Critical Evaluation of Data Privacy and Security Threats in Federated Learning: Issues and Challenges Related to Privacy and Security in IoT

Abstract

Data security and privacy received a great deal of researchattention recently, as privacy protection becoming a key factor inthe development of artificial intelligence based IOTs. The End-to-End VPN security has an essential role especially in connectingsmart objects in the Internet of Things (IoT) environments. It noted that security is a crucial issue in the End-to-End VPNapproach. Theapplication of Machine Learning (ML) techniques to thewell-known intrusion detection systems (IDS) is key to copewithincreasingly sophisticated cybersecurity attacks throughaneffective and efficient detection process. This paper providesacomprehensive exploration of Virtual Private Network (VPN) technologies, emphasizing their importance in modern networkingfor ensuring secure communication over untrusted networks likethe internet. VPNs have evolved significantly, addressingthegrowing need for data protection in both personal andenterprisecontexts. This study delves into various VPN protocols suchasPPTP, L2TP/IPsec, OpenVPN, IKEv2/IPsec, and WireGuard, evaluating their security mechanisms, strengths, and vulnerabilities. The paper also examines the emerging challenges facingVPNs, including advanced cyber threats and the impact of evolvingtechnologies such as quantum computing. Furthermore, thestudyhighlights future directions, such as integrating AI for dynamicthreat detection and developing quantum-resistant VPNprotocols. Through this analysis, the aim is to provide actionable insights into optimizing VPN usage for enhanced network security inanincreasingly complex digital landscape.

Keywords: VPN, Security Protocols, Encryption, OpenVPN, IKEv2/IPsec, WireGuard, Quantum Computing