A Composite Approach of Intrusion Detection Systems: Hybrid RNN and Correlation-Based Feature Optimization

Detection of intrusions is a system that is competent in detecting cyber-attacks and network anomalies. A variety of strategies have been developed for IDS so far. However, there are factors that they lack in performance, creating scope for further research. The current trend shows that the Deep Learning (DL) technique has been proven better than traditional techniques for IDS. Throughout these studies, we presented a hybrid model that is a Deep Learning method called Bidirectional Recurrent Neural Network using Long Short-Term Memory and Gated Recurrent Unit. Through simulations on the public dataset CICIDS2017, we have shown the model’s effectiveness. It has been noted that the suggested model successfully predicted most of the network attacks with 99.13% classification accuracy. The proposed model outperformed the Naïve Bayes classifier in terms of prediction accuracy and False Positive rate. The suggested model managed to perform well with only 58% attributes of the dataset compared to other existing classifiers. Moreover, this study also demonstrates the performance of LSTM and GRU with RNN independently.