Real Time Network Traffic Analysis Using Artificial Intelligence, Machine Learning and Deep Learning: A Review of Methods, Tools and Applications

In order to spot potential security threats or performance problems, Network Traffic Analysis (NTA) involves monitoring and analyzing network traffic. However, Machine Learning (ML) methods are frequently used to automate NTA. Network traffic classification, anomaly detection, and malicious activity detection can all be done using ML techniques. In order to enhance network performance, they can also be utilized to forecast future traffic patterns. ML algorithms come in a variety of forms and can be applied to NTA. Support vector machines (SVM), decision trees, and random forests are the most used methods. Depending on the particular application, an algorithm will be chosen. SVMs, for instance, are frequently used for classification tasks, whereas decision trees are frequently utilized for anomaly detection jobs. Network performance and security can be enhanced using NTA employing ML. It can aid in the detection of possible risks, the prevention of data breaches, and the enhancement of network performance. In the proposed work real time NTA using ML and DL algorithms discussed with tools and applications. Random forest algorithm is implemented and obtained an accuracy of 99.31%. Benefits of applying ML to NTA includes increased accuracy when it comes to spotting dangers and anomalies, ML algorithms have the potential to be more precise than conventional rule-based techniques. Less false positives, ML algorithms can be customized to produce fewer false positives, which can save time and money. Enhanced scalability, ML algorithms can be scaled to manage high levels of network traffic.