Artificial Intelligence Based Real Time Packet Analysing to Detect DOS Attacks

A Denial-of-Service attack is a common network attack. Hence, the research into the early detection of DOS attacks is very crucial. However, there has yet to be a detection approach that is both accurate and quick to detect the attack. In light of this, this research presents a neural network-based DOS detection approach. The dataset of collected packets, feature extraction, and classification comprises the three parts of this article. The dataset consists of both malicious and non-malicious raw captured packets; in the classification stage, packets are categorized as malicious or non-malicious; in the feature extraction stage, different attributes of packets are extracted using Natural Language Processing; in the implementation stage, these features are used as input to the machine learning model. The experimental findings demonstrate that the proposed DOS attack detection model has a high level of accuracy and can identify common DOS assaults in a reasonable amount of time.