Hybrid optimization enabled deep learning technique for multi-level intrusion detection

Abstract

The intrusion detection system identifies the attack through the reputation and progression of network methodology and the Internet. Moreover, conventional intrusion recognition techniques usually utilize mining association rules for identifying intrusion behaviors. However, the intrusion detection model failed to extract typical information of user behaviors completely and experienced several issues, including poor generalization capability, high False Alarm Rate (FAR), and poor timeliness. This paper uses a hybrid optimization-based Deep learning technique for the multi-level intrusion detection process. First, the fisher score scheme is applied to extract the important features. Then, in the data augmentation the data size is increased. In this model, Rider Optimization Algorithm-Based Neural Network (RideNN) is employed for first level detection, where the data is categorized as normal and attacker. Besides, the RideNN classifier is trained by devised Rider Social Optimization Algorithm (RideSOA). Additionally, the Deep Neuro Fuzzy network (DNFN) is utilized for the second level classification process in which attack types are categorized. Besides, the DNFN classifier is trained through devised Social Squirrel Search Algorithm (SSSA). The introduced intrusion detection algorithm outperformed with maximum precision of 0.9254, recall of 0.8362, and F-measure 0.8718.