DDoS Detection in Industrial Internet of Things Using Machine Learning

다국어 초록 (Multilingual Abstract)

Ensuring security in IIoT devices is a big concern where sometimes critical infrastructure related to it, risks are not acceptable. Distributed denial of service (DDoS) attack is one of the dangerous attack which generally come from the application or network layer where effected system and attackers system are connected. An efficient detection system for DDoS attack is important. This paper propose machine learning-based DDoS detection system with Synthetic Minority Over-sampling Technique (SMOTE) for solving imbalance data problem and increase accuracy. The simulation is done with UNSW-NB 15 dataset over AdaBoost,Logistic Regression, Decision Trees, Random Forest, KNN, Naive Bayes Classifier and achieve attack detection rate higher than 99% with a false alarm rate less than 1%.