Empowering Intrusion Detection Systems: A Synergistic Hybrid Approach with Optimization and Deep Learning Techniques for Network Security

Over last decade, there is a rapid advancement in networking and computing technologies that produced large volume of sensitive data. Clearly, protecting those data from intrusions and attack is of paramount importance. Researchers have proposed many cyber security solutions and tools to protect the data. One such technique for safeguarding data is the Intrusion Detection System (IDS). This research introduces a hybrid optimization-based Feature Selection (FS) and deep learning-driven categorization namely Honey Badger Optimization-Artificial Neural Network (HBO-ANN) to identify intrusions. The Honey Badger Optimization (HBO) is an optimization technique that is utilized to choose the dataset’s most important features. The Artificial Neural Network (ANN) receives reduced features dataset and classifies it as benign or attack. Additionally, a well-known CIC-IDS 2017 dataset is employed to construct and validate the suggested system. Performance metrics for assessing the effectiveness of the suggested system are the false alarm rate, Mean Squared Error (MSE), precision, accuracy and recall. The testing and training MSEs are 0.009 and 0.00317, respectively. The model’s accuracy is 97.66%. The model has a precision of 98.03% and a recall of 97.18%. There is a 1.97% false alarm rate. The outcomes have been compared with bench mark models such as Grey Wolf Optimizer-Support Vector Machine (GWO-SVM), Particle Swarm Optimization-Support Vector Machine (PSO-SVM), Fuzzy Clustering-Artificial Neural Network (FC-ANN), Bidirectional Long-Short-Term-Memory (BiDLSTM) and Feed-Forward Deep Neural Network (FFDNN). As demonstrated by the experimental results, the suggested model outperforms the benchmark algorithms