At present, botnets are thought to be among the most advanced vulnerability threats. Botnets generate significant control over a substantial amount of network traffic. According to information security reports, distributed denial-of-service (DDoS) attacks have resulted in major financial losses for governments and businesses around the world in recent years. Traditional detection techniques frequently encounter challenges when it's trying to effectively mitigate novel and advanced distributed DDoS attacks. As a result, there is an increasing interest in utilizing machine learning methods to enhance detection capabilities. The occurrence of a DDoS attack is possible across various layers of the OSI model, including the network, transport, and application layers. Furthermore, the detection is concentrated on identifying application layer DDoS attacks rather than transport and network DDoS attacks. The primary objective of the research is to develop and deploy a real-time early detection method for DDoS attacks by using different machine learning algorithms.
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