Comparative Evaluation of Machine Learning Techniques for Enhanced Intrusion Detection Systems Performance

Abstract

In today's rapidly evolving digital landscape, safeguarding computer systems from increasingly sophisticated and frequent cyberattacks is more critical than ever. Intrusion detection plays a pivotal role in maintaining the integrity and confidentiality of sensitive data. Leveraging cutting-edge machine learning advancements, this paper explores the development of highly efficient and responsive security systems. We rigorously evaluate various machine learning techniques for intrusion detection. The results indicate that the Random Forest Classifier stands out for its exceptional precision and speed, making it an ideal choice for real-time applications. Additionally, our research benchmarks these models against existing relevant works, demonstrating the clear superiority of our implementation across multiple performance metrics.