Jul 2026· 2026 4th International Conference on Sustainable Computing and Smart Systems (ICSCSS)· pp. 891-896· 0 citations· 20 references
Abstract
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.
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.
R. Velu· 2026 4th International Confe...· 0 citations
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
An Advanced Machine Learning Model for Anticipating and Preventing Cyber Attacks Using Random Forest is presented, which effectively detects cyber threats with high accuracy and reliability, thereby improving threat anticipation and reducing security risks.
T Pushpalatha and RP Rajeshwari· International Journal of Adv...· 0 citations
A thorough analysis of a modest version of a suggested system that use Support Vector Machines (SVM) to address networking anomaly and misuse detection in the face of insurmountable obstacles, foreseeing an all-encompassing solution to modern network security issues.
Gaurav Kishor Saxena, Shambhu Dayal Sahu· International Journal of Cre...· 0 citations
: As the internet usage is exponentially increasing and with the emerging cyber threats, the conventional rule-based intrusion detection systems (IDS) are limited to identifying new and advanced attacks. The paper features an artificial intelligence-powered Intrusion Detection System (IDS) utilizing the machine learning methods to detect the malicious actions in the network traffic automatically. The system based on the CICIDS datasets, data preprocessing, feature selection, and a Random Forest classifier was used to determine various types of intrusions with a high success rate. The capability of the system to classify intrusions, generate confidence scores and be interpretable with feature importance analysis is evidenced by real-time packet-level simulation. Besides, the model is combined with a notification system that will notify through email or SMS in case of intrusions, so that timely response and mitigation is realized. The suggested framework does not only improve the detection performance, it also brings in scalability, automation, and transparency, which is appropriate in the present day network setup.
S. S, Martin Victor· Proceedings of the 1st Inter...· 0 citations
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