In recent years, network expansion has increased exponentially, making security a pressing issue for modern systems. Monitoring user activity for abnormalities is a useful fraud detection strategy. The ability of a system to efficiently discover new, previously unknown vulnerabilities and respond in a way that minimises damage and, ideally, removes the threat, is one of the most important open research topics in the field of cyber security. This research provides a blueprint for an intrusion detection system that employs pattern matching and self-replication among other methods. As the system detects potentially dangerous symptoms in the surroundings, it compares them to the events that have become apparent so far to find a pattern that may explain their occurrence. Once this happens, it alerts other nodes in the system to keep an eye out for harmful event sequences, and it initiates the defence mechanism that lessens the number of false intrusion alarms. Using natural intrusion detection and self-healing idea, this research outlines a novel method for network security. An Imperative Node Evaluator with Self Replication Code and Auto Triggering Mode (INE-SRC-ATM) is proposed in this research for auto healing of the network if intrusion occurs and also to perform auto triggering of nodes for securing the network and reducing the false alarms. To activate the self-healing mechanism, the IDS must first identify and assess the impact of hostile actions on the network. This means that the self-healing process begins when the damage caused by malevolent activity is identified. The proposed model self triggering model immediately triggers when there is a dissimilarity on attributes that improve the network security levels. The proposed model when contrasted with the traditional model performs high in intrusion detection in terms of self replication triggering accuracy and intrusion detection accuracy levels.

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 signalbased networks.

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