Deep Learning- based Network Intrusion Detection System

Abstract

Multimedia data centres have grown as a result of the use of cloud services, IoT, and real-time streaming, but they are now more susceptible to sophisticated attacks. This study suggests a hybrid deep learning-based intrusion detection system (IDS) to identify a variety of attacks, such as DoS/DDoS, Probe/Reconnaissance, R2L, U2R, and IoT-specific threats like ransomware and backdoors. We employ a supervised architecture to record the spatial-temporal patterns of known attacks, and an unsupervised Autoencoder to detect abnormalities. Using a majority voting method, the outputs of both models were combined to provide the final findings. A number of preparation stages, such as MinMax normalization, binary and one-hot label encoding, data forming to match deep learning models, and class balancing using strategies like SMOTE or under-sampling, are required to make the methodology function in real-world conditions. The best performance on CIC-IDS2018, with an accuracy of 99.1%, precision of 98.9%, recall of 99.3%, and F1-score of 99.1%, was achieved by the hybrid model when it was tested on three benchmark datasets: TON_IoT, CIC-IDS2018, and NSL-KDD.
Additionally, strong results were shown by NSL-KDD (accuracy 98.2%) and TON_IoT (accuracy 97.3%), and the flexibility and adaptability of the suggested IDS were illustrated. Good accuracy, precision, recall, F1-score, and ROC-AUC are kept by the system while false positives and false negatives are reduced. These findings show that the suggested IDS is reliable, flexible, and capable of defending multimedia data centers against dynamic Internet attacks.