AI-Driven Adaptive Intrusion Detection Framework for Cloud Environments Using Ensemble Learning and Behavioral Traffic Analytics

Sultan saleem A; Eazhlmahan E R; Hariprasath A S; Aniruth A; Deepika B

doi:10.36948/ijfmr.2026.v08i02.72399

AI-Driven Adaptive Intrusion Detection Framework for Cloud Environments Using Ensemble Learning and Behavioral Traffic Analytics

Author(s)	Mr. Sultan saleem A, Eazhlmahan E R, Hariprasath A S, Aniruth A, Deepika B
Country	India
Abstract	The paper is an AI-based intrusion detection system (IDS) targeting a cloud computing system that is getting more vulnerable to more sophisticated cyber attacks, such as malware, denial-of-service attacks, and insider attacks that hinder the efficacy of conventional security systems in protection. The framework proposed combines machine learning based traffic analysis, behavioural modeling, ensemble learning, and adaptive retraining to detect not only known but also zero-day intrusions with a better set of reliability. Real time processing of network traffic, user activity patterns, and system logs are also done so that the IDS can identify abnormal deviations that are possible signs of malicious activities as well as minimize false alarms. Experiments on benchmark datasets and a MATLAB-type cloud infrastructure performance have proven good classification, steady behavior under detection, as well as being guarded against changing attack vectors. The continuous learning in the system is enhanced by the adaptive architecture which is appropriate in the dynamic cloud environment.
Keywords	AI-based Intrusion Detection System (IDS), Cloud Computing Security, Machine Learning Traffic Analysis, Behavioral Modeling, Ensemble Learning, Adaptive Retraining, Zero-Day Intrusions, Real-Time Network Traffic Processing, User Activity Patterns, System Logs Analysis, Anomaly Detection, False Alarm Minimization, Benchmark Datasets Evaluation, MATLAB Cloud Simulation, Continuous Learning Architecture, Dynamic Attack Vectors.
Field	Engineering
Published In	Volume 8, Issue 2, March-April 2026
Published On	2026-04-03
DOI	https://doi.org/10.36948/ijfmr.2026.v08i02.72399

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AI-Driven Adaptive Intrusion Detection Framework for Cloud Environments Using Ensemble Learning and Behavioral Traffic Analytics

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