Intelligent Detection System for a Distributed Denial-of - Service (DDoS) Attack Based on Time Series

With a surge in the usage of systems that largely depend on networking and programming, the need for cybersecurity has grown as well. Cyberattacks are a rising threat to companies and people. The Distributed Denial of Service (DDoS) attack is one of the destructive hacks that have swiftly acquired appeal among hackers. In this work, a security system is proposed to prevent DDoS. In other words, it has the ability to protect external and internal communication systems from attacks. The primary contribution of this work is to acquire the best accuracy based on time series. Multiple machine learning algorithms are applied and compared between them. The Random Forest accuracy is 100% and the XGBoost was 91% using the same data set.

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