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dc.contributor.advisorMemon, Atifen_US
dc.contributor.authorMilton, Leslie Carrieen_US
dc.date.accessioned2016-06-22T05:33:51Z
dc.date.available2016-06-22T05:33:51Z
dc.date.issued2015en_US
dc.identifierdoi:10.13016/M2GR3Q
dc.identifier.urihttp://hdl.handle.net/1903/18133
dc.description.abstractAuthentication plays an important role in how we interact with computers, mobile devices, the web, etc. The idea of authentication is to uniquely identify a user before granting access to system privileges. For example, in recent years more corporate information and applications have been accessible via the Internet and Intranet. Many employees are working from remote locations and need access to secure corporate files. During this time, it is possible for malicious or unauthorized users to gain access to the system. For this reason, it is logical to have some mechanism in place to detect whether the logged-in user is the same user in control of the user's session. Therefore, highly secure authentication methods must be used. We posit that each of us is unique in our use of computer systems. It is this uniqueness that is leveraged to "continuously authenticate users" while they use web software. To monitor user behavior, n-gram models are used to capture user interactions with web-based software. This statistical language model essentially captures sequences and sub-sequences of user actions, their orderings, and temporal relationships that make them unique by providing a model of how each user typically behaves. Users are then continuously monitored during software operations. Large deviations from "normal behavior" can possibly indicate malicious or unintended behavior. This approach is implemented in a system called Intruder Detector (ID) that models user actions as embodied in web logs generated in response to a user's actions. User identification through web logs is cost-effective and non-intrusive. We perform experiments on a large fielded system with web logs of approximately 4000 users. For these experiments, we use two classification techniques; binary and multi-class classification. We evaluate model-specific differences of user behavior based on coarse-grain (i.e., role) and fine-grain (i.e., individual) analysis. A specific set of metrics are used to provide valuable insight into how each model performs. Intruder Detector achieves accurate results when identifying legitimate users and user types. This tool is also able to detect outliers in role-based user behavior with optimal performance. In addition to web applications, this continuous monitoring technique can be used with other user-based systems such as mobile devices and the analysis of network traffic.en_US
dc.language.isoenen_US
dc.titleUser Behavioral Modeling of Web-based Systems for Continuous User Authenticationen_US
dc.typeDissertationen_US
dc.contributor.publisherDigital Repository at the University of Marylanden_US
dc.contributor.publisherUniversity of Maryland (College Park, Md.)en_US
dc.contributor.departmentComputer Scienceen_US
dc.subject.pqcontrolledComputer scienceen_US
dc.subject.pquncontrolledauthenticationen_US
dc.subject.pquncontrolledbehavioral modelingen_US
dc.subject.pquncontrolledcontinuous authenticationen_US
dc.subject.pquncontrolledintrusion detectionen_US
dc.subject.pquncontrolledn-gramsen_US
dc.subject.pquncontrolledsoftware securityen_US


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