Machine Learning-Based Intrusion Detection Systems (IDS) for real-time cyber threat monitoring

The continuous increase of cyberattacks in both frequency and complexity has made the security of the network environment in organizations very vital. Innovative and adaptive attacks are difficult to identify by Traditional Intrusion Detection Systems (IDS). Recent developments in the field of Machine Learning (ML) have paved the way for one such solution — an ML-based Intrusion Detection System (IDS) where anomalies within network traffic can be detected, in real-time, using data-driven algorithms. As network traffic and attack methods increase, so too should the need for a scalable and sustainable IDS that can detect both known and unknown attacks. Machine learning models provide a high level of adaptability and accuracy, which are the cornerstones of modern cybersecurity. Here, we investigate the following three commonly employed machine learning models: Logistic Regression, Gradient Boosting, and Random Forest for the intrusion detection approach. And then, the best one for being used to predict a real-time network traffic monitoring algorithm. Results: The experimental results show that Gradient Boosting and Random Forest outperform Logistic Regression with perfect accuracy, precision, recall and F1-measure. The abilities of these models to classify normal and anomalous traffic are strong and hard to break, with sturdy protection from cyber threats. Of all the different models used, Random Forest proved to be the most accurate and reliable method for real-time intrusion detection. This study reveals the promise of IDS based on machine learning for improving network security with the changing dynamics of cyberattacks.