Using Artificial Intelligence to Evaluating Detection of Cybersecurity Threats in Ad Hoc Networks

Abstract

This paper is devoted to the use of AI managed to contribute to security of the MANETs (Mobile Ad-hoc Networks), decentralized and mobile wireless networks, that are fully dynamic in nature. The intention of the research is to audit the dangers of cyber and to spot the variety of cyber threats types, including Distributed Denial of Service (DDoS) attacks, malware intrusions, leakages or data breaches, or unauthorized access attempts, using AI-powered algorithms and models. The purpose is to obtain higher degree of veracity of defining and classifying these threats and as result puts more security and reliability to MANET networks. Anomaly detection addressed as a secondary line of defense specific for MANET hardware and network traffic. The monitoring method is needed here to find abnormal behavior that might anyhow signify the possible security flaws or the attacks of the MANET environments. This ultimate goal is penetrated with the timely detection Peculiarities, which makes possible to reinforce MANET security capabilities that require to be well-developed against cyber threats. Experimental results reveal a clear trend of Fleet Grid Algorithm Improvements along with Detection Accuracy (Digital Signals and Anomaly) by means of training AI models (CNN and RF) with algorithms like Random forest and Convolutional neural networks. The machine learning based algorithms often present remarkable results comprising efficiency in detecting and effectively categorizing different cyber threats existing such as DDoS attacks, malware infiltrations and attempted unauthorized access. This method of anomaly detection is able to accurately detect robot anomalies and malicious activities in network traffic in addition to we preventing system vulnerabilities or threats from occurring prematurely. Besides, the findings of this study wide relatively efficient AI-based cybersecurity systems for dynamic decentralized MANET systems, which are developed for street-view switching and path finding, self healing and self configuration.