The Utilization of Mobile Technology for Crime Scene Investigation in the San Francisco Bay Area

The research presented aims to explore factors affecting the decision to adopt a mobile crime scene investigation application in police departments throughout the San Francisco Bay Area. To accomplish this goal, the mobile technology acceptance model was used in designing a survey for data collection. This model utilizes four categories to interpret the factors that influence a police officer’s decision to accept or reject mobile technologies: performance, security and reliability, management style, and cognitive acceptance. Nine police departments were sampled through a series of in-person and over-the-phone interviews to obtain data regarding factors affecting the adoption of a mobile crime scene investigation application. Results suggest that if a mobile crime scene investigation application were made available, a vast majority of the police departments in the Bay Area would implement this new technology

A Forensically Sound Adversary Model for Mobile Devices

In this paper, we propose an adversary model to facilitate forensic investigations of mobile devices (e.g. Android, iOS and Windows smartphones) that can be readily adapted to the latest mobile device technologies. This is essential given the ongoing and rapidly changing nature of mobile device technologies. An integral principle and significant constraint upon forensic practitioners is that of forensic soundness. Our adversary model specifically considers and integrates the constraints of forensic soundness on the adversary, in our case, a forensic practitioner. One construction of the adversary model is an evidence collection and analysis methodology for Android devices. Using the methodology with six popular cloud apps, we were successful in extracting various information of forensic interest in both the external and internal storage of the mobile device