Embedded Signal Artificial Neural Network Based Intelligent Non-Dependent Feature Selection for Cyber Attack Classification in Signal-Based Networks

Abstract

Signal-based cyber attacks pose a significant threat to the integrity, confidentiality, and availability of information systems. Intrusion Detection Systems (IDS) monitor network and system activities for malicious activity or policy breaches, which are then reported to a management station. Due to the high volume of network traffic in cyber networks, real-time threat detection is often computationally infeasible. In this study, we explore the use of an Artificial Neural Network (ANN) for cyber network threat identification, specifically focusing on its application in nonlinear characteristics and network security domains. Data reduction is crucial for achieving real-time detection in a Signal-based Cyber Attack Detection Model (SCADM). However, traditional CADMs analyze all data features to detect patterns of intrusion or misuse, leading to redundancy in detection features. The primary objective of this research is to identify computationally efficient and effective input features for SCADM. We propose an embedded Signal with ANN-based Intelligent Non-Dependent Feature Selection Model (ANN-INDFSM) that effectively extracts signal-based cyber attack features and performs feature reduction for accurate detection of signal-based cyber attacks while maintaining security. The ANN-based feature selection method was employed for eliminating non-salient features and determining dimensionality levels. Given the diverse characteristics and pattern types of emerging cyber attacks, tracking them has become increasingly challenging. Various methods have been used for feature extraction and selection, with the ultimate goal of detecting anomalies in large cyber security datasets. Although this process is both time-consuming and computationally demanding, the efficiency of machine learning algorithms can be improved by removing unnecessary and redundant features. Feature selection (FS) serves as one such method. By utilizing datasets containing only a sufficient subset of features instead of the full dataset, the computational time required for attack detection algorithms can be reduced. When compared to existing models, the proposed ANN-INDFSM demonstrates optimized performance levels, providing a streamlined and effective solution for the detection of cyber attacks in signal-based networks.