Battista Biggio

research

∙ 09/19/2023

Adversarial Attacks Against Uncertainty Quantification

Machine-learning models can be fooled by adversarial examples, i.e., car...

0 Emanuele Ledda, et al. ∙

research

∙ 08/09/2023

Adversarial ModSecurity: Countering Adversarial SQL Injections with Robust Machine Learning

ModSecurity is widely recognized as the standard open-source Web Applica...

0 Biagio Montaruli, et al. ∙

research

∙ 12/12/2022

A Survey on Reinforcement Learning Security with Application to Autonomous Driving

Reinforcement learning allows machines to learn from their own experienc...

33 Ambra Demontis, et al. ∙

research

∙ 11/05/2022

Stateful Detection of Adversarial Reprogramming

Adversarial reprogramming allows stealing computational resources by rep...

0 Yang Zheng, et al. ∙

research

∙ 08/10/2022

Explaining Machine Learning DGA Detectors from DNS Traffic Data

One of the most common causes of lack of continuity of online systems st...

5 Giorgio Piras, et al. ∙

research

∙ 08/09/2022

Robust Machine Learning for Malware Detection over Time

The presence and persistence of Android malware is an on-going threat th...

0 Daniele Angioni, et al. ∙

research

∙ 07/12/2022

Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware

While machine learning is vulnerable to adversarial examples, it still l...

9 Luca Demetrio, et al. ∙

research

∙ 07/11/2022

"Why do so?" – A Practical Perspective on Machine Learning Security

Despite the large body of academic work on machine learning security, li...

29 Kathrin Grosse, et al. ∙

research

∙ 06/01/2022

Support Vector Machines under Adversarial Label Contamination

Machine learning algorithms are increasingly being applied in security-r...

11 Huang Xiao, et al. ∙

research

∙ 05/26/2022

Denial-of-Service Attack on Object Detection Model Using Universal Adversarial Perturbation

Adversarial attacks against deep learning-based object detectors have be...

14 Avishag Shapira, et al. ∙

research

∙ 03/14/2022

Energy-Latency Attacks via Sponge Poisoning

Sponge examples are test-time inputs carefully-optimized to increase ene...

5 Antonio Emanuele Cinà, et al. ∙

research

∙ 03/07/2022

ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches

Adversarial patches are optimized contiguous pixel blocks in an input im...

50 Maura Pintor, et al. ∙

research

∙ 08/26/2021

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

Adversarial reprogramming allows repurposing a machine-learning model to...

20 Yang Zheng, et al. ∙

research

∙ 06/30/2021

The Threat of Offensive AI to Organizations

AI has provided us with the ability to automate tasks, extract informati...

21 Yisroel Mirsky, et al. ∙

research

∙ 06/18/2021

Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples

Evaluating robustness of machine-learning models to adversarial examples...

25 Maura Pintor, et al. ∙

research

∙ 06/14/2021

Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions

Backdoor attacks inject poisoning samples during training, with the goal...

5 Antonio Emanuele Cinà, et al. ∙

research

∙ 04/26/2021

secml-malware: A Python Library for Adversarial Robustness Evaluation of Windows Malware Classifiers

Machine learning has been increasingly used as a first line of defense f...

0 Luca Demetrio, et al. ∙

research

∙ 03/23/2021

The Hammer and the Nut: Is Bilevel Optimization Really Needed to Poison Linear Classifiers?

One of the most concerning threats for modern AI systems is data poisoni...

7 Antonio Emanuele Cinà, et al. ∙

research

∙ 02/25/2021

Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints

Evaluating adversarial robustness amounts to finding the minimum perturb...

14 Maura Pintor, et al. ∙

research

∙ 12/23/2020

Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems

Recently, neural network (NN)-based methods, including autoencoders, hav...

52 Moshe Kravchik, et al. ∙

research

∙ 10/18/2020

FADER: Fast Adversarial Example Rejection

Deep neural networks are vulnerable to adversarial examples, i.e., caref...

1 Francesco Crecchi, et al. ∙

research

∙ 08/17/2020

Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection

Recent work has shown that adversarial Windows malware samples - also re...

0 Luca Demetrio, et al. ∙

research

∙ 06/06/2020

Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers?

Adversarial attacks on machine learning-based classifiers, along with de...

16 Stefano Melacci, et al. ∙

research

∙ 05/25/2020

Adversarial Feature Selection against Evasion Attacks

Pattern recognition and machine learning techniques have been increasing...

8 Fei Zhang, et al. ∙

research

∙ 05/04/2020

Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?

Machine-learning algorithms trained on features extracted from static co...

3 Marco Melis, et al. ∙

research

∙ 04/15/2020

Poisoning Attacks on Algorithmic Fairness

Research in adversarial machine learning has shown how the performance o...

8 David Solans, et al. ∙

research

∙ 03/30/2020

Efficient Black-box Optimization of Adversarial Windows Malware with Constrained Manipulations

Windows malware detectors based on machine learning are vulnerable to ad...

0 Luca Demetrio, et al. ∙

research

∙ 12/20/2019

secml: A Python Library for Secure and Explainable Machine Learning

We present secml, an open-source Python library for secure and explainab...

122 Marco Melis, et al. ∙

research

∙ 10/01/2019

Deep Neural Rejection against Adversarial Examples

Despite the impressive performances reported by deep neural networks in ...

47 Angelo Sotgiu, et al. ∙

research

∙ 09/16/2019

Towards Quality Assurance of Software Product Lines with Adversarial Configurations

Software product line (SPL) engineers put a lot of effort to ensure that...

0 Paul Temple, et al. ∙

research

∙ 04/30/2019

Detecting Adversarial Examples through Nonlinear Dimensionality Reduction

Deep neural networks are vulnerable to adversarial examples, i.e., caref...

16 Francesco Crecchi, et al. ∙

research

∙ 01/11/2019

Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries

Recent work has shown that deep-learning algorithms for malware detectio...

0 Luca Demetrio, et al. ∙

research

∙ 11/25/2018

Poisoning Behavioral Malware Clustering

Clustering algorithms have become a popular tool in computer security to...

0 Battista Biggio, et al. ∙

research

∙ 11/25/2018

Is Data Clustering in Adversarial Settings Secure?

Clustering algorithms have been increasingly adopted in security applica...

0 Battista Biggio, et al. ∙

research

∙ 11/02/2018

Towards Robust Detection of Adversarial Infection Vectors: Lessons Learned in PDF Malware

Malware still constitutes a major threat in the cybersecurity landscape,...

0 Davide Maiorca, et al. ∙

research

∙ 09/08/2018

On the Intriguing Connections of Regularization, Input Gradients and Transferability of Evasion and Poisoning Attacks

Transferability captures the ability of an attack against a machine-lear...

0 Ambra Demontis, et al. ∙

research

∙ 05/30/2018

Towards Adversarial Configurations for Software Product Lines

Ensuring that all supposedly valid configurations of a software product ...

12 Paul Temple, et al. ∙

research

∙ 04/21/2018

Is feature selection secure against training data poisoning?

Learning in adversarial settings is becoming an important task for appli...

0 Huang Xiao, et al. ∙

research

∙ 04/01/2018

Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning

As machine learning becomes widely used for automated decisions, attacke...

0 Matthew Jagielski, et al. ∙

research

∙ 03/12/2018

Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables

Machine-learning methods have already been exploited as useful tools for...

0 Bojan Kolosnjaji, et al. ∙

research

∙ 03/09/2018

Explaining Black-box Android Malware Detection

Machine-learning models have been recently used for detecting malicious ...

0 Marco Melis, et al. ∙

research

∙ 12/17/2017

Super-sparse Learning in Similarity Spaces

In several applications, input samples are more naturally represented in...

0 Ambra Demontis, et al. ∙

research

∙ 12/08/2017

Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning

Learning-based pattern classifiers, including deep networks, have demons...

0 Battista Biggio, et al. ∙

research

∙ 10/27/2017

Adversarial Detection of Flash Malware: Limitations and Open Issues

During the past two years, Flash malware has become one of the most insi...

0 Davide Maiorca, et al. ∙

research

∙ 08/23/2017

Is Deep Learning Safe for Robot Vision? Adversarial Examples against the iCub Humanoid

Deep neural networks have been widely adopted in recent years, exhibitin...

0 Marco Melis, et al. ∙

research

∙ 09/06/2016

Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems

Prior work has shown that multibiometric systems are vulnerable to prese...

0 Battista Biggio, et al. ∙

research

∙ 06/27/2012

Poisoning Attacks against Support Vector Machines

We investigate a family of poisoning attacks against Support Vector Mach...

0 Battista Biggio, et al. ∙

Battista Biggio

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