A Novel Sampling Technique for Detecting Cyber Denial of Service Attacks on the Internet of Things

Abstract

Internet of Things (IoT) devices are vulnerable to a wide range of unique security risks during the data collection and transmission processes. Due to a lack of resources, these devices increased the attack surface and made it easier for an attacker to find a target. The Denial of Service (DoS) attack is one of the most common attacks that can target all layers of the IoT protocol. Therefore, Intrusion Detection Systems (IDS) based on machine learning (ML) are the best ways to confront these risks. However, an imbalanced dataset for cyber attacks makes it difficult to detect them with ML models. We propose an undersampling technique that clusters the data set using Fuzzy C-means (FCM) and picks similar instances with the same features to ensure the integrity of the dataset. We used accuracy, precision, sensitivity, specificity, F-measure, AUC, and G-means to determine how good the results were. The proposed technique had 97.6% overall accuracy. Furthermore, it got 96.94%, 96.39%, 99.59%, 98.08%, and 97.16% True Positive Rates (TPR) in the Blackhole, Grayhole, Flooding, Scheduling, and Normal (no attacks) classes, respectively. The results show that the proposed method for detecting DoS attacks in the IoT has succeeded.