Attack detection for healthcare monitoring systems using mechanical learning in virtual private networks over optical transport layer architecture
Chronopoulos, Spyridon K.
Tafiadis, Dionysios C.
Christofilakis, Vasilis N.
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Data security plays a crucial role in healthcare monitoring systems, since critical patient information is transacted over the Internet, especially through wireless devices, wireless routes such as optical wireless channels, or optical transport networks related to optical fibers. Many hospitals are acquiring their own metro dark fiber networks for collaborating with other institutes as a way to maximize their capacity to meet patient needs, as sharing scarce and expensive assets, such as scanners, allows them to optimize their efficiency. The primary goal of this article is to develop of an attack detection model suitable for healthcare monitoring systems that uses internet protocol (IP) virtual private networks (VPNs) over optical transport networks. To this end, this article presents the vulnerabilities in healthcare monitoring system networks, which employ VPNs over optical transport layer architecture. Furthermore, a multilayer network architecture for closer integration of the IP and optical layers is proposed, and an application for detecting DoS attacks is introduced. The proposed application is a lightweight implementation that could be applied and installed into various remote healthcare control devices with limited processing and memory resources. Finally, an analytical and focused approach correlated to attack detection is proposed, which can also serve as a tutorial oriented towards even nonprofessionals for practical and learning purposes.