Machine Learning-Driven Network Intrusion Detection: An Intelligent Approach

Abstract

With the exponential growth of digitalization and data volumes, the cybersecurity threat landscape has become increasingly complex, amplifying the need for robust intrusion detection systems (IDS). Traditional IDS approaches often struggle with static architectures, requiring costly and frequent retraining to keep up with evolving threats. This study introduces an incremental, majority-voting IDS system that leverages machine learning to adapt to continuous network traffic streams without the need for extensive retraining. By integrating multiple machine learning algorithms—K-Nearest Neighbors (KNN), Logistic Regression, Bernoulli Naive Bayes, and Decision Tree classifiers—the system employs a collective decision-making approach to enhance detection accuracy and minimize false alarms in real-time. Results indicate that this multi-algorithm IDS framework offers substantial improvements in adaptability, performance, and resilience against intrusions, especially within real-world, imbalanced data scenarios.