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You searched for +publisher:"Rochester Institute of Technology" +contributor:("Shanchieh J. Yang"). Showing records 1 – 2 of 2 total matches.

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Rochester Institute of Technology

1. Krall, Alexander Leon. Comparing Cyber Defense Alternatives Using Rare-Event Simulation Techniques to Compute Network Risk.

Degree: MS, Industrial and Systems Engineering, 2018, Rochester Institute of Technology

Vulnerabilities inherent in a cyber network can be exploited by individuals with malicious intent. Thus, machines on the network are at risk. Formally, security specialists seek to mitigate the risk of intrusion events through network reconfiguration and defense. Comparison between configuration alternatives may be difficult if an event is sufficiently rare; risk estimates may of be questionable quality making definitive inferences unattainable. Furthermore, that which constitutes a “rare” event can imply different rates of occurrence, depending on network complexity. To measure rare events efficiently without the risk of doing damage to a cyber network, special rare-event simulation techniques can be employed, such as splitting or importance sampling. In particular, importance sampling has shown promise when modeling an attacker moving through a network with intent to steal data. The importance sampling technique amplifies certain aspects of the network in order to cause a rare event to happen more frequently. Output statistics collected under these amplified conditions must then be scaled back to the context of the original network to produce meaningful results. This thesis successfully tailors the importance sampling methodology to scenarios where an attacker must search a network. Said tailoring takes the attacker’s successes and failures as well as the attacker’s targeting choices into account. The methodology is shown to be more computationally efficient and can produce higher quality estimates of risk when compared to standard simulation. Advisors/Committee Members: Michael E. Kuhl, Shanchieh J. Yang, Katie McConky.

Subjects/Keywords: Cyber security; Importance sampling

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Krall, A. L. (2018). Comparing Cyber Defense Alternatives Using Rare-Event Simulation Techniques to Compute Network Risk. (Masters Thesis). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/9778

Chicago Manual of Style (16th Edition):

Krall, Alexander Leon. “Comparing Cyber Defense Alternatives Using Rare-Event Simulation Techniques to Compute Network Risk.” 2018. Masters Thesis, Rochester Institute of Technology. Accessed February 18, 2019. https://scholarworks.rit.edu/theses/9778.

MLA Handbook (7th Edition):

Krall, Alexander Leon. “Comparing Cyber Defense Alternatives Using Rare-Event Simulation Techniques to Compute Network Risk.” 2018. Web. 18 Feb 2019.

Vancouver:

Krall AL. Comparing Cyber Defense Alternatives Using Rare-Event Simulation Techniques to Compute Network Risk. [Internet] [Masters thesis]. Rochester Institute of Technology; 2018. [cited 2019 Feb 18]. Available from: https://scholarworks.rit.edu/theses/9778.

Council of Science Editors:

Krall AL. Comparing Cyber Defense Alternatives Using Rare-Event Simulation Techniques to Compute Network Risk. [Masters Thesis]. Rochester Institute of Technology; 2018. Available from: https://scholarworks.rit.edu/theses/9778

2. Azary, Sherif. Grassmann Learning for Recognition and Classification.

Degree: PhD, Computer Science (GCCIS), 2014, Rochester Institute of Technology

Computational performance associated with high-dimensional data is a common challenge for real-world classification and recognition systems. Subspace learning has received considerable attention as a means of finding an efficient low-dimensional representation that leads to better classification and efficient processing. A Grassmann manifold is a space that promotes smooth surfaces, where points represent subspaces and the relationship between points is defined by a mapping of an orthogonal matrix. Grassmann learning involves embedding high dimensional subspaces and kernelizing the embedding onto a projection space where distance computations can be effectively performed. In this dissertation, Grassmann learning and its benefits towards action classification and face recognition in terms of accuracy and performance are investigated and evaluated. Grassmannian Sparse Representation (GSR) and Grassmannian Spectral Regression (GRASP) are proposed as Grassmann inspired subspace learning algorithms. GSR is a novel subspace learning algorithm that combines the benefits of Grassmann manifolds with sparse representations using least squares loss §¤1-norm minimization for improved classification. GRASP is a novel subspace learning algorithm that leverages the benefits of Grassmann manifolds and Spectral Regression in a framework that supports high discrimination between classes and achieves computational benefits by using manifold modeling and avoiding eigen-decomposition. The effectiveness of GSR and GRASP is demonstrated for computationally intensive classification problems: (a) multi-view action classification using the IXMAS Multi-View dataset, the i3DPost Multi-View dataset, and the WVU Multi-View dataset, (b) 3D action classification using the MSRAction3D dataset and MSRGesture3D dataset, and (c) face recognition using the ATT Face Database, Labeled Faces in the Wild (LFW), and the Extended Yale Face Database B (YALE). Additional contributions include the definition of Motion History Surfaces (MHS) and Motion Depth Surfaces (MDS) as descriptors suitable for activity representations in video sequences and 3D depth sequences. An in-depth analysis of Grassmann metrics is applied on high dimensional data with different levels of noise and data distributions which reveals that standardized Grassmann kernels are favorable over geodesic metrics on a Grassmann manifold. Finally, an extensive performance analysis is made that supports Grassmann subspace learning as an effective approach for classification and recognition. Advisors/Committee Members: Andreas Savakis, Nathan D. Cahill, Shanchieh J. Yang.

Subjects/Keywords: None provided

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APA · Chicago · MLA · Vancouver · CSE | Export to Zotero / EndNote / Reference Manager

APA (6th Edition):

Azary, S. (2014). Grassmann Learning for Recognition and Classification. (Doctoral Dissertation). Rochester Institute of Technology. Retrieved from https://scholarworks.rit.edu/theses/8452

Chicago Manual of Style (16th Edition):

Azary, Sherif. “Grassmann Learning for Recognition and Classification.” 2014. Doctoral Dissertation, Rochester Institute of Technology. Accessed February 18, 2019. https://scholarworks.rit.edu/theses/8452.

MLA Handbook (7th Edition):

Azary, Sherif. “Grassmann Learning for Recognition and Classification.” 2014. Web. 18 Feb 2019.

Vancouver:

Azary S. Grassmann Learning for Recognition and Classification. [Internet] [Doctoral dissertation]. Rochester Institute of Technology; 2014. [cited 2019 Feb 18]. Available from: https://scholarworks.rit.edu/theses/8452.

Council of Science Editors:

Azary S. Grassmann Learning for Recognition and Classification. [Doctoral Dissertation]. Rochester Institute of Technology; 2014. Available from: https://scholarworks.rit.edu/theses/8452

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