Dense capsule stacked auto-encoder model based DDoS attack detection and hybrid optimal bandwidth allocation with routing in VANET environment

The vehicle ad hoc network, or VANET, is a fantastic tool for smart transport since it improves efficiency, management, traffic safety, and comfort. Distributed Denial of Service (DDoS) attacks on VANET infrastructure have the potential to compromise traffic safety by causing collisions and fatalities. Therefore, while integrating VANETs into intelligent transport networks, the pertinent security issues must be addressed. This paper provides an efficient routing optimization as well as a deep learning-based attack detection approach. The input data are first collected from publically accessible datasets. After that, a unique Dense Capsule Stacked Auto Encoder (DCSAE) network is developed to detect the presence of DDoS attacks in the inputs. Here, the detection method is enabled by the hybridization of the Capsule Network with a Stacked Auto Encoder. Moreover, the Improved Fire Hawks Optimization Algorithm (IFHOA) is employed to refine the proposed detection technique. Once assaults have been discovered, bandwidth is allocated using the Hybrid Remora Whale Optimization (HRWO) approach. Finally, an Improved Osprey Optimization (IOO) method is utilized to identify a better routing path by taking into account aspects such as energy usage, delay, and drop. The DDoS SDN dataset is employed to implement the proposed method. In the results section, the suggested technique is compared to existing methods in terms of recall, accuracy, precision, F1 score, Mean Absolute Error (MAE), Packet Delivery Ratio (PDR), Packet Loss Ratio (PLR), and consumption of energy. The proposed model achieved an accuracy of 94.07 % while achieving the precision, recall, and F1-score of 94.2 %, 93.33 %, and 93.88 %, respectively. The model achieved the MAE of 0.132, delay of 4812.976, energy consumption of 40.13 %, PDR of 95.1805, and PLR of 3.6816 %, respectively.