Intrusion Detection System using Osprey Optimization Algorithm with Bidirectional Gated Recurrent Unit Technique
Keywords:
Bidirectional Gated Recurrent Unit, Label Encoding, Intrusion Detection System, Min-max Normalization, Osprey Optimization Algorithm, SMOTEAbstract
Nowadays, the Intrusion Detection System (IDS) is one of the most important application of security in mobile networks, and it is considered a significant method for exposing attacks and applying security measures to networks. For this reason, various types of IDS approaches have been established in conventional research that focus on recognizing intrusions from datasets with the help of a classification issue. However, conventional techniques are limited in identifying malicious attacks due to the issue of overfitting. To overcome this issue, the Osprey Optimization Algorithm with Bidirectional Gated Recurrent Unit (OOA-BiGRU) is proposed in this research for IDS classification. The OOA selects the set of best features by updating positions based on the chasing and attack behavior. The weights are assigned by a self-attention mechanism which enables the BiGRU to adopt attack patterns and enhance the classification accuracy. Various datasets such as CICIDS-2018, CICIDS-2017, UNSW-NB15 and NSL-KDD are preprocessed by label encoding and min-max normalization to convert categorical feature into integer format and normalize the features. The Synthetic Minority Over-sampling Technique (SMOTE) is the oversampling technique employed for balancing the dataset. The accuracy, precision, recall and f1-score are taken as parameters to estimate the model’s performance. The OOA-BIGRU achieves the accuracies of 99.86%, 98.64%, 99.72% and 99.83% respectively on NSL-KDD, UNSW-NB15, CICIDS-2017 and CICIDS-2018 datasets, which is superior when compared to existing methods.
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