A New Design of Custom Optimized Cnn-Lstm Assists to Detect Network Anomaly Using Categorical Data

Abstract

For traditional intrusion detection model, the system effectiveness is fully based on training dataset and feature selection. During feature selection, it needs more labour charge and trusted mainly on expert’s knowledge. Moreover, the training dataset contains more imbalanced data which in terms model tends to be biased. Here, an automatic approach is introduced to correct deficiency in the system. In this paper, the author proposes novel network anomaly detection (NID) build using categorical data. A model has to be designed with modified form of deep neural network primarily utilized for detecting anomaly within the network. Custom CNN-LSTM with Harris Hawks Optimization (named as custom optimized CNN-LSTM) is designed as a new classifier majorly used to detect the anomaly from word cloud to distinguish the data with effective performance. The experimental result shows that the proposed method achieves a promising output for network anomaly detection.