Network Intrusion Detection Using Machine Learning

The rapid growth of the internet and communication technologies, there has been a significant increase in network size and the volume of data transmitted. This expansion has led to the emergence of numerous novels cyberattacks, posing serious challenges to network security. Intrusion Detection Systems (IDS) play a vital role in identifying and mitigating such threats by monitoring network traffic to ensure confidentiality, integrity, and availability. Despite ongoing research efforts, IDS still face challenges such as improving detection accuracy, minimizing false alarms, and effectively identifying new types of attacks. To address these issues, recent advancements have focused on integrating machine learning (ML) techniques into IDS. ML-based IDS offer promising solutions for enhancing intrusion detection capabilities by learning from data patterns and adapting to emerging threats. As cyberattacks continue to evolve, the development of intelligent, adaptive IDS using machine learning has become essential for robust network security. IndexTerms: Machine Learning (ML), Network Intrusion Detection System (IDS), Machine Learning (ML), Random Forest, Data Preprocessing, Python, Matplotlib, Data Mining.