Date of Award
Doctor of Philosophy in Computer Science (CISD)
Graduate School of Computer and Information Sciences
James D. Cannady
This work presents a proof of concept of an Unsupervised Learning Trojan. The Unsupervised Learning Trojan presents new challenges over previous work on the Neural network Trojan, since the attacker does not control most of the environment. The current work will presented an analysis of how the attack can be successful by proposing new assumptions under which the attack can become a viable one. A general analysis of how the compromise can be theoretically supported is presented, providing enough background for practical implementation development. The analysis was carried out using 3 selected algorithms that can cover a wide variety of circumstances of unsupervised learning. A selection of 4 encoding schemes on 4 datasets were chosen to represent actual scenarios under which the Trojan compromise might be targeted. A detailed procedure is presented to demonstrate the attack's viability under assumed circumstances. Two tests of hypothesis concerning the experimental setup were carried out which yielded acceptance of the null hypothesis. Further discussion is contemplated on various aspects of actual implementation issues and real world scenarios where this attack might be contemplated.
Arturo Geigel. 2014. Unsupervised Learning Trojan. Doctoral dissertation. Nova Southeastern University. Retrieved from NSUWorks, Graduate School of Computer and Information Sciences. (17)