Examining the impact of Artificial Intelligence methods on Intrusion Detection with the NSL-KDD dataset

This paper examines the prominent and challenging concern of ensuring Cybersecurity in contemporary information technology (IT) infrastructures. The rapid advancement of technology has given rise to a new breed of hackers who are constantly developing sophisticated and dangerous malware attacks. Consequently, the task of detecting and preventing these intrusions has become increasingly difficult. This study examines the challenges faced by current standard analytic techniques in detecting and mitigating potential dangers in the given setting. This paper presents a research study on the development of an intelligent intrusion detection system (IDS) using statistical analysis and autoencoder (AE) techniques. This paper proposes an Intrusion Detection System (IDS) that leverages data analytics, statistical methods, and advancements in artificial intelligence (AI) to extract enhanced and interconnected features. This research paper evaluates the validity of the suggested Intrusion Detection Systems (IDS) using the NSL-KDD database as a benchmark.