Forensic Analysis of WhatsApp Messenger on Android Smartphones

We present the forensic analysis of the artifacts left on Android devices by \textit{WhatsApp Messenger}, the client of the WhatsApp instant messaging system. We provide a complete description of all the artifacts generated by WhatsApp Messenger, we discuss the decoding and the interpretation of each one of them, and we show how they can be correlated together to infer various types of information that cannot be obtained by considering each one of them in isolation. By using the results discussed in this paper, an analyst will be able to reconstruct the list of contacts and the chronology of the messages that have been exchanged by users. Furthermore, thanks to the correlation of multiple artifacts, (s)he will be able to infer information like when a specific contact has been added, to recover deleted contacts and their time of deletion, to determine which messages have been deleted, when these messages have been exchanged, and the users that exchanged them.Comment: (c)2014. This manuscript version is made available under the CC-BY-NC-ND 4.0 license http://creativecommons.org/licenses/by-nc-nd/4.0

Mining Individual Behavior Pattern Based on Semantic Knowledge Discovery of Trajectory

This paper attempts to mine the hidden individual behavior pattern from the raw users’ trajectory data. Based on DBSCAN, a novel spatio-temporal data clustering algorithm named Speed-based Clustering Algorithm was put forward to find slow-speed subtrajectories (i.e., stops) of the single trajectory that the user stopped for a longer time. The algorithm used maximal speed and minimal stopping time to compute the stops and introduced the quantile function to estimate the value of the parameter, which showed more effectively and accurately than DBSCAN and certain improved DBSCAN algorithms in the experimental results. In addition, after the stops are connected with POIs that have the characteristic of an information presentation, the paper designed a POI-Behavior Mapping Table to analyze the user’s activities according to the stopping time and visiting frequency, on the basis of which the user’s daily regular behavior pattern can be mined from the history trajectories. In the end, LBS operators are able to provide intelligent and personalized services so as to achieve precise marketing in terms of the characteristics of the individual behavior.</p

Introductory Computer Forensics

INTERPOL (International Police) built cybercrime programs to keep up with emerging cyber threats, and aims to coordinate and assist international operations for ?ghting crimes involving computers. Although signi?cant international efforts are being made in dealing with cybercrime and cyber-terrorism, ?nding effective, cooperative, and collaborative ways to deal with complicated cases that span multiple jurisdictions has proven dif?cult in practic

Usefulness of commercially available GPS data-loggers for tracking human movement and exposure to dengue virus

<p>Abstract</p> <p>Background</p> <p>Our understanding of the effects of human movement on dengue virus spread remains limited in part due to the lack of precise tools to monitor the time-dependent location of individuals. We determined the utility of a new, commercially available, GPS data-logger for long-term tracking of human movements in Iquitos, Peru. We conducted a series of evaluations focused on GPS device attributes key to reliable use and accuracy. GPS observations from two participants were later compared with semi-structured interview data to assess the usefulness of GPS technology to track individual mobility patterns.</p> <p>Results</p> <p>Positional point and line accuracy were 4.4 and 10.3 m, respectively. GPS wearing mode increased spatial point error by 6.9 m. Units were worn on a neck-strap by a carpenter and a moto-taxi driver for 14-16 days. The application of a clustering algorithm (I-cluster) to the raw GPS positional data allowed the identification of locations visited by each participant together with the frequency and duration of each visit. The carpenter moved less and spent more time in more fixed locations than the moto-taxi driver, who visited more locations for a shorter period of time. GPS and participants' interviews concordantly identified 6 common locations, whereas GPS alone identified 4 locations and participants alone identified 10 locations. Most (80%) of the locations identified by participants alone were places reported as visited for less than 30 minutes.</p> <p>Conclusion</p> <p>The present study demonstrates the feasibility of a novel, commercially available GPS data-logger for long-term tracking of humans and shows the potential of these units to quantify mobility patterns in relationship with dengue virus transmission risk in a tropical urban environment. Cost, battery life, size, programmability and ease of wear are unprecedented from previously tested units, proving the usefulness of GPS-dataloggers for linking movement of individuals and transmission risk of dengue virus and other infectious agents, particularly in resource-poor settings.</p

Using Volunteer Tracking Information for Activity-Based Travel Demand Modeling and Finding Dynamic Interaction-Based Joint-Activity Opportunities

Technology used for real-time locating is being used to identify and track the movements of individuals in real time. With the increased use of mobile technology by individuals, we are now able to explore more potential interactions between people and their living environment using real-time tracking and communication technologies. One of the potentials that has hardly been taken advantage of is to use cell phone tracking information for activity-based transportation study. Using GPS-embedded smart phones, it is convenient to continuously record our trajectories in a day with little information loss. As smart phones get cheaper and hence attract more users, the potential information source for self-tracking data is pervasive. This study provides a cell phone plus web method that collects volunteer cell phone tracking data and uses an algorithm to identify the allocation of activities and traveling in space and time. It also provides a step that incorporates user-participated prompted recall attribute identification (travel modes and activity types) which supplements the data preparation for activity-based travel demand modeling. Besides volunteered geospatial information collection, cell phone users’ real-time locations are often collected by service providers such as Apple, AT&T and many other third-party companies. This location data has been used in turn to boost new location-based services. However, few applications have been seen to address dynamic human interactions and spatio-temporal constraints of activities. This study sets up a framework for a new kind of location-based service that finds joint-activity opportunities for multiple individuals, and demonstrates its feasibility using a spatio-temporal GIS approach