Abstract
The 5G wireless networks associated with higher data-transferring speeds considerably affect the performance of IoT networks. Nowadays, the Internet has become a very significant aspect of human lives, and it aids in data transfers, processing, and storing. However, 5G networks are subjected to varied cyber security attacks, which are hard to detect. As a result, it is required to set up attack detection models that can recognize 5G network's distributed denial of service (DDoS) attack. Thereby, this article aims to introduce a new model for detecting DDoS attacks. Initially, from input data, features such as statistical features, improved exponential moving average, higher order statistical features, MI, and improved correlation based features are derived. Further, a gain ratio ranking model is used for picking fine features from the overall derived features. Finally, at the detection stage, bidirectional long short-term memory as well as optimized deep belief network (DBN) are introduced that portray the detected results in a precise way. DBN weights get fine-tuned by the combined shark smell and electric fish model with new distance based active eco-location model. Finally, the created approach's advantages are demonstrated using a variety of metrics, including precision, accuracy, F-measure, and others. In particular, the performance metrics of the proposed work at node = 6000 are accuracy = 98.59%, sensitivity = 96.82%, specificity = 97.52%, precision = 98.91%, F-measure = 97.86%, MCC = 92.19%, NPV = 97.24%, FPR = 2.47%, and FNR = 3.17%, respectively.