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09/19/2023
Adversarial Attacks Against Uncertainty Quantification
Machine-learning models can be fooled by adversarial examples, i.e., car...
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08/09/2023
Adversarial ModSecurity: Countering Adversarial SQL Injections with Robust Machine Learning
ModSecurity is widely recognized as the standard open-source Web Applica...
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12/12/2022
A Survey on Reinforcement Learning Security with Application to Autonomous Driving
Reinforcement learning allows machines to learn from their own experienc...
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11/05/2022
Stateful Detection of Adversarial Reprogramming
Adversarial reprogramming allows stealing computational resources by rep...
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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...
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08/09/2022
Robust Machine Learning for Malware Detection over Time
The presence and persistence of Android malware is an on-going threat th...
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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...
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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...
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06/01/2022
Support Vector Machines under Adversarial Label Contamination
Machine learning algorithms are increasingly being applied in security-r...
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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...
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03/14/2022
Energy-Latency Attacks via Sponge Poisoning
Sponge examples are test-time inputs carefully-optimized to increase ene...
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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...
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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...
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06/30/2021
The Threat of Offensive AI to Organizations
AI has provided us with the ability to automate tasks, extract informati...
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06/18/2021
Indicators of Attack Failure: Debugging and Improving Optimization of Adversarial Examples
Evaluating robustness of machine-learning models to adversarial examples...
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06/14/2021
Backdoor Learning Curves: Explaining Backdoor Poisoning Beyond Influence Functions
Backdoor attacks inject poisoning samples during training, with the goal...
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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...
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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...
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02/25/2021
Fast Minimum-norm Adversarial Attacks through Adaptive Norm Constraints
Evaluating adversarial robustness amounts to finding the minimum perturb...
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12/23/2020
Poisoning Attacks on Cyber Attack Detectors for Industrial Control Systems
Recently, neural network (NN)-based methods, including autoencoders, hav...
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10/18/2020
FADER: Fast Adversarial Example Rejection
Deep neural networks are vulnerable to adversarial examples, i.e., caref...
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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...
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06/06/2020
Can Domain Knowledge Alleviate Adversarial Attacks in Multi-Label Classifiers?
Adversarial attacks on machine learning-based classifiers, along with de...
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05/25/2020
Adversarial Feature Selection against Evasion Attacks
Pattern recognition and machine learning techniques have been increasing...
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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...
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04/15/2020
Poisoning Attacks on Algorithmic Fairness
Research in adversarial machine learning has shown how the performance o...
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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...
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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...
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10/01/2019
Deep Neural Rejection against Adversarial Examples
Despite the impressive performances reported by deep neural networks in ...
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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...
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04/30/2019
Detecting Adversarial Examples through Nonlinear Dimensionality Reduction
Deep neural networks are vulnerable to adversarial examples, i.e., caref...
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01/11/2019
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries
Recent work has shown that deep-learning algorithms for malware detectio...
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11/25/2018
Poisoning Behavioral Malware Clustering
Clustering algorithms have become a popular tool in computer security to...
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11/25/2018
Is Data Clustering in Adversarial Settings Secure?
Clustering algorithms have been increasingly adopted in security applica...
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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,...
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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...
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05/30/2018
Towards Adversarial Configurations for Software Product Lines
Ensuring that all supposedly valid configurations of a software product ...
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04/21/2018
Is feature selection secure against training data poisoning?
Learning in adversarial settings is becoming an important task for appli...
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04/01/2018
Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning
As machine learning becomes widely used for automated decisions, attacke...
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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...
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03/09/2018
Explaining Black-box Android Malware Detection
Machine-learning models have been recently used for detecting malicious ...
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12/17/2017
Super-sparse Learning in Similarity Spaces
In several applications, input samples are more naturally represented in...
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12/08/2017
Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning
Learning-based pattern classifiers, including deep networks, have demons...
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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...
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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...
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09/06/2016
Statistical Meta-Analysis of Presentation Attacks for Secure Multibiometric Systems
Prior work has shown that multibiometric systems are vulnerable to prese...
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06/27/2012
