Open Access. Powered by Scholars. Published by Universities.®

Digital Commons Network™

Open Access. Powered by Scholars. Published by Universities.®

2025

Discipline
Institution
Keyword
Publication
Publication Type
File Type

Articles 25621 - 25627 of 25627

Full-Text Articles in Entire DC Network

Comparative Analysis Of Embedding Techniques With Clustering Algorithms For Malware Opcodes, Ayush Koul Jan 2025

Comparative Analysis Of Embedding Techniques With Clustering Algorithms For Malware Opcodes, Ayush Koul

Master's Projects

Malware detection and classification remain critical challenges in cybersecurity, especially as malicious software becomes increasingly sophisticated and prevalent. While much of the work involving embeddings has traditionally relied on supervised learning approaches, there is significant potential in leveraging unsupervised learning techniques to discern hidden structures in malware data. By employing embedding techniques to convert malware samples into high-dimensional vector representations, we can capture the subtle and complex patterns inherent in malicious code without relying on pre-labeled data. This unsupervised approach helps categorize malware into predefined malware families, greatly aiding in developing cybersecurity solutions. In contrast to traditional supervised models that …

Go to article

Synthetic Malware Generation Using Generative Ai, Phanidhar Sai Sravan Chandana Jan 2025

Synthetic Malware Generation Using Generative Ai, Phanidhar Sai Sravan Chandana

Master's Projects

Malware grows in numbers and complexity, evading conventional signature-and anomaly-based defenses and worsening extreme data sparsity and class imbalance problems for machine learning based detection. Generative models, specifically GANs conditioned on contextual embeddings like BERT have proved effective augmenting training corpora to improve classifier accuracy, but these approaches have largely produced family-specific samples In this paper, we propose a generalized augmentation scheme for generating robust malware embeddings for various families. We begin by extracting opcode sequences from 13 malware families and encoding them into three embedding methods: CountVectorizer, TF-IDF, and BERT’s ‘[CLS]‘ vectors. We therefore train standard GANs and Wasserstein …

Go to article

Comparative Analysis Of Adversarial Permeability In Cpu-Native, Qat And Onnx-Based Quantized Transformer Models, Fahad Siddiqui Jan 2025

Comparative Analysis Of Adversarial Permeability In Cpu-Native, Qat And Onnx-Based Quantized Transformer Models, Fahad Siddiqui

Master's Projects

This work explores securing and optimization of Transformer-based time series forecasting models. We employ several quantization techniques, including quantization-aware training (QAT), and tested the robustness of quantized models by adversarially attacking them. The preliminary results of this work, in our controlled setup, indicate that quantized models outperform the full precision model in terms of robustness against adversarial attacks. They achieved this robustness while showing a very minimal decrease in their forecasting performance.

Go to article

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan Jan 2025

Large Language Models For Bacterial Genomic Analysis, Manvendra Chavan

Master's Projects

Identification of bacterial gene sequences with agricultural applications has the potential to transform agricultural biotechnology. These genes can be used in environmentally friendly pest control strategies. One such use case is identifying genes with potential insecticidal properties. With an increasing number of genomic information and decreasing numbers of available annotated sequences, finding new insecticidal genes has become more challenging.The traditional methods relying on sequence alignment and annotated databases are not effective in detecting functionally relevant genes lacking close homology to known cases. This project investigates the data-driven classification of genes by sequence modeling. This research is focused on learning DNA …

Go to article

Genegate: Genetic Gating In A Mixture-Of-Experts For Real-Time Multi-Objective Traffic Signal Control, Rashmi Vishwanath Bhat Jan 2025

Genegate: Genetic Gating In A Mixture-Of-Experts For Real-Time Multi-Objective Traffic Signal Control, Rashmi Vishwanath Bhat

Master's Projects

Urban traffic signal control often needs to juggle between competing goals. It needs to minimize delays, reduce emissions, prevent crashes, and prioritize emergency vehicles all while the demand is constantly fluctuating. Traditional fixed-time or statically blended policies cannot reallocate priorities quickly when conditions change. We introduce GeneGate, a mixture-of-experts framework that uses a lightweight genetic gate to fuse four specialist controllers (throughput, emissions, safety, emergency) and adjusts their weights in real time. A short offline genetic search produces a robust initial blend, and an online micro-evolution step refines it every few cycles based on live traffic feedback. GeneGate’s adaptive gating …

Go to article

Abstractions Of The Game “Set”, Martin Grant Jan 2025

Abstractions Of The Game “Set”, Martin Grant

Master's Projects

Each card in the game of SET can be represented as a point in Z43, where Z3 is the f ield of 3 elements; an in-game position without any SETs can be represented as a cap set. We find the largest cap sets in Zn 3 for n ≤ 4 and prove their uniqueness. Then, we provide more insight into Ellenberg and Gijswijt’s proof of upper bound for the maximum size of cap set in Fnq, where Fq is the field of q elements, which they find to be o(cn …

Go to article

Overview Of The Head And Neck Tumor Segmentation For Magnetic Resonance Guided Applications (Hnts-Mrg) 2024 Challenge, Kareem A Wahid, Cem Dede, Dina M El-Habashy, Serageldin Kamel, Michael K Rooney, Yomna Khamis, Moamen R A Abdelaal, Sara Ahmed, Kelsey L Corrigan, Enoch Chang, Stephanie O Dudzinski, Travis C Salzillo, Brigid A Mcdonald, Samuel L Mulder, Lucas Mccullum, Qusai Alakayleh, Carlos Sjogreen, Renjie He, Abdallah S R Mohamed, Stephen Y Lai, John P Christodouleas, Andrew J Schaefer, Mohamed A Naser, Clifton D Fuller Jan 2025

Overview Of The Head And Neck Tumor Segmentation For Magnetic Resonance Guided Applications (Hnts-Mrg) 2024 Challenge, Kareem A Wahid, Cem Dede, Dina M El-Habashy, Serageldin Kamel, Michael K Rooney, Yomna Khamis, Moamen R A Abdelaal, Sara Ahmed, Kelsey L Corrigan, Enoch Chang, Stephanie O Dudzinski, Travis C Salzillo, Brigid A Mcdonald, Samuel L Mulder, Lucas Mccullum, Qusai Alakayleh, Carlos Sjogreen, Renjie He, Abdallah S R Mohamed, Stephen Y Lai, John P Christodouleas, Andrew J Schaefer, Mohamed A Naser, Clifton D Fuller

Faculty, Staff and Student Publications

Magnetic resonance (MR)-guided radiation therapy (RT) is enhancing head and neck cancer (HNC) treatment through superior soft tissue contrast and longitudinal imaging capabilities. However, manual tumor segmentation remains a significant challenge, spurring interest in artificial intelligence (AI)-driven automation. To accelerate innovation in this field, we present the Head and Neck Tumor Segmentation for MR-Guided Applications (HNTS-MRG) 2024 Challenge, a satellite event of the 27th International Conference on Medical Image Computing and Computer Assisted Intervention. This challenge addresses the scarcity of large, publicly available AI-ready adaptive RT datasets in HNC and explores the potential of incorporating multi-timepoint data to enhance RT …

Go to article

First Prev