Over the last decade, the mobile device has become a ubiquitous tool within everyday life. Unfortunately, whilst the popularity of mobile devices has increased, a corresponding increase can also be identified in the threats being targeted towards these devices. Security countermeasures such as AV and firewalls are being deployed; however, the increasing sophistication of the attacks requires additional measures to be taken. This paper proposes a novel behaviour-based profiling technique that is able to build upon the weaknesses of current systems by developing a comprehensive multilevel approach to profiling. In support of this model, a series of experiments have been designed to look at profiling calling, device usage and Bluetooth network scanning. Using neural networks, experimental results for the aforementioned activities' are able to achieve an EER (Equal Error Rate) of: 13.5%, 35.1% and 35.7%.
|Title of host publication||Proceedings - EST 2010 - 2010 International Conference on Emerging Security Technologies, ROBOSEC 2010 - Robots and Security, LAB-RS 2010 - Learning and Adaptive Behavior in Robotic Systems|
|Number of pages||6|
|Publication status||Published - 2010|
|Event||2010 International Conference on Emerging Security Technologies, EST 2010, Robots and Security, ROBOSEC 2010, Learning and Adaptive Behavior in Robotic Systems, LAB-RS 2010 - Canterbury, United Kingdom|
Duration: 6 Sep 2010 → 8 Sep 2010
|Conference||2010 International Conference on Emerging Security Technologies, EST 2010, Robots and Security, ROBOSEC 2010, Learning and Adaptive Behavior in Robotic Systems, LAB-RS 2010|
|Period||6/09/10 → 8/09/10|