The document discusses using machine learning algorithms to classify network traffic as malicious or non-malicious. It describes capturing packets from a dummy website under distributed denial of service (DDoS) attack to create a dataset. Two machine learning algorithms, naive Bayes and support vector machines (SVM), are used to classify the network traffic. Both algorithms achieved over 98% accuracy in detecting spam traffic. The paper proposes creating a real-time network traffic classification system using machine learning algorithms to improve network security.