A novel mobile and vehicular Ad Hoc cloud framework for intursion detection. (c2017)

Abstract

As the usage of smart-devices is increasing, security threats affecting the confidentiality, integrity, and privacy of such devices are briskly emerging due to the rapid growth of malware. Mobile security suites exist to defend devices against malware and other intrusions. However, they require extensive resources which is a constraint of the device itself. In this thesis, we address the problem of intrusion detection for both smartdevices and vehicles while taking into account the devices’ limited resources such as energy, CPU usage, and Internet connectivity via intelligent offloading. We provide a mobile and vehicular ad-hoc cloud based intrusion detection framework that takes advantage of Wi-Fi Direct to allow connectivity, sharing resources, and integrating security as a service with or without the availability of an Internet connection. The frame-framework leverages intelligent offloading via an Intelligent Offloading Distributor module that outputs the optimal offloading decision and distribution. The experiments demonstrate various improvements achieved by our framework. Based on our IOD module, our approach is capable of significantly reducing energy consumption, execution time, and number of selected computational nodes.