ETDs

This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.

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Compression and Regularization of Vision Transformers

Description
Vision Transformers (ViT) achieve state-of-the-art performance on image classification tasks. However, their massive size makes them unsuitable for edge devices. Unlike CNNs, limited research has been conducted on the compression of ViTs. This thesis work proposes the ”adjoined training technique” to compress any transformer based architecture. The architecture, Adjoined Vision…

Vision Transformers (ViT) achieve state-of-the-art performance on image classification tasks. However, their massive size makes them unsuitable for edge devices. Unlike CNNs, limited research has been conducted on the compression of ViTs. This thesis work proposes the ”adjoined training technique” to compress any transformer based architecture. The architecture, Adjoined Vision Transformer (AN-ViT), achieves state-of-the-art performance on the ImageNet classification task. With the base network as Swin Transformer, AN-ViT with 4.1× fewer parameters and 5.5× fewer floating point operations (FLOPs) achieves similar accuracy (within 0.15%). This work further proposes Differentiable Adjoined ViT (DAN-ViT), whichuses neural architecture search to find hyper-parameters of our model. DAN-ViT outperforms the current state-of-the-art methods including Swin-Transformers by about ∼ 0.07% and achieves 85.27% top-1 accuracy on the ImageNet dataset while using 2.2× fewer parameters and with 2.2× fewer FLOPs.
ContributorsGoel, Rajeev (Author) / Yang, Yingzhen (Thesis advisor) / Yang, Yezhou (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2023
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Identification of Compromised Nodes in Collaborative Intrusion Detection Systems for Large Scale Networks Due to Insider Attacks

Description
Large organizations have multiple networks that are subject to attacks, which can be detected by continuous monitoring and analyzing the network traffic by Intrusion Detection Systems. Collaborative Intrusion Detection Systems (CIDS) are used for efficient detection of distributed attacks by having a global view of the traffic events in large…

Large organizations have multiple networks that are subject to attacks, which can be detected by continuous monitoring and analyzing the network traffic by Intrusion Detection Systems. Collaborative Intrusion Detection Systems (CIDS) are used for efficient detection of distributed attacks by having a global view of the traffic events in large networks. However, CIDS are vulnerable to internal attacks, and these internal attacks decrease the mutual trust among the nodes in CIDS required for sharing of critical and sensitive alert data in CIDS. Without the data sharing, the nodes of CIDS cannot collaborate efficiently to form a comprehensive view of events in the networks monitored to detect distributed attacks. The compromised nodes will further decrease the accuracy of CIDS by generating false positives and false negatives of the traffic event classifications. In this thesis, an approach based on a trust score system is presented to detect and suspend the compromised nodes in CIDS to improve the trust among the nodes for efficient collaboration. This trust score-based approach is implemented as a consensus model on a private blockchain because private blockchain has the features to address the accountability, integrity and privacy requirements of CIDS. In this approach, the trust scores of malicious nodes are decreased with every reported false negative or false positive of the traffic event classifications. When the trust scores of any node falls below a threshold, the node is identified as compromised and suspended. The approach is evaluated for the accuracy of identifying malicious nodes in CIDS.
ContributorsYenugunti, Chandralekha (Author) / Yau, Stephen S. (Thesis advisor) / Yang, Yezhou (Committee member) / Zou, Jia (Committee member) / Arizona State University (Publisher)
Created2020