No Place to Hide that Bytes Won't Reveal: Sniffing Location-Based Encrypted Traffic to Track a User's Position. - Giuseppe Ateniese; Briland Hitaj; Luigi Vincenzo Mancini; Nino Vincenzo Verde; Antonio Villani (NSS, 2015)

No Place to Hide that Bytes won't Reveal: Sniffing Location-Based Encrypted Traffic to Track User Position. - Giuseppe Ateniese; Briland Hitaj; Luigi V. Mancini; Nino Vincenzo Verde; Antonio Villani (CoRR, 2015)

Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning. - Briland Hitaj; Giuseppe Ateniese; Fernando Pérez-Cruz (CCS, 2017)

PassGAN: A Deep Learning Approach for Password Guessing. - Briland Hitaj; Paolo Gasti; Giuseppe Ateniese; Fernando Pérez-Cruz (ACNS, 2019)

Evasion Attacks Against Watermarking Techniques found in MLaaS Systems. - Dorjan Hitaj; Briland Hitaj; Luigi V. Mancini (SDS, 2019)

Scratch that! An Evolution-based Adversarial Attack against Neural Networks. - Malhar Jere; Briland Hitaj; Gabriela F. Ciocarlie; Farinaz Koushanfar (CoRR, 2019)

Capture the Bot: Using Adversarial Examples to Improve CAPTCHA Robustness to Bot Attacks. - Dorjan Hitaj; Briland Hitaj; Sushil Jajodia; Luigi V. Mancini (IEEE Intell. Syst., 2021)

MaleficNet: Hiding Malware into Deep Neural Networks Using Spread-Spectrum Channel Coding. - Dorjan Hitaj; Giulio Pagnotta; Briland Hitaj; Luigi V. Mancini; Fernando Pérez-Cruz (ESORICS (3), 2022)

Capturing the iccMAX calculatorElement: A Case Study on Format Design. - Vijay H. Kothari; Prashant Anantharaman; Sean W. Smith; Briland Hitaj; Prashanth Mundkur; Natarajan Shankar; Letitia W. Li; Iavor Diatchki; William Harris (SP (Workshops), 2022)

Trust in Motion: Capturing Trust Ascendancy in Open-Source Projects using Hybrid AI. - Huascar Sanchez; Briland Hitaj (CoRR, 2022)

Software Introspection for Signaling Social-Cyber Operations. - Huascar Sanchez; Briland Hitaj (CPS-IoT Week Workshops, 2023)

PassGPT: Password Modeling and (Guided) Generation with Large Language Models. - Javier Rando; Fernando Pérez-Cruz; Briland Hitaj (ESORICS (4), 2023)

An Augmented MetiTarski Dataset for Real Quantifier Elimination Using Machine Learning. - John Hester; Briland Hitaj; Grant O. Passmore; Sam Owre; Natarajan Shankar; Eric Yeh (CICM, 2023)

CoProver: A Recommender System for Proof Construction. - Eric Yeh; Briland Hitaj; Sam Owre; Maena Quemener; Natarajan Shankar (CICM, 2023)

Revisiting Variable Ordering for Real Quantifier Elimination using Machine Learning. - John Hester; Briland Hitaj; Grant O. Passmore; Sam Owre; Natarajan Shankar; Eric Yeh (CoRR, 2023)

Automatic Measures for Evaluating Generative Design Methods for Architects. - Eric Yeh; Briland Hitaj; Vidyasagar Sadhu; Anirban Roy; Takuma Nakabayashi; Yoshito Tsuji (CoRR, 2023)

Critical Infrastructure Security Goes to Space: Leveraging Lessons Learned on the Ground. - Tim Ellis; Briland Hitaj; Ulf Lindqvist; Deborah Shands; Laura Tinnel; Bruce DeBruhl (CoRR, 2023)

Adversarial scratches: Deployable attacks to CNN classifiers. - Loris Giulivi; Malhar Jere; Loris Rossi; Farinaz Koushanfar; Gabriela F. Ciocarlie; Briland Hitaj; Giacomo Boracchi (Pattern Recognit., 2023)

TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding. - Giulio Pagnotta; Dorjan Hitaj; Briland Hitaj; Fernando Pérez-Cruz; Luigi V. Mancini (ACSAC, 2024)

Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem. - Dorjan Hitaj; Giulio Pagnotta; Fabio De Gaspari; Sediola Ruko; Briland Hitaj; Luigi V. Mancini; Fernando Pérez-Cruz (CoRR, 2024)

FedComm: Federated Learning as a Medium for Covert Communication. - Dorjan Hitaj; Giulio Pagnotta; Briland Hitaj; Fernando Pérez-Cruz; Luigi V. Mancini (IEEE Trans. Dependable Secur. Comput., 2024)

A Case Study on the Use of Representativeness Bias as a Defense Against Adversarial Cyber Threats. - Briland Hitaj; Grit Denker; Laura Tinnel; Michael McAnally; Bruce DeBruhl; Nathan Bunting; Alex Fafard; Daniel Aaron; Richard D. Roberts; Joshua Lawson; Greg McCain; Dylan Starink (CoRR, 2025)