Machine Learning based Enhanced Intrusion Detection for Cybersecurity

Modern cyber security relies heavily on intrusion detection systems or IDS. However there are a number of issues with traditional IDS techniques such as high false positive rates, issues with scalability and trouble identifying zero-day attacks. IDS performance has increased due to recent developments in machine learning (ML); however problems like imbalanced datasets and processing overhead still exist. This article proposes an ML-based intrusion detection system (IDS) that makes use of Extra Trees Classifier (ETC), Gaussian Naive Bayes, and AdaBoost to improve detection accuracy. The usefulness of the suggested model is demonstrated by comparison with current techniques which emphasize its better classification performance resilience in real-time intrusion detection scenarios and versatility in managing a variety cyberthreats.