DDoS attack detection in IoT environment using optimized Elman recurrent neural networks based on chaotic bacterial colony optimization

The Internet of Things (IoT) is made up of billions of interconnected devices that can transmit and receive data over the Internet. IoT devices have many vulnerabilities that attackers could use to compromise their security because of the heterogeneity of device connectivity. Distributed denial-of-service (DDoS) attacks against those applications become more common as IoT applications continue to expand and devolve. Identifying DDoS attacks is a difficult process due to the variety of IoT devices connected. The present article proposed a new method to detect DDoS attacks using an optimized Elman recurrent neural network (ERNN) based on chaotic bacterial colony optimization (CBCO) called CBCO-ERNN. The proposed method uses CBCO for obtaining optimal parameters (weights and biases) and structure (number of hidden neurons) of ERNN architecture. The chaos theory is applied to improve BCO's exploration and exploitation capabilities by initializing the bacterial population and selecting the appropriate chemotaxis step size value. The CBCO approach is used to train the ERNN model to avoid local optima and enhance the convergence rate. The performance of the CBCO-ERNN is tested and evaluated using four benchmark attack datasets such as the BoT-IoT, CIC-IDS2017, CIC-DDoS2019, and IoTID20 datasets, and five performance metrics are considered: accuracy, sensitivity, specificity, precision, and F-Score. According to the experimental results, the CBCO-ERNN method provides a high detection and a faster convergence rate when compared to earlier algorithms.