Advancements in cyberthreat intelligence through resource exhaustion attack detection using hybrid deep learning with heuristic search algorithms

The Distributed Denial of Service (DDoS) attack is uncontrollable and appears in different patterns and shapes; accordingly, it is not easily detected and solved with preceding solutions. A DDoS attack is the most serious threat on the Internet. These attacks became a preferred weapon for cyber extortionists, terrorists, and hackers. These attacks can quickly undermine a target, producing massive revenue loss. Classification methods are applied in numerous investigations and have been used to identify and resolve DDoS attacks. Detection of DDoS attacks is problematic in terms of identifying and mitigating them. However, it is valuable as these attacks may lead to big problems. Various methods are presented for attack detection and prevention. However, artificial intelligence (AI)-based Machine learning (ML) and deep learning (DL) methodologies are highly effective for detecting DDoS attacks in cybersecurity. This paper proposes a Cybersecurity-Resource Exhaustion Attack Using Hybrid Deep Learning Model and Metaheuristic Optimizer Algorithms (CREA-HDLMOA) technique. The primary goal of the CREA-HDLMOA technique is to advance an effective method for DDoS attack detection using advanced optimization algorithms. Initially, the data normalization stage leverages linear scaling normalization (LSN) for converting input data into a beneficial format. Furthermore, the feature selection process uses the RIME optimization algorithm (ROA) model to select the most relevant features from the data. In addition, the hybrid of long short-term memory and bidirectional gated recurrent unit (LSTM + Bi-GRU) technique is employed for the DDoS attack classification process. Finally, the modernized pufferfish optimization algorithm (MPOA)-based hyperparameter selection process is performed to optimize the classification results of the LSTM + BiGRU technique. An extensive simulation is performed to validate the performance of the CREA-HDLMOA method under CIC-IDS2017 and Edge-IIoT datasets. The experimental validation of the CREA-HDLMOA method portrayed a superior accuracy value of 99.31% and 99.36% under dual datasets over existing approaches.