Fabio Di Troia, PhD

Teaching & Mentorship

• Course: Topics in Machine Learning (Graduate level)
• Course: Information Security
• Course: Introduction to Machine Learning
• Mentorship: Advising for MS Thesis and Project students (CS 297/298)
• Mentorship: Mentorship for independent research studies
• Mentorship: Guidance on paper drafting and conference submissions
• Mentorship: Focus on practical implementation and mathematical rigor

Current Research Projects

Publications

Journal Publications

• [2026] Addressing Data Scarcity in Malware Classification via Pixel-Level Synthetic Image Generation
  Electronics (Special Issue: AI in Cybersecurity, 3rd Edition) | [Link]
• [2026] A Comparative Study of Linear and Non-Linear Dimensionality Reduction for Opcode-Frequency Malware Classification
  Journal of Computer Virology and Hacking Techniques
• [2025] Embedding-Driven Synthetic Malware Generation with Autoencoders and Cluster-Tangent Diffusion
  Appl. Sci. 2025, 15, 11791 | [Link]
• [2025] Discriminative Regions and Adversarial Sensitivity in CNN-Based Malware Image Classification
  Electronics 2025, 14(19), 3937 | [Link]
• [2025] Robust Hashing for Improved CNN Performance in Image-Based Malware Detection
  Electronics 2025, 14(19), 3915 | [Link]
• [2025] Generating Synthetic Malware Samples Using Generative AI
  IEEE Access, vol. 13, pp. 59725-59736 | [Link]
• [2024] Malware Classification Using Dynamically Extracted API Call Embeddings
  Appl. Sci. 2024, 14, 5731 | [Link]
• [2024] Enhancing Botnet Detection in Network Security Using Profile Hidden Markov Models
  Applied Sciences 14, no. 10: 4019 | [Link]
• [2024] Creating Valid Adversarial Examples of Malware
  Journal of Computer Virology and Hacking Techniques, 1-15
• [2024] Classification and online clustering of zero-day malware
  Journal of Computer Virology and Hacking Techniques, 20, 579–592 | [Link]
• [2024] A comparison of adversarial malware generators
  Journal of Computer Virology and Hacking Techniques, 20, 623–639 | [Link]
• [2023] Generative Adversarial Networks and Image-Based Malware Classification
  Journal of Computer Virology and Hacking Techniques | [Link]
• [2022] Malware Classification with Word2Vec, HMM2Vec, BERT, and ELMo
  Journal of Computer Virology and Hacking Techniques, 1-16
• [2020] Detecting malware evolution using support vector machines
  Expert Systems with Applications, 143
• [2020] Convolutional neural networks for image spam detection
  Information Security Journal: A Global Perspective 29(3):103–117
• [2020] Multifamily malware models
  Journal of Computer Virology and Hacking Techniques
• [2020] Black box analysis of Android malware detectors
  Array, 6, 100022
• [2019] Feature analysis of encrypted malicious traffic
  Expert Systems with Applications, 125:130–141
• [2019] An analysis of Android adware
  Journal of Computer Virology and Hacking Techniques, 15(3):147–160
• [2019] Hidden Markov models with random restarts vs boosting for malware detection
  Journal of Computer Virology and Hacking Techniques, 15, 97–107 | [Link]
• [2018] Vigenère scores for malware detection
  Journal of Computer Virology and Hacking Techniques, 14, 157–165 | [Link]
• [2017] A comparison of static, dynamic, and hybrid analysis for malware detection
  Journal of Computer Virology and Hacking Techniques, 13(1):1–12
• [2017] Clustering for malware classification
  Journal of Computer Virology and Hacking Techniques, 13(4):95–107
• [2017] SocioBot: A Twitter-based botnet
  International Journal of Security and Networks, 12(1):1–12
• [2017] Classic cryptanalysis using hidden Markov models
  Cryptologia, 41(1):1–28
• [2016] Support vector machines and malware detection
  Journal of Computer Virology and Hacking Techniques, 12(4):203–212
• [2016] Clustering versus SVM for malware detection
  Journal of Computer Virology and Hacking Techniques, 12(4):213–224

Conference Papers

• [2025] Context-Aware Natural Language Processing for Malware Detection
  SVCC 2025, San Francisco | [Link]
• [2025] Synthetic Malware Image Generation Based on Generative Models Against Zero-Day Attacks
  SVCC 2025, San Francisco | [Link]
• [2024] An Advanced Malware Detection System Based on NLP to Generate Genetic Markers
  IEEE ICCE, Las Vegas
• [2023] Enhancing Malware Detection Using Genetic Markers and Machine Learning
  DASC/PiCom/CBDCom/CyberSciTech, Abu Dhabi | [Link]
• [2023] Synthetic Malware Using Deep Variational Autoencoders and Generative Adversarial Networks
  EAI AICSEC 2023, Bratislava
• [2023] Malware Detection through Contextualized Vector Embeddings
  SVCC 2023, IEEE | [Link]
• [2023] A Blockchain-Based Tamper-resistant Logging Framework
  SVCC 2022, Springer Nature | [Link]
• [2023] Word Embeddings for Fake Malware Generation
  SVCC 2022, Springer | [Link]
• [2023] Twitter bots detection with Benford’s Law and Machine Learning
  SVCC 2022, Springer | [Link]
• [2023] Robustness of Image-Based Malware Analysis
  SVCC 2022, Springer | [Link]
• [2022] NLP-based User Authentication through Mouse Dynamics
  ICISSP 2022
• [2022] Profile Hidden Markov Model Malware Detection and API Call Obfuscation
  ICISSP 2022
• [2021] Fake malware generation using HMM and GAN
  SVCC 2021, Springer | [Link]
• [2021] Advancement on Security Applications of Private Intersection Sum Protocol
  FTC 2021
• [2021] A new dataset for smartphone gesture-based authentication
  ForSE/ICISSP 2021
• [2021] Malware classification using long short-term memory models
  ForSE/ICISSP 2021
• [2021] Malware classification with word embedding features
  ForSE/ICISSP 2021
• [2019] A comparative analysis of Android malware
  ForSE/ICISSP 2019, Prague
• [2019] Transfer learning for image-based malware classification
  ForSE/ICISSP 2019, Prague
• [2018] Robust hashing for image-based malware classification
  BASS 2018, Porto
• [2018] On the effectiveness of generic malware models
  BASS/ICETE 2018, Porto
• [2018] Hidden Markov models for Vigenère cryptanalysis
  HistoCrypt 2018, Uppsala
• [2018] A comparison of machine learning classifiers for acoustic gait analysis
  SAM'18
• [2018] Acoustic gait analysis using support vector machines
  ForSE/ICISSP 2018
• [2018] Deep learning versus gist descriptors for image-based malware classification
  ForSE/ICISSP 2018
• [2018] Autocorrelation analysis of financial botnet traffic
  ForSE/ICISSP 2018
• [2018] Support vector machines for image spam analysis
  BASS/ICETE 2018
• [2017] Static and dynamic analysis of Android malware
  ForSE/ICISSP 2017, Porto
• [2016] Advanced Transcriptase for JavaScript malware
  MALCON 2016, Puerto Rico
• [2016] Malware detection using dynamic birthmarks
  IWSPA/ACM CODASPY 2016
• [2016] Static analysis of malicious Java applets
  IWSPA/ACM CODASPY 2016

Books & Book Chapters

• [2025] Applying Word Embeddings and Graph Neural Networks for Effective Malware Classification
  Book: Machine Learning, Deep Learning and AI for Cybersecurity
• [2025] An Empirical Analysis of Hidden Markov Models with Momentum
  Book: Machine Learning, Deep Learning and AI for Cybersecurity
• [2025] Comparing Balancing Techniques for Malware Classification
  Book: Machine Learning, Deep Learning and AI for Cybersecurity
• [2025] A Comparative Analysis of SHAP and LIME in Detecting Malicious URLs
  Book: Machine Learning, Deep Learning and AI for Cybersecurity
• [2022] Clickbait Detection for YouTube Videos
  Book: Cybersecurity for Artificial Intelligence
• [2022] Evaluating Deep Learning Models and Adversarial Attacks on Accelerometer-Based Gesture Authentication
  Book: Cybersecurity for Artificial Intelligence
• [2022] Detecting Botnets Through Deep Learning and Network Flow Analysis
  Book: Cybersecurity for Artificial Intelligence
• [2022] BERT for Malware Classification
  Book: Cybersecurity for Artificial Intelligence
• [2022] Cybersecurity for Artificial Intelligence (Editor)
  Springer
• [2021] Emulation Versus Instrumentation for Android Malware Detection
  Book: Digital Forensic Investigation of IoT Devices | [Link]
• [2021] Machine learning classification for advanced malware detection
  Kingston University, London
• [2021] Intrusion Detection and CAN Vehicle Networks
  Book: Digital Forensic Investigation of IoT Devices | [Link]
• [2020] Sentiment analysis for troll detection on Weibo
  Book: Malware Analysis using AI and Deep Learning
• [2018] Function call graphs versus machine learning for malware detection
  Book: Guide to Vulnerability Analysis for Computer Networks
• [2018] Detecting encrypted and polymorphic malware using HMMs
  Book: Guide to Vulnerability Analysis for Computer Networks
• [2018] Masquerade detection on mobile devices
  Book: Guide to Vulnerability Analysis for Computer Networks