LAUSR

Internet of Things (IoT) systems are beneficial to our daily lives and have become increasingly important. A complete IoT system includes devices, sensors, networks, software, and other essential components necessary for operation and interconnection. However, devices and sensors of this nature often have low resource requirements and multiple security vulnerabilities from manufacturers. Moreover, edge network areas of IoT systems exhibit several security weaknesses. Consequently, unauthorized hijacking of sensors or denial-of-service attacks on edge network areas can have severe consequences for the system’s operation. In this study, we propose a model that combines machine learning algorithms and principal component analysis techniques to train and predict Distributed Denial of Service (DDoS) attacks. Principal component analysis techniques were applied to reduce data dimensionality. We used accuracy, precision, recall, and F1-Score as the evaluation metrics. We explain the True Positive, False Positive, True Negative, and False Negative measures as basic parts of the above evaluation metrics. Unlike previous studies, we used the Training Time to evaluate the training time of each model. We employed two datasets, CICIDS 2017 and CSE-CIC-IDS 2018, to evaluate our proposed model. In general, the proposed models exhibited the best performance and improved training time.