Remora whale optimization-based hybrid deep learning for network intrusion detection using CNN features

Security remains as a key role in this internet world owing to the fast expansion of users on the internet. Numerous existing intrusion detection approaches were introduced by numerous researchers to recognize and identify intruders. Meanwhile, the existing systems failed to achieve satisfactory detection accuracy. Hence, this paper develops a robust intrusion detection model, named Remora Whale Optimization (RWO)-based Hybrid deep model for detecting intrusions. Here, the input data is pre-processed, and thereafter data transformation is done. With the transformed data, effective CNN features are extracted and feature conversion is performed to convert the features into vector form. Moreover, RV-coefficient is accomplished for performing feature selection process and finally, network intrusions are effectively detected using Hybrid deep model where the Deep Maxout Network and Deep Auto Encoder are used. On the other hand, the training procedure of the Hybrid deep model is carried out using the designed optimization algorithm, named RWO, which is the hybridization of the Remora Optimization Algorithm (ROA) and Whale Optimization Algorithm (WOA). Furthermore, the devised technique achieved superior performance using the evaluation metrics, such as testing accuracy, precision, recall, and F1score with the higher values of 0.938, 0.920, 0.932, and 0.926, respectively.

Automated summary

This paper proposes a hybrid deep model named RWO for detecting network intrusions. The model is trained using a combination of Remora Optimization Algorithm and Whale Optimization Algorithm, and improves detection accuracy through steps such as extracting CNN features and performing feature selection. Experimental results show that the technique achieves superior performance in terms of testing accuracy, precision, recall, and F1 score.