Detection of Distributed Denial-of-Service Attacks in Cloud Computing Environments Using Deep Learning
Keywords:
DDoS attack detection, deep learning, feature selection, feature fusion, cloud computing, cybersecurity, CNN-LSTMAbstract
With the rapid expansion of cloud computing and the Internet of Things (IoT), Distributed Denial-of-Service (DDoS) attacks have become one of the most critical cybersecurity threats, increasing the importance of their rapid and accurate detection. This study proposes a two-stage detection approach based on group feature fusion for identifying DDoS attacks in cloud environments. In the first stage, optimal feature selection is performed using a combination of several metaheuristic algorithms, including the Genetic Algorithm, Grey Wolf Optimizer, Particle Swarm Optimization, Harris Hawk Optimization, and Whale Optimization Algorithm. Subsequently, the selected features are integrated using three fusion strategies, namely voting-based fusion, weighted fusion, and ensemble learning-based fusion. In the second stage, a hybrid deep learning model composed of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network is developed to extract the spatial patterns and temporal dependencies of network traffic, respectively. Experimental evaluation of the proposed method on the NSL-KDD and BoT-IoT datasets demonstrates that the presented model achieved accuracies of 99.1% and 99.2%, respectively, representing a significant improvement over previous methods. In addition to enhancing detection accuracy, the false alarm rate was reduced, and the model exhibited satisfactory generalization capability against different types of cyberattacks. Future research may further improve the performance of this approach in real-world environments through model architecture optimization, the utilization of pre-trained networks, and computational complexity reduction.
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Copyright (c) 2025 Hind Saad Hussein Mosawi (Author); Farhad Navabifar (Corresponding author); Hayder Kadhim Hammood Mzedawee, Fariba Majidi (Author)
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
