DDAM: Detecting DDoS Attacks Using Machine Learning Approach

Dealing the Distributed Denial of Service (DDoS) attack is a continuing challenge in the field of network security. An Intrusion Detection System (IDS) is one of the solutions to detect the DDoS attack. The IDS system should always be updated with the attack disincentive to preserve the network security service. In this paper, we propose a new approach for anomaly detection using machine learning to secure the network and to determine the attack patterns. The major contribution is to create real-time dataset and to use the naive Bayes algorithm as a classifier for detecting and comparing its performance with the existing classifiers like random forest and J48 algorithm. The experimental results on the real-time dataset confirm that the proposed machine learning approach can effectively detect network anomalies with high detection rate and low false positive rate.