Publications
2024
Dorjan Hitaj, Giulio Pagnotta, Fabio De Gaspari, Sediola Ruko, Briland Hitaj, Luigi V. Mancini, and Fernando Perez-Cruz. Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem. 2024. [pre-print]
2023
Javier Rando, Fernando Perez-Cruz, and Briland Hitaj. PassGPT: Password Modeling and (Guided) Generation with Large Language Models. In the Proceedings of the 28th European Symposium on Research in Computer Security (ESORICS 2023). Hague, Netherlands. 2023 [PDF]
Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz, and Paolo Gasti. System and process for generating passwords or password guesses. US Patent 11,669,612. June 2023.
Dorjan Hitaj, Giulio Pagnotta, Briland Hitaj, Fernando Perez-Cruz, and Luigi V. Mancini. FedComm: Federated Learning as a Medium for Covert Communication. In IEEE Transactions on Dependable and Secure Computing (TDSC). 2023. [PDF]
Tim Ellis, Briland Hitaj, Ulf Lindqvist, Deborah Shands, Laura Tinnel, and Bruce DeBruhl. Critical Infrastructure Security Goes to Space: Leveraging Lessons Learned on the Ground – A Position Paper. In 2023 Accelerating Space Commercialization, Exploration, and New Discovery (ASCEND). Las Vegas, Nevada. 2023. [PDF]
Eric Yeh, Briland Hitaj, Sam Owre, Maena Quemener, and Natarajan Shankar. CoProver: A Recommender System for Proof Construction. In 16th Conference on Intelligent Computer Mathematics (CICM'23). Cambridge, UK. 2023 [PDF]
John Hester, Briland Hitaj, Grant Passmore, Sam Owre, Natarajan Shankar, and Eric Yeh. An Augmented Metitarski Dataset for Real Quantifier Elimination using Machine Learning. In 16th Conference on Intelligent Computer Mathematics (CICM'23). Cambridge, UK. 2023. [PDF]
Eric Yeh, Briland Hitaj, Vidyasagar Sadhu, Anirban Roy, Takuma Nakabayashi, and Yoshito Tsuji. Automatic Measures for Evaluating Generative Design Methods for Architects. 2023. [PDF]
Huascar Sanchez, Briland Hitaj. Software Introspection for Signaling Social-Cyber Operations. In Design Automation for CPS and IoT workshop (DESTION 2023). 2023. [PDF]
John Hester, Briland Hitaj, Grant Passmore, Sam Owre, Natarajan Shankar, and Eric Yeh. Revisiting Variable Ordering for Real Quantifier Elimination using Machine Learning. 2023. [PDF]
Loris Giulivi, Malhar Jere, Loris Rossi, Farinaz Koushanfar, Gabriela Ciocarlie, Briland Hitaj, and Giacomo Boracchi. Adversarial Scratches: Deployable Attacks to CNN Classifiers. In Pattern Recognition journal, 133, 108985. 2023. [PDF (journal)][PDF]
2022
Huascar Sanchez and Briland Hitaj. Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI. [PDF]
Dorjan Hitaj, Giulio Pagnotta, Briland Hitaj, Luigi V. Mancini, and Fernando Perez-Cruz. MaleficNet: Hiding Malware into Deep Neural Networks using Spread-Spectrum Channel Coding. In the Proceedings of the 27th European Symposium on Research in Computer Security (ESORICS 2022). Copenhagen, Denmark. 2022. [PDF]
Vijay H. Kothari, Prashant Anantharaman, Sean W. Smith, Briland Hitaj, Prashanth Mundkur, Natarajan Shankar, Letitia W. Li, Iavor Diatchki, and William Harris. Capturing the iccMAX calculatorElement: A Case Study on Format Design. In the Proceedings of the Eighth Workshop on Language-Theoretic Security (LangSec'22) - co-located with the IEEE Security and Privacy Symposium. May 2022. [PDF]
Giulio Pagnotta, Dorjan Hitaj, Briland Hitaj, Fernando Perez-Cruz, and Luigi V. Mancini. TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding. [PDF]
Dorjan Hitaj, Giulio Pagnotta, Briland Hitaj, Fernando Perez-Cruz, and Luigi V. Mancini. FedComm: Federated Learning as a Medium for Covert Communication. [PDF]
2020
Malhar Jere, Loris Rossi, Briland Hitaj, Gabriela Ciocarlie, Giacomo Boracchi, and Farinaz Koushanfar. Scratch that! An Evolution-based Adversarial Attack against Neural Networks [PDF]
Briland Hitaj, Giuseppe Ateniese, Fernando Perez-Cruz, and Paolo Gasti. System and Process for Generating Passwords or Password Guesses. U.S. Patent Application 16/557,416, filed March 5, 2020. [PDF]
Dorjan Hitaj, Briland Hitaj, Sushil Jajodia, and Luigi V. Mancini. Capture the Bot: Using Adversarial Examples to Improve CAPTCHA Robustness to Bot Attacks. In IEEE Intelligent Systems. October, 2020. [PDF]
2019
Malhar Jere, Briland Hitaj, Gabriela Ciocarlie and Farinaz Koushanfar. Scratch that! An Evolution-based Adversarial Attack against Neural Networks [PDF]
Briland Hitaj, Paolo Gasti, Giuseppe Ateniese and Fernando Perez-Cruz. PassGAN: A Deep Learning Approach for Password Guessing. In Proceedings of the 17 International Conference on Applied Cryptography and Network Security. ACNS'19. Bogota, Colombia, 2019.
Dorjan Hitaj, Briland Hitaj, Luigi V. Mancini. Evasion Attacks Against Watermarking Techniques found in MLaaS Systems. In 6th IEEE International Conference on Software Defined System. SDS 2019. Rome, Italy, 2019
2018
Briland Hitaj, Paolo Gasti, Giuseppe Ateniese and Fernando Perez-Cruz. PassGAN: A Deep Learning Approach for Password Guessing. In NIPS 2018 Workshop on Security in Machine Learning. SECML'18. Montreal, Canada, 2018 [PDF]
Briland Hitaj. GANs n' Privacy: Novel Attacks on Privacy via Generative Adversarial Networks. PhD Thesis. October 2018 [PDF]
Briland Hitaj, Giuseppe Ateniese. The Broken Promise of Decentralized Deep Learning. Featured Topic in Communications and Information Security Technical Committee (CIS-TC) Newsletter. 2018 [PDF]
Pablo M. Olmos, Briland Hitaj, Paolo Gasti, Giuseppe Ateniese and Fernando Perez-Cruz. What are GANs Useful for?
2017
Briland Hitaj, Paolo Gasti, Giuseppe Ateniese and Fernando Perez-Cruz. PassGAN: A Deep Learning Approach for Password Guessing [PDF]
Briland Hitaj, Giuseppe Ateniese and Fernando Perez-Cruz. Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. In Proceedings of the 24th ACM SIGSAC Conference on Computer and Communications Security. ACM CCS'17. pages 603-618. Dallas, TX, USA, 2017 [PDF] [Slides] [Presentation] (Best Ph.D Student Paper of the Year 2017 Award -- CS Department, Sapienza University of Rome)
2015
Giuseppe Ateniese, Briland Hitaj, Luigi V. Mancini, Nino V. Verde and Antonio Villani. No Place to Hide that Bytes won't Reveal: Sniffing Location-Based Encrypted Traffic to Track a User's Position. In Proceedings of the 9th International Conference on Network and System Security, volume 9408 of Lecture Notes in Computer Science, pages 45-69. Springer International Publishing, 2015 [PDF] [Slides]