A Secure Keyless Entry System Based on Contextual Information

Abstract

Proximity identification has been widely used in access control systems. These systems, in particular Passive Keyless Entry and Start systems (PKES) for high-end automobiles, allow drivers to unlock their vehicles by standing within one meter of the vehicle while carrying a legitimate key fob in their pockets. The PKES systems re¬quire zero interaction from a user since the vehicle is able to detect the presence of the key fob and verify its proximity, thereby allowing the user to have further access to the vehicle. However, due to the restricted processing capacity of the key fobs and the vulnerabilities of RFID technology, these systems may be prone to relay attacks. Some security videos show that thieves executed relay attacks to steal a luxury car in less than one minute. Previous literature findings also demonstrate that conventional PKES systems are vulnerable to attacks that exploit cryptographic techniques such as rolling code scheme and challenge-response scheme.

In this thesis, we propose a Context-based Secure Keyless Entry System (CSKES) that adopts Bluetooth Low Energy (Bluetooth 4.0) wireless communication technology and utilizes a wide range of contextual information including round-trip time, RSSI (Receiving Signal Strength Indicator), GPS (Global Positioning System) coordinates, and Jaccard similarity of WiFi APs (Access Points) in order to precisely identify the close proximity of a car to its corresponding key. This multi-feature proximity identification system is highly efficient to mitigate classic relay attacks. We first evaluate each security feature individually to demonstrate their reliability, stability, and rigidity in identifying the characteristics of the environment. Then we implement the proposed system in two parts: an app on iPhone and a simulation application on the laptop. We evaluate the system performance based on three classification models with a dataset collected from normal and abnormal use cases. The results show that the proposed Context-based Secure Keyless Entry System demonstrates great efficiency in identifying physical proximity and preventing classic relay attacks.