As ubiquitous computing (ubicomp) technologies reach maturity, smart phones and context-based services are gaining mainstream popularity. A smart phone accompanies its user throughout (nearly) all aspects of his life, becoming an indispensable assistant the busy user relies on to help navigate his life, using map applications to navigate the physical world, email and instant messaging applications to keep in touch, media player applications to be entertained, etc. As a smart phone is capable of sensing the physical and virtual context of the user with an array of “hard” sensors (e.g., GPS, accelerometer) and “soft” sensors (e.g., email, social network, calendar),it is well-equipped to tailor the assistance it provides to the user. Over the life of a smart phone, it is entrusted with an enormous amount of personal information, everything from context-information sensed by the phone to contact lists to call-logs to passwords. Based on this rich set of information it is possible to model the behavior of the user, and use the models to detect anomalies (i.e., significant variations) in the user’s behavior. Anomaly detection capabilities enable a variety of application domains such as device theft detection, improved authentication mechanisms, impersonation, prevention, physical emergency detection, remote elder-care monitoring, and other proactive services. There has been extensive prior research on anomaly detection in various application domain areas (e.g., fraud detection, intrusion detection). Yet these approaches cannot be used in ubicomp environments as 1) they are very application-specific and not versatile enough to learn complex day to day behavior of users, 2) they work with a very small number of information sources with a relatively uniform stream of information (unlike sensor data from mobile devices), and 3) most approaches require labeled or semi-labeled data about anomalies (in ubicomp environments, it is very costly to create labeled datasets). Existing work in the field of anomaly detection in ubicomp environments is quite sparse. Most of the existing work focuses on using a single sensor information stream (GPS in most cases) to detect anomalies in the user’s behavior. However there exists a somewhat richer vein of prior work in modeling user behavior with the goal of behavior prediction; this is again limited mostly to a single sensor stream or single type of prediction (mostly location). This dissertation presents the notion of modeling mobile user behavior as a collection of models each capturing an aspect of the user’s behavior such as indoor mobility, typing patterns, calling patterns. A novel mechanism is developed for combining these models (i.e.,CobLE), which operate on asynchronous information sources from the mobile device, taking into consider how well each model is estimated to perform in the current context. These ideas are concretely implemented in an extensible framework, McFAD. Evaluations carried out using real-world datasets on this framework in contrast to prior work show that the framework for detecting anomalous behavior, 1) vastly reduces the training data requirement, 2) increases coverage, and 3) dramatically increases performance.
| Identifer | oai:union.ndltd.org:cmu.edu/oai:repository.cmu.edu:dissertations-1362 |
| Date | 01 April 2014 |
| Creators | Buthpitiya, Senaka |
| Publisher | Research Showcase @ CMU |
| Source Sets | Carnegie Mellon University |
| Detected Language | English |
| Type | text |
| Format | application/pdf |
| Source | Dissertations |
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