Battista Biggio

About Battista Biggio

Battista Biggio, With an exceptional h-index of 47 and a recent h-index of 40 (since 2020), a distinguished researcher at Università degli Studi di Cagliari, specializes in the field of Machine Learning, Adversarial Machine Learning, Computer Security, Biometrics.

His recent articles reflect a diverse array of research interests and contributions to the field:

Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De) Randomized Smoothing

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples

When Your AI Becomes a Target: AI Security Incidents and Best Practices

Toward Effective Traffic Sign Detection via Two-Stage Fusion Neural Networks

Machine learning security against data poisoning: Are we there yet?

Living-off-The-Land Reverse-Shell Detection by Informed Data Augmentation

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates

-zero: Gradient-based Optimization of -norm Adversarial Examples

Battista Biggio Information

University

Position

Assistant Professor at Co-Founder of Pluribus One

Citations(all)

14338

Citations(since 2020)

11505

Cited By

7067

hIndex(all)

47

hIndex(since 2020)

40

i10Index(all)

92

i10Index(since 2020)

81

Email

University Profile Page

Google Scholar

Battista Biggio Skills & Research Interests

Machine Learning

Adversarial Machine Learning

Computer Security

Biometrics

Top articles of Battista Biggio

Certified Adversarial Robustness of Machine Learning-based Malware Detectors via (De) Randomized Smoothing

arXiv preprint arXiv:2405.00392

2024/5/1

AttackBench: Evaluating Gradient-based Attacks for Adversarial Examples

arXiv preprint arXiv:2404.19460

2024/4/30

When Your AI Becomes a Target: AI Security Incidents and Best Practices

Proceedings of the AAAI Conference on Artificial Intelligence

2024/3/24

Toward Effective Traffic Sign Detection via Two-Stage Fusion Neural Networks

IEEE Transactions on Intelligent Transportation Systems

2024/3/18

Machine learning security against data poisoning: Are we there yet?

2024/3/6

Living-off-The-Land Reverse-Shell Detection by Informed Data Augmentation

arXiv preprint arXiv:2402.18329

2024/2/28

Robustness-Congruent Adversarial Training for Secure Machine Learning Model Updates

arXiv preprint arXiv:2402.17390

2024/2/27

-zero: Gradient-based Optimization of -norm Adversarial Examples

arXiv preprint arXiv:2402.01879

2024/2/2

Rethinking data augmentation for adversarial robustness

Information Sciences

2024/1/1

Hardening RGB-D object recognition systems against adversarial patch attacks

Information Sciences

2023/12/1

Raze to the Ground: Query-Efficient Adversarial HTML Attacks on Machine-Learning Phishing Webpage Detectors

2023/11/30

Improving Fast Minimum-Norm Attacks with Hyperparameter Optimization

arXiv preprint arXiv:2310.08177

2023/10/12

Nebula: Self-Attention for Dynamic Malware Analysis

arXiv preprint arXiv:2310.10664

2023/9/19

Stateful Detection of Adversarial Reprogramming

Information Sciences

2023/9/1

Adversarial ModSecurity: Countering Adversarial SQL Injections with Robust Machine Learning

arXiv preprint arXiv:2308.04964

2023/8/9

Detecting Attacks Against Deep Reinforcement Learning for Autonomous Driving

2023/7/9

Samples on Thin Ice: Re-Evaluating Adversarial Pruning of Neural Networks

2023/7/9

Why Adversarial Reprogramming Works, When It Fails, and How to Tell the Difference

Information Sciences

2023/6/1

Wild Patterns Reloaded: A Survey of Machine Learning Security against Training Data Poisoning

2023/7/13

Machine Learning Security in Industry: A Quantitative Survey

IEEE Transactions on Information Forensics and Security

2023/3/2

Kathrin Grosse
Kathrin Grosse

H-Index: 6

Battista Biggio
Battista Biggio

H-Index: 33

See List of Professors in Battista Biggio University(Università degli Studi di Cagliari)

Co-Authors

academic-engine