Classification of Abnormal Patterns in Traffic Analysis Based on a Fusion Approach

Optical signal processing plays a crucial role in Intrusion Detection System (IDS) for optical networks, as optical techniques provide more bandwidth and more security for the communication channel. It involves various techniques and operations performed on the optical signals to extract relevant information and identify potential security threats or intrusions. The IDS is a very important component which detect unauthorized access to computer networks and systems. IDS analyzes network traffic and system logs to identify potential security threats and alert system administrators to take appropriate action. There are different types of IDS, including signature-based, anomaly-based, and hybrid approaches. IDS uses a variety of detection methods to identify potential security threats, including statistical analysis, machine learning, and rule-based methods. However, IDS faces several challenges, including the need to balance detection accuracy with false positives and false negatives while keeping up with the ever-changing threat landscape. There are an evaluation metrics, such as detection rate, false positive rate, and accuracy. In this paper, we use accuracy of the detection to evaluate the effectiveness of IDS. Data preprocessing and feature selection are also important to improve search capabilities in IDS. In general. Ongoing research in this area, including the use of deep learning models such as convolution neural network (CNN), long-short term memory (LSTM) and hybrid models with CIC-IDS 2018 dataset, will continue to improve IDS ability to detect and prevent security breaches. This research will enhance accuracy detection of the IDS.