Enhancing Trust-Based Attacker Detection in 5G Social Networks Through Advanced Artificial Intelligence Control
Dr Ramakrishnan Raman, Vikram Kumar, Biju G. Pillai, Dhaval Rabadiya, Smruti Patre, R. Meenakshi · 2024
In the evolving landscape of 5G social networks, the surge in connectivity and data exchange has amplified the susceptibility to malicious attackers, undermining the trust and security integral to these networks. This paper introduces an innovative approach to bolster trust-based attacker detection mechanisms through the integration of advanced artificial intelligence (AI) control techniques. Leveraging the prowess of machine learning (ML) algorithms and AI-driven analytics, our proposed framework enhances the accuracy and efficiency of identifying potential threats within 5G social networks. By implementing a hybrid model that combines anomaly detection algorithms with trust scoring systems, we aim to establish a robust security posture that dynamically adapts to emerging threats. The core of our work lies in the development of a predictive model that utilizes deep learning techniques to analyze network behaviors and user interactions, facilitating the early detection of anomalous activities indicative of malicious intent. Through extensive simulation and real-world application, our results demonstrate a significant improvement in detection rates and a reduction in false positives, thereby fortifying the trust and reliability of 5 G social networks. This study not only contributes to the advancement of secure network architectures but also sets a new precedent for the application of AI in cybersecurity, offering a scalable and efficient solution to safeguard against sophisticated attackers.