has been performed based on a cloud intrusion detection dataset that consists of various types of attacks including network intrusion, malware, phishing, and data exfiltration. The implemented model had a total classification accuracy of 99.98% that proved to be very reliable with regard to detection of threats in which there are few false positives as well as false negatives. The findings confirm the assertion that the proposed framework offers real-time, scalable and effective security protection that is applicable in contemporary cloud-networks.
Pallapati Solmon, Shaik Khuran Bi, Yerram Lokeshreddy et al.· 2026 4th International Confe...· 0 citations
The findings confirm that the proposed IDSaaS framework provides an efficient, scalable, and adaptive solution for real-time cloud intrusion detection and significantly enhances the reliability and resilience of modern cloud and industrial cybersecurity infrastructures.
Unik B. Lokhande, Kavita Sonawane· Journal of Cloud Computing· 0 citations
This study examines the application of artificial intelligence-powered intrusion detection systems that leverage deep learning architectures and anomaly detection methodologies to identify malicious activities within dynamic network environments and concludes that the convergence of deep learning methodologies and anomaly detection techniques provides a robust foundation for next-generation intrusion detection systems.
M. A. Gandhi, Dinesh Baban Kute, U. Hemavathi· International journal of com...· 0 citations
The rapid growth of heterogeneous network environments such as the Internet of Things (IoT), Industrial IoT (IIoT), cloud computing, and software‐defined networks has significantly increased exposure to sophisticated cyberattacks, making intrusion detection a critical component of modern cybersecurity infrastructures. Traditional intrusion detection systems and conventional machine learning techniques often face limitations when handling high‐dimensional network traffic, class imbalance, and evolving attack patterns, resulting in reduced detection performance and limited scalability under complex network environments. These challenges reduce their effectiveness in practical, large‐scale deployments. To overcome these issues, this paper proposes a hybrid intrusion detection framework based on an Autoencoder and a TabTransformer, optimized using the Whale Optimization Algorithm (WOA). The Autoencoder is employed to perform unsupervised feature learning, transforming high‐dimensional network traffic data into compact and noise‐resistant latent representations. These latent features are then processed by the TabTransformer, which utilizes multi‐head self‐attention to capture complex inter‐feature relationships and enhance classification performance. The WOA is incorporated to automatically optimize key hyperparameters, improving convergence speed, stability, and generalization capability of the model. The proposed framework is primarily evaluated using the CIC‐IDS2018 benchmark dataset. In addition, supplementary cross‐dataset validation on the CIC‐IDS2017 and UNSW‐NB15 datasets is conducted to assess the generalization capability of the proposed framework. Experimental results demonstrate that the proposed model achieves an accuracy of 99.87%, precision of 99.85%, recall of 99.88%, and an F1‐score of 99.86% while maintaining very low false alarm and false negative rates. Comparative analysis with existing deep learning‐based intrusion detection approaches confirms the superior and balanced performance of the proposed method. Overall, the Hybrid Autoencoder–TabTransformer framework provides an effective intrusion detection solution that demonstrates strong performance under the evaluated experimental conditions.
Rui Guo, Guangjun Wen· Transactions on Emerging Tel...· 0 citations
