DDoS Attack Simulation and Machine Learning-Based Detection Approach in Internet of Things Experimental Environment

Abstract

Aiming at the problem of DDoS attack detection in internet of things (IoT) environment, statistical and machine-learning algorithms are proposed to model and analyze the network traffic of DDoS attack. Docker-based virtualization platform is designed and configured to collect IoT network traffic data. Then the packet-level, flow-level, and second-level network traffic datasets are generated, and the importance of features in different traffic datasets are sorted. By SKlearn and TensorFlow machine-learning software framework, different machine learning algorithms are researched and compared. In packet-level DDoS attack detection, KNN algorithm achieves the best results; the accuracy is 92.8%. In flow-level DDoS attack detection, the voting algorithm achieves the best results; the accuracy is 99.8%. In second-level DDoS attack detection, the RNN algorithm behaves best results; the accuracy is 97.1%. The DDoS attack detection method combined with statistical analysis and machine-learning can effectively detect large-scale DDoS attacks on the internet of things simulation experimental environment.