Critical Evaluation of Data Privacy and Security Threats: An Intelligent Federated Learning-based Intrusion Detection System Poisoning Attack and Defense for Cyber-Physical Systems its Issues and Challenges Related to Privacy and Security in IoT

Abstract

Federated learning is a distributed learning method used to solve data silos and privacy

protection in machine learning, aiming to train global models together via multiple clients without sharing data.The rapid evolution of cyber threats poses significant challenges to modern cybersecurity systems and their associated legal frameworks. This paper addresses the problem of increasingly sophisticated breach methods that outpace traditional defense mechanisms. Data security and privacy received a great deal of research attention recently, as privacy protection becoming a key factor in the development of artificial intelligence based IOTs. The End-to-End VPN security has an essential role especially in connecting smart objects in the Internet of Things (IoT) environments. It noted that security is a crucial issue in the End-to-End data security approach. The application of Machine Learning (ML) techniques to the well-known intrusion detection systems (IDS) is key to cope with increasingly sophisticated cybersecurity attacks through an effective and efficient detection process.  This paper provides a comprehensive exploration of Virtual Private Network (VPN) technologies, emphasizing their importance in modern networking for ensuring secure communication over untrusted networks like the internet. VPNs have evolved significantly, addressing the growing need for data protection in both personal and enterprise contexts. This study delves into various data security protocols such as PPTP, L2TP/IPsec, OpenVPN, IKEv2/IPsec, and WireGuard, evaluating their security mechanisms, strengths, and vulnerabilities. The paper also examines the emerging challenges facing VPNs, including advanced cyber threats and the impact of evolving technologies such as quantum computing. Furthermore, the study highlights future directions, such as integrating AI for dynamic threat detection and developing quantum-resistant VPN protocols. Through this analysis, the aim is to provide actionable insights into optimizing VPN usage for enhanced network security in an increasingly complex digital landscape.