An Automated Ensemble Intrusion Detection System with Adaptive Response for Cloud Security

Abstract

Cybersecurity has become a critical concern due to the exponential growth of computer networks and the increasing number of connected devices. Traditional security mechanisms such as firewalls, authentication protocols, and encryption are insufficient to protect against sophisticated cyber threats, particularly internal attacks that evade perimeter defenses. This research presents an Automated Ensemble Intrusion Detection System (AE-IDS) for cloud environments that combines multiple machine learning classifiers—Random Forest, Decision Tree, Bayesian Network, Naïve Bayes, and Artificial Neural Network—through an ensemble architecture to improve detection accuracy. The system incorporates an automated response module that initiates predefined mitigation actions upon intrusion detection, including alerting administrators, isolating compromised resources, blocking suspicious traffic, and applying adaptive security policies. The proposed system was evaluated on the KDD'99 cup dataset, containing 4,898,431 instances with 41 attributes across four attack categories: Denial of Service (DoS), Remote to Local (R2L), User to Root (U2R), and Probe. Experimental results demonstrate that the ensemble approach achieves 98.7% accuracy, outperforming individual classifiers by 3-12%. The automated response module achieved an average mitigation time of 156ms, demonstrating its suitability for real-time cloud deployment.