MACHINE LEARNING TECHNIQUES APPLIED TO INTRUSION DETECTION SYSTEMS

Abstract

Cybersecurity threats have evolved dramatically in sophistication and frequency, rendering traditional signature-based intrusion detection systems increasingly ineffective against novel and zero-day attacks. Machine learning techniques offer promising solutions by enabling systems to learn normal network behavior patterns and identify anomalous activities that may indicate intrusions. This research comprehensively examines the application of machine learning algorithms to intrusion detection systems, analyzing supervised, unsupervised, and hybrid approaches across various network environments. We evaluate classical algorithms including decision trees, support vector machines, and naive Bayes alongside advanced techniques such as deep learning, ensemble methods, and reinforcement learning. Performance analysis using standard datasets including NSL-KDD, CICIDS2017, and UNSW-NB15 reveals that ensemble methods combining multiple algorithms achieve superior detection rates of 96-98% with false positive rates below 2%, outperforming individual classifiers by 8-12%. Deep learning approaches, particularly convolutional and recurrent neural networks, demonstrate exceptional capability in detecting complex attack patterns with 97.3% accuracy, though requiring substantial computational resources and training time. However, significant challenges persist including imbalanced datasets where attack samples comprise only 0.1-5% of traffic, concept drift as attack methodologies evolve, adversarial attacks targeting machine learning models themselves, and interpretability concerns where black-box models provide limited insight into detection reasoning. The research reveals that no single machine learning technique dominates across all metrics—supervised methods excel at detecting known attack types, unsupervised approaches identify novel threats, and hybrid systems balance both capabilities. Practical deployment considerations including real-time processing requirements, computational constraints, and integration with existing security infrastructure significantly influence algorithm selection. This work provides evidence-based guidance for security practitioners selecting appropriate machine learning techniques for intrusion detection based on specific operational requirements, threat landscapes, and resource availability.