Binary Grasshopper Optimization Based Feature Selection for Intrusion Detection System Using Feed Forward Neural Network Classifier

Background: The occurrence of intrusions and attacks has increased tremendously in recent years, thanks to the ever-growing technological advancements in the internet and networking domains. Intrusion Detection System (IDS) is employed nowadays to prevent distinct attacks. Several machine learning approaches have been presented for classifying IDS. However, IDS undergoes dimensionality issues that result in increased complexity and decreased resource exploitation. Consequently, it becomes necessary to investigate the significant features of data using IDS in order to reduce the dimensionality. Aim: In this article, a new Feature Selection (FS)-based classification system is presented which performs both FS and classification processes. Methods: In this study, a binary variant of the Grasshopper Optimization Algorithm called BGOA is applied as FS model. The significant features are integrated using an effective model to extract the useful ones and discard the useless features. The chosen features are given to Feed-Forward Neural Network (FFNN) model to train and test the KDD99 dataset. Results: The presented model was validated using the benchmark KDD Cup 1999 dataset. With the inclusion of FS process, the classifier results got increased by attaining a FPR of 0.43, FNR of 0.45, sensitivity of 99.55, specificity of 99.57, accuracy of 99.56, F-score of 99.59 and kappa value of 99.11. Conclusion: The experimental outcome confirmed the superior performance of the presented model compared to diverse models from several aspects and was found to be an appropriate tool for detecting intrusions.