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Knowledge Modeling Of Phishing Emails, Courtney Falk
Knowledge Modeling Of Phishing Emails, Courtney Falk
Open Access Dissertations
This dissertation investigates whether or not malicious phishing emails are detected better when a meaningful representation of the email bodies is available. The natural language processing theory of Ontological Semantics Technology is used for its ability to model the knowledge representation present in the email messages. Known good and phishing emails were analyzed and their meaning representations fed into machine learning binary classifiers. Unigram language models of the same emails were used as a baseline for comparing the performance of the meaningful data. The end results show how a binary classifier trained on meaningful data is better at detecting phishing …
Hardware Trojan Detection Via Golden Reference Library Matching, Lucas Weaver
Hardware Trojan Detection Via Golden Reference Library Matching, Lucas Weaver
Graduate Theses and Dissertations
Due to the proliferation of hardware Trojans in third party Intellectual Property (IP) designs, the issue of hardware security has risen to the forefront of computer engineering. Because of the miniscule size yet devastating effects of hardware Trojans, few detection methods have been presented that adequately address this problem facing the hardware industry. One such method with the ability to detect hardware Trojans is Structural Checking. This methodology analyzes a soft IP at the register-transfer level to discover malicious inclusions. An extension of this methodology is presented that expands the list of signal functionalities, termed assets, in addition to introducing …
Exploring Privacy Leakage From The Resource Usage Patterns Of Mobile Apps, Amin Rois Sinung Nugroho
Exploring Privacy Leakage From The Resource Usage Patterns Of Mobile Apps, Amin Rois Sinung Nugroho
Graduate Theses and Dissertations
Due to the popularity of smart phones and mobile apps, a potential privacy risk with the usage of mobile apps is that, from the usage information of mobile apps (e.g., how many hours a user plays mobile games in each day), private information about a user’s living habits and personal activities can be inferred. To assess this risk, this thesis answers the following research question: can the type of a mobile app (e.g., email, web browsing, mobile game, music streaming, etc.) used by a user be inferred from the resource (e.g., CPU, memory, network, etc.) usage patterns of the mobile …