DDoS Cyber-Attacks Detection-Based Hybrid CNN-LSTM

Protecting software-defined networking (SDN) against cyber-attacks has become crucial in an expanding digital threat environment. Distributed Denial-of-Service (DDoS) attacks are risky since they may seriously interrupt operations. To mitigate these risks, this study introduces an anomaly detection method that utilizes a hybrid convolutional and short-term memory (CNN-LSTM) deep neural network. This model merges the CNN's ability to automatically extract spatial features with the LSTM's proficiency in sequence modeling, thereby enhancing the detection of anomalies in network traffic metadata. The model also integrates an autoencoder structure to facilitate representation learning and reduce dimensionality. The model's effectiveness was tested using publicly accessible SDN datasets, and the results were remarkable. The model identified DDoS attacks with an accuracy rate of over 99%, surpassing the performance of previous shallow learning models. Moreover, the model proved highly adaptable, successfully detecting attacks across various data samples. This deep learning-based detection system is a significant advancement, providing precise and efficient analytics that bolster real-time cybersecurity monitoring. However, it's crucial to continue research in deployment, interpretability, and the potential of combinatorial learning with other advanced technologies. We can only fully harness the great potential of artificial intelligence for adequate cyber protection by looking into these areas.