The design of secure embedded VPN gateway

Han, Kun Hee; Liu, Junjie; Yang, D; Yuan, Quan

doi:10.1109/wartia.2014.6976267

Cited by 2 publications

(1 citation statement)

References 5 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The physical connection of the VPN channels is different from the logical one: in this direction the overlay network level can be distinguished from the physical network level [3]. Firewall and VPN gateway drop-in-router solutions can be embedded into VPN channels [4] allowing the secure management of network elements as routers, hubs and switches using encrypted tunnels. One of the most important aspects concerning the validation of a new cybersecurity model is the evaluation of network performance: the server throughput represents the transmission capacity and could provide important indications about the network loaded by safety elements [5].…”

Section: Introductionmentioning

confidence: 99%

Prototype Cross Platform Oriented on Cybersecurity, Virtual Connectivity, Big Data and Artificial Intelligence Control

Massaro¹,

Gargaro²,

Dipierro³

et al. 2020

IEEE Access

View full text Add to dashboard Cite

This paper describes a prototype cross platform based on intelligent switching of Virtual Private Network (VPN) communications by means of artificial intelligence algorithms able to identify and classify attack risks in self-learning mode by analysing the traffic logs of the system. The platform is also suitable for disaster recovery, data migration and ensures virtualization of communications between nodes in case of risk detection. In order to test the models and evaluate the accuracy of the AI algorithms for risk detection and classification, a number of cyberattack scenario have been simulated. The proposed platform implements Cassandra Big Data system interfacing with supernodes enabling data migration, security and disaster recovery. By comparing the performance of different AI algorithms, the results show that a XGBoost-based algorithm is the most efficient and accurate method for cyberattacks prevention, showing a remarkable ability of classifying and identifying characteristic patterns of the most representative traffic log variables. The research work has been carried out within the framework of a research industry project.

show abstract

Section: Introductionmentioning

confidence: 99%