Active User Authentication for Smartphones: A Challenge Data Set and Benchmark Results

In this paper, automated user verification techniques for smartphones are investigated. A unique non-commercial dataset, the University of Maryland Active Authentication Dataset 02 (UMDAA-02) for multi-modal user authentication research is introduced. This paper focuses on three sensors - front camera, touch sensor and location service while providing a general description for other modalities. Benchmark results for face detection, face verification, touch-based user identification and location-based next-place prediction are presented, which indicate that more robust methods fine-tuned to the mobile platform are needed to achieve satisfactory verification accuracy. The dataset will be made available to the research community for promoting additional research.Comment: 8 pages, 12 figures, 6 tables. Best poster award at BTAS 201

Using Technology Enabled Qualitative Research to Develop Products for the Social Good, An Overview

This paper discusses the potential benefits of the convergence of three recent trends for the design of socially beneficial products and services: the increasing application of qualitative research techniques in a wide range of disciplines, the rapid mainstreaming of social media and mobile technologies, and the emergence of software as a service. Presented is a scenario facilitating the complex data collection, analysis, storage, and reporting required for the qualitative research recommended for the task of designing relevant solutions to address needs of the underserved. A pilot study is used as a basis for describing the infrastructure and services required to realize this scenario. Implications for innovation of enhanced forms of qualitative research are presented

Human Activity Recognition & Mobily Path Prediction

Individual Mobility is the study that depicts how individuals move inside a region or system. As of late a few researches have been accomplished for this reason and there has been a flood in enormous informational accessible in individual developments. Most of these information’s are gathered from cellphone or potentially GPS with variable accuracy relying upon the distance from the tower. Enormous scope information, for example, cell phone follows are significant hotspot for urban modeling. The individual travel designs breakdown into a solitary likelihood distribution however despite the assorted variety of their travel history people follow basic reproducible examples. This similitude in movement example can help us in an extremely different zones of utilizations, for example, city arranging, traffic building, spread of disease and versatile infections. The motive of this project is to show that by utilizing a measure of direct estimation that human directions do follow a few high reproducible scaling designs. Activity recognition expects to perceive the activities and objectives of at least one operator from a progression of perceptions on the specialists\u27 activities and the natural conditions. Human movement acknowledgment, which is one of the developing fields of research, plans to figure out which action is finished by people. Some true applications, for example, health monitoring, abnormal behavior detection, and sport. In this way, it is a troublesome issue given the enormous number of perceptions delivered each second, the fleeting idea of the perceptions, and the absence of an unmistakable method to relate accelerometer information to known developments. Keen PDAs presently fuse numerous different and ground-breaking sensors, for example, GPS sensors, vision sensors, sound sensors, light sensors, temperature sensors, course sensors and speeding up sensors. This project is about utilizations telephone-based accelerometers to perform activity recognition, which includes identifying the physical movement a user is performing

Understanding Mobile Traffic Patterns of Large Scale Cellular Towers in Urban Environment

Understanding mobile traffic patterns of large scale cellular towers in urban environment is extremely valuable for Internet service providers, mobile users, and government managers of modern metropolis. This paper aims at extracting and modeling the traffic patterns of large scale towers deployed in a metropolitan city. To achieve this goal, we need to address several challenges, including lack of appropriate tools for processing large scale traffic measurement data, unknown traffic patterns, as well as handling complicated factors of urban ecology and human behaviors that affect traffic patterns. Our core contribution is a powerful model which combines three dimensional information (time, locations of towers, and traffic frequency spectrum) to extract and model the traffic patterns of thousands of cellular towers. Our empirical analysis reveals the following important observations. First, only five basic time-domain traffic patterns exist among the 9,600 cellular towers. Second, each of the extracted traffic pattern maps to one type of geographical locations related to urban ecology, including residential area, business district, transport, entertainment, and comprehensive area. Third, our frequency-domain traffic spectrum analysis suggests that the traffic of any tower among the 9,600 can be constructed using a linear combination of four primary components corresponding to human activity behaviors. We believe that the proposed traffic patterns extraction and modeling methodology, combined with the empirical analysis on the mobile traffic, pave the way toward a deep understanding of the traffic patterns of large scale cellular towers in modern metropolis.Comment: To appear at IMC 201

Smartphone Forensic Challenges

Article originally published in Internation Journal of Computer Science and SecurityGlobally, the extensive use of smartphone devices has led to an increase in storage and transmission of enormous volumes of data that could be potentially be used as digital evidence in a forensic investigation. Digital evidence can sometimes be difficult to extract from these devices given the various versions and models of smartphone devices in the market. Forensic analysis of smartphones to extract digital evidence can be carried out in many ways, however, prior knowledge of smartphone forensic tools is paramount to a successful forensic investigation. In this paper, the authors outline challenges, limitations and reliability issues faced when using smartphone device forensic tools and accompanied forensic techniques. The main objective of this paper is intended to be consciousness-raising than suggesting best practices to these forensic work challenges

Context-awareness for mobile sensing: a survey and future directions

The evolution of smartphones together with increasing computational power have empowered developers to create innovative context-aware applications for recognizing user related social and cognitive activities in any situation and at any location. The existence and awareness of the context provides the capability of being conscious of physical environments or situations around mobile device users. This allows network services to respond proactively and intelligently based on such awareness. The key idea behind context-aware applications is to encourage users to collect, analyze and share local sensory knowledge in the purpose for a large scale community use by creating a smart network. The desired network is capable of making autonomous logical decisions to actuate environmental objects, and also assist individuals. However, many open challenges remain, which are mostly arisen due to the middleware services provided in mobile devices have limited resources in terms of power, memory and bandwidth. Thus, it becomes critically important to study how the drawbacks can be elaborated and resolved, and at the same time better understand the opportunities for the research community to contribute to the context-awareness. To this end, this paper surveys the literature over the period of 1991-2014 from the emerging concepts to applications of context-awareness in mobile platforms by providing up-to-date research and future research directions. Moreover, it points out the challenges faced in this regard and enlighten them by proposing possible solutions