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- University of California, Santa CruzUS74 papers
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9 papers · 1 filter
Software Supply Chain Vulnerabilities Detection in Source Code: Performance Comparison between Traditional and Quantum Machine Learning Algorithms
Mst Shapna Akter, Md Jobair Hossain Faruk, Nafisa Anjum +5
The software supply chain (SSC) attack has become one of the crucial issues that are being increased rapidly with the advancement of the software development domain. In general, SS…
A Comparison of Static, Dynamic, and Hybrid Analysis for Malware Detection
Anusha Damodaran, Fabio Di Troia, Visaggio Aaron Corrado +2
In this research, we compare malware detection techniques based on static, dynamic, and hybrid analysis. Specifically, we train Hidden Markov Models (HMMs ) on both static and dyna…
CNN vs ELM for Image-Based Malware Classification
Mugdha Jain, William Andreopoulos, Mark Stamp
Research in the field of malware classification often relies on machine learning models that are trained on high-level features, such as opcodes, function calls, and control flow g…
Malware Classification with GMM-HMM Models
Jing Zhao, Samanvitha Basole, Mark Stamp
Discrete hidden Markov models (HMM) are often applied to malware detection and classification problems. However, the continuous analog of discrete HMMs, that is, Gaussian mixture m…
Malware Classification Using Long Short-Term Memory Models
Dennis Dang, Fabio Di Troia, Mark Stamp
Signature and anomaly based techniques are the quintessential approaches to malware detection. However, these techniques have become increasingly ineffective as malware has become…
Malware Classification with Word Embedding Features
Aparna Sunil Kale, Fabio Di Troia, Mark Stamp
Malware classification is an important and challenging problem in information security. Modern malware classification techniques rely on machine learning models that can be trained…