Fabio Di Troia

San Jose State University

Affiliations

College of Science

Department Of Computer Science

San Jose State University

NoneFabio Di Troia is currently serving as a College Of Science faculty at San Jose State University.

Honors

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Experiences

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Education

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Fabio Di Troia

San Jose State University

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computer science computer security data science malware analysis software analysis information forensics threat detection data mining program analysis machine learning research malware detection information security machine learning data security clustering pattern recognition content analysis online information computational intelligence static program analysis static checking firmware detection evasion technique hidden markov models journalism infectious diseases multimedia retrieval web search programming languages system software mobile computing financial cryptography blockchain economic dynamics social simulation world wide web support vector machines unsupervised machine learning intrusion detection systems software security anti-virus technique blockchain privacy blockchain security blockchain protocols botnet detection financial network analysis financial time series analysis social network security chatbots markov kernel hidden markov model markov chain monte carlo structured prediction sequential learning hybrid analysis malware classification static analysis javascript malware autocorrelation analysis twitter-based botnet gradient descent hmm training

Notable Scholarly Works

Notable Federal Grants

Collaborators

A Comparative Study of Linear and Non-Linear Dimensionality Reduction for Opcode-Frequency Malware Classification. Journal of Computer Virology and Hacking Techniques (2026)

Classifier-Guided Discrete Diffusion for Malware Family-Conditional Graph Generation. Research Square (2026)

Generating Synthetic Malware Samples Using Generative AI. arXiv (Cornell University) (2026)

A Comparative Analysis of Shap and Lime in Detecting Malicious Urls. (2025)

An Empirical Analysis of Hidden Markov Models With Momentum. (2025)

Andrew MillerFabio Di TroiaMark Stamp

computer scienceapplied probabilitystatistical methodologyhidden markov modelsmarkov kernelmachine learning researchstochastic processcomputational statisticshidden markov modelprobability theorystatistical inferencemarkov chain monte carlostatistical models

Applying Word Embeddings and Graph Neural Networks for Effective Malware Classification. (2025)

Comparing Balancing Techniques for Malware Classification. (2025)

Context-Aware Natural Language Processing for Malware Detection. (2025)

Discriminative Regions and Adversarial Sensitivity in CNN-Based Malware Image Classification. Electronics (2025)

Embedding-Driven Synthetic Malware Generation With Autoencoders and Cluster-Tangent Diffusion. Applied Sciences (2025)