Hybrid Feature Selection for Efficient Detection of DDoS Attacks in IoT

The increasing Distributed Denial of Service (DDoS) attacks on the Internet of Things (IoT) is leading to the need for an efficient detection approach. Although much research has been conducted to detect DDoS attacks on traditional networks, such as machine learning (ML) based approaches that have improved accuracy and confidence, the limited bandwidth and computation resources in IoT networks restrict the application of ML, especially deep learning (DL) based solutions that require extensive input data. In order to appropriately address the security issues in the resources-constrained IoT network, this paper is aimed to reduce the input data dimensions by extracting a subset of the most relevant features from the original features and using this subset to detect DDoS attacks on IoT without degrading the detection performance. A cost-effective model is developed to clean and prepare raw data before dimensionality reduction. A hybrid feature selection that uses Mutual Information (MI), Analysis of Variance (ANOVA), Chi-Squared, L1-based feature selection, and Tree-based feature selection algorithms is designed to identify important data features and reduce the data inputs needed for detection. Simulation results show that detection accuracy is improved with the combination of features chosen by the proposed hybrid feature selection approach. The training time is much less than the combination of each individual feature selection method.