A Review of Anomaly Based Multiple Intrusion Detection Methods Using a Feature Based Deep Learning Approach

The purpose of this review is to look at cutting-edge techniques in anomaly-based intrusion detection systems that use a feature-based deep learning approach. A thorough literature review was carried out by collecting and analyzing relevant research papers published in the previous decade. The review focuses on various types of deep learning algorithms and how they can be used to detect network anomalies. The study discovered that the feature-based deep learning approach was highly effective in detecting network anomalies. It has also been discovered that the most popular techniques used in anomaly-based intrusion detection systems are Convolutional Neural Network and Autoencoder-based Deep Learning Models. Furthermore, the review emphasized the significance of selecting appropriate features and extraction techniques to achieve optimal performance in deep learning-based intrusion detection systems. Overall, this review provides useful insights into current trends and challenges in anomaly-based intrusion detection systems employing deep learning techniques.