An Efficient Technique for Identifying Distributed Denial of‎Service Active Assaults Using Deep Neural Networks Based on the ‎Adaptive System Intelligence Paradigm - Kaliyaperumal, Karthikeyan; Boddu, Raja Sarath Kumar; Oruganti , Sai Kiran; Kebesa , Guidsa Tesema; Aghaeiboorkheili , Mohsen; Bhojan, Rajendran (International Journal of Basic and Applied Sciences, 2025)
A collection of interconnected devices that exchange data online is known as the Internet of Things. The IoT environment’s diverse components make the distributed denial-of-service attack a security risk. One of the most important tasks in creating a smarter environment for ‎end users is detecting DDoS attacks in the Internet of Things. A new version of the optimized Elman recurrent neural network (ERNN) is ‎proposed to detect DDoS active attacks in Internet of Things scenarios. The proposed detection approach optimizes the weight and bias of ‎ERNN (ABCO-ERNN) using a novel adaptive bacterial colony optimization (ABCO) technique. The ABCO algorithm uses an adaptable ‎step size to increase the BCO’s capacity for both exploration and exploitation. The four datasets, BoT-IoT, CIC-IDS2017, CIC-DDoS2019, ‎and IoTID20, are used to compare performance, and five distinct performance measures, including accuracy, precision, sensitivity, specificity-‎ty, and f-measure, are considered. When compared to previous literature algorithms, the proposed ABCO-ERNN detection approach ‎produced a high detection rate according to the experimental results‎.