Intrusion Detection Systems (IDS) are critical for ensuring the security of networks and computer systems. Traditional IDS methods often rely on signature-based or anomaly-based techniques that may not be adequate for detecting sophisticated or zero-day attacks. In recent years, deep learning (DL) has become a potent method for enhancing Intrusion Detection System (IDS) capabilities. This paper evaluates the leading deep learning techniques for IDS across multiple attack categories, such as Denial of Service (DoS), Probe, User to Root (U2R), and Remote to Local (R2L). We examine approaches including Convolutional Neural Networks (CNN), Recurrent Neural Networks (RNN), Long Short-Term Memory (LSTM), Autoencoders, and Hybrid models. This review highlights the strengths, limitations, and performance of each approach, offering insights into their application in detecting different attack vectors.