Thesis (Ph.D.)--University of Melbourne, Dept. of Computer Science and Software Engineering, 2004. / Typescript (photocopy). Includes bibliographical references (leaves 247-263).

Thesis (B.A.)--Haverford College, Dept. of Computer Science, 2009. / Includes bibliographical references.

Thesis (MArch (Prof)) -- University of Pretoria, 2008. / Abstract in English. Includes bibliographical references.

Thesis (MS)--University of Montana, 2009. / Title from author supplied metadata. Contents viewed on April 7, 2010. Includes bibliographical references.

Thesis (Ph. D.)--Rutgers University, 2008. / "Graduate Program in Communication, Information and Library Studies." Includes bibliographical references (p. 181-190).

Thesis (Ph. D.)--University of Canterbury, 2009. / Typescript (photocopy). Includes bibliographical references (p. 287-318). Also available via the World Wide Web.

Thesis (M.S.)--State University of New York at Binghamton, Department of Computer Science, 2004. / Includes bibliographical references.

Search goals of users of information retrieval systems have commonly been assumed to be static and well-formed. However, a significant amount of goal redefinition is detected in the studies described. A pilot study examined user behaviour at a library OPAC, showing that search results would quite frequently induce users to reconsider and revise their search goals, sometimes following up with a new search based on this revision (labeled "strong" goal redefinition). The main analysis employed transaction logs from the OCLC FirstSearch service, investigating what factors, if any, might affect the amount of goal redefinition that takes place during a search session. To this end, ten hypotheses were proposed and considered. Within each search session, logged queries were coded according to their conceptual differences or similarities, in order for indices of strong goal redefinition to be constructed: a chronological content analysis was thus performed on the transaction logs. The indices of redefinition for search sessions on different FirstSearch databases were compared. It was found that different databases induced goal redefinition to different extents. Further analysis showed that the metadata displayed by a database appeared to affect the amount of goal redefinition, and that the presence of abstracts in results was a positive factor, as was the presence of descriptors and identifiers, perhaps because of the former's hyperlinking nature on the FirstSearch interface. On the other hand, no evidence was found to indicate that abstract length has much of an effect on goal redefinition, nor hit rate or levels of precision and recall. Of the two indices of redefinition that were produced, the "refined" index showed signs of greater precision. Implications of the findings are discussed. It is suggested that goal redefinition should be considered a positive result of system feedback, and that systems should readily allow users to follow up on redefined goals. Abstracts and summaries of documents should be presented to the user as frequently as possible, and hyperlinks from key terms in the metadata should also be created to assist evolving searches. The importance of how system feedback is encountered by the user is emphasized in a new model of information retrieval, which embraces the nonconscious as part of the "cognitive viewpoint," allowing for nonconscious information wants to enter into a user's consciousness through cues encountered during the scanning of search results, triggering a redefinition of search goal. This thesis paves the way for a considerable amount of potentially important research, including: further testing and development of the index of goal redefinition; deeper transaction log analyses, perhaps using screen recorders, examining semantic content and contextualizing at the level of the query; and further identification and analysis of the factors affecting goal redefinition, across different types of information retrieval system.

Inefficiencies associated with online information search are becoming increasingly prevalent in digital environments due to a surge in Consumer Generated Content (CGC). Despite growing scholarly interest in investigating users' information search behavior in CGC environments, there is a paucity of studies that explores the phenomenon from a theory-guided angle. Drawing on Information Foraging Theory (IFT), we re-conceptualize online information search as a form of adaptive user behavior in response to system design constraints. Through this theoretical lens, we advance separate taxonomies for online information search tactics and strategies, both of which constitute essential building blocks of the search process. Furthermore, we construct a research framework that bridges the gap between online information search tactics and strategies by articulating how technology-enabled search tactics contribute to the fulfillment of strategic search goals. We validate our research framework via an online experiment by recruiting participants from Amazon Mechanical Turk (AMT). Participants were tasked to perform searches on custom-developed online review websites, which were modeled after a popular online review website and populated with real restaurant review data. Empirical findings reveal that the provision of different search features indeed engenders distinct search tactics, thereby allowing users varying levels of search determination control and search manipulation control. In turn, both types of search controls affects users' result anticipation and search costs, which when combined, determine the efficiency of goal-oriented search strategy and the utility of exploratory search strategy. This study provides valuable insights that can guide future research and practice.

Today’s smart phones are used for wider range of activities. This extended range of functionalities has also seen the infiltration of new security threats. Android has been the favorite target of cyber criminals. The malicious parties are using highly stealthy techniques to perform the targeted operations, which are hard to detect by the conventional signature and behaviour based approaches. Additionally, the limited resources of mobile device are inadequate to perform the extensive malware detection tasks. Impulsively emerging Android malware merit a robust and effective malware detection solution. In this thesis, we present the PIndroid ― a novel Permissions and Intents based framework for identifying Android malware apps. To the best of author’s knowledge, PIndroid is the first solution that uses a combination of permissions and intents supplemented with ensemble methods for malware detection. It overcomes the drawbacks of some of the existing malware detection methods. Our goal is to provide mobile users with an effective malware detection and prevention solution keeping in view the limited resources of mobile devices and versatility of malware behavior. Our detection engine classifies the apps against certain distinguishing combinations of permissions and intents. We conducted a comparative study of different machine learning algorithms against several performance measures to demonstrate their relative advantages. The proposed approach, when applied to 1,745 real world applications, provides more than 99% accuracy (which is best reported to date). Empirical results suggest that the proposed framework is effective in detection of malware apps including the obfuscated ones. In this thesis, we also present AndroPIn—an Android based malware detection algorithm using Permissions and Intents. It is designed with the methodology proposed in PInDroid. AndroPIn overcomes the limitation of stealthy techniques used by malware by exploiting the usage pattern of permissions and intents. These features, which play a major role in sharing user data and device resources cannot be obfuscated or altered. These vital features are well suited for resource constrained smartphones. Experimental evaluation on a corpus of real-world malware and benign apps demonstrate that the proposed algorithm can effectively detect malicious apps and is resilient to common obfuscations methods. Besides PInDroid and AndroPIn, this thesis consists of three additional studies, which supplement the proposed methodology. First study investigates if there is any correlation between permissions and intents which can be exploited to detect malware apps. For this, the statistical significance test is applied to investigate the correlation between permissions and intents. We found statistical evidence of a strong correlation between permissions and intents which could be exploited to detect malware applications. The second study is conducted to investigate if the performance of classifiers can further be improved with ensemble learning methods. We applied different ensemble methods such as bagging, boosting and stacking. The experiments with ensemble methods yielded much improved results. The third study is related to investigating if the permissions and intents based system can be used to detect the ever challenging colluding apps. Application collusion is an emerging threat to Android based devices. We discuss the current state of research on app collusion and open challenges to the detection of colluding apps. We compare existing approaches and present an integrated approach that can be used to detect the malicious app collusion.