|
Abstract
|
The increasing prevalence and significance of DDoS attacks highlight the critical need for developing efficient diagnostic techniques to enhance network security. The ensemble strategy, which integrates hybrid deep learning (DL) models such as Convolutional Neural Network-Generative Adversarial Network (CNN-GAN), Channel Attention-LSTM, Long Short-Term Memory (LSTM), and Bidirectional LSTM (BiLSTM), could improve the accuracy of DDoS attack diagnosis. By leveraging the unique strengths of each model—LSTM and BiLSTM for their sequential data processing abilities, CNN-GAN for feature extraction and general diagnosis, and Channel Attention-LSTM for emphasizing relevant information—this multi-model approach is well-positioned to better understand the complex patterns and behaviors associated with DDoS attacks.
The significance of this study lies in its potential to provide a comprehensive solution for DDoS attack diagnosis, which is essential for maintaining the integrity and accessibility of online services. As cyber threats continue to evolve, traditional diagnostic techniques often fail to accurately identify and address such attacks in real-world scenarios. By integrating various deep learning (DL) models, the proposed ensemble strategy aims to enhance diagnostic performance, reduce false positives, and improve response times to threats. This study not only contributes to the field of cybersecurity but also offers practical benefits for organizations relying on online infrastructures, thereby strengthening their resilience against one of the most prevalent types of cyberattacks.
|