Intrusion Detection Using Artificial Intelligence Techniques: Review

The rising frequency of cyberattacks emphasizes the vital necessity for resilient Intrusion Detection Systems (IDS) to safeguard computer networks, focusing on the application of artificial intelligence approaches. This study delivers a comprehensive overview of intrusion detection methodologies. The review includes a thorough examination of current research articles that present innovative intrusion detection methods. The realm of intrusion detection delves into diverse artificial intelligence approaches, encompassing ensemble learning, meta-learning, deep learning algorithms, and traditional machine learning techniques. One of the primary accomplishments assessed in the articles is the effective utilization of deep learning techniques, including CNN, LSTM, and DNN, resulting in remarkable detection accuracies. The effectiveness of ensemble fusion and feature-level aggregation in boosting the prediction potential of machine-learning models is demonstrated by the novel meta-learning system INFUSE. The adaptability of intrusion detection models is further demonstrated by the application of attention mechanisms, adaptive voting algorithms, and ensemble methodologies. Even though these approaches have shown promising results, some drawbacks are noted, including difficulties with real-world implementation, the requirement for better feature selection, and inadequacies in comprehensive evaluations.