INTELLIGENT NETWORK INTRUSION DETECTION SYSTEM USING MACHINE LEARNING AND MLOPS

Abstract

The fast development of digital communication and internet services poses a great threat to computer networks in terms of possible cyber-attacks and unauthorized access. The traditional security measures are not able to detect attacks promptly and effectively. This project introduces “An Intelligent Network Intrusion Detection System Based on Machine Learning and MLOps” that can detect cyberattacks and other malicious network operations quickly and automatically using a MLOps workflow. In particular, Machine Learning (ML) will be used to create an efficient intrusion detection system. For this purpose, network traffic data sets that include several kinds of attacks like Denial of Service (DoS) attacks, probes, brute force attacks, unauthorized access, and others will be used. The data will be preprocessed, transformed, validated, and engineered. Several supervised machine learning methods are employed in intrusion detection, such as the Random Forest algorithm, Decision Tree algorithm, Support Vector Machine algorithm (SVM), and the K-Nearest Neighbors (KNN) algorithm. The training and evaluation of the algorithms are done based on their performances, which include accuracy, precision, recall, F1 score, and confusion matrix. The most effective one is chosen and deployed via an automated MLOps pipeline. The MLOps structure ensures continuous integration, continuous training, continuous deployment, and model monitoring. Features like ETL pipelines, data ingestion, model versioning, experiment tracking, automatic deployment, and monitoring help ensure that the process scales, becomes repeatable, and remains easy to maintain. The proposed solution can also detect intrusions in real-time and minimize manual effort required when updating the model. In conclusion, the implemented solution constitutes an advanced adaptive system that is intelligent and able to detect both known and unknown types of attacks with higher levels of accuracy while reducing false positives. The current project proves that the implementation of Machine Learning and MLOps concepts can lead to improvements in the field of modern network security systems.