Real-Time Threat Detection in Mobile Networks Using an Adaptive AI-Based Firewall Framework

Abstract

The rapid growth of cyber landscapes and the development of a new cybersecurity model incorporating PET, deep learning, fuzzy systems, keystroke dynamic authentication, and encryption. It is used to prevent attacks by malware or unauthorized access to cloud systems. The proposed framework, which integrates an artificial intelligence (AI)-driven approach with identity and access management (IAM), enables the adaptive implementation of risk-based login authentication and real time anomaly detection. Unlike conventional security systems that depend on fixed rules and signatures, we provide more sophisticated solutions. A Floydel firewall is dynamically tailored through deep neural networks (DNNs) and automatically adjusts to fluctuating traffic patterns. It employs malware classification based on behavior, utilizes fuzzy logic to manage uncertainty during intrusions, and uses keystroke dynamics for user verification through typing patterns. The experiment demonstrates a 97.6% detection accuracy on benchmark data, while significantly reducing false positives and ensuring data confidentiality through encryption. The nature of cloud security can evolve based on the specific circumstances and threats we encounter. Looking ahead, we plan to delve into cryptography and distributed training to bolster decentralized infrastructures. This proposed framework aims to fortify data protection and ensure user privacy in essential areas like healthcare, financial services, and e-governance, thereby fostering increased trust.