Dos Attack Prediction

The rapid growth and dependence on internet-based services have made networks increasingly vulnerable to a wide array of cyber threats. Among these, Denial of Service (DoS) attacks stand out as one of the most disruptive and harmful types of cyberattacks. A DoS attack targets network services with excessive traffic or malicious requests, rendering them inaccessible to legitimate users. The consequences of these attacks can range from temporary service interruptions to substantial financial losses and reputational harm fororganizations.Traditional security solutions, such as signature-based and rule-based intrusion detection systems, have been widely implemented to identify and mitigate these threats. However, these conventional approaches face significant limitations. They primarily rely on predefined patterns and known attack signatures, making them ineffective against novel or evolving attack strategies. In today’s dynamic cyber environment, attackers continuously develop new tactics to bypass static defense mechanisms.As a result, there is a growing demand for intelligent, adaptive security solutions capable of identifying abnormal patterns in real-time. Machine Learning (ML) presents a promising alternative to traditional detection systems by offering the ability to learn from data and adapt to new attack patterns without explicit programming.