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Practical Attacks on Machine Learning: A Case Study on Adversarial Windows Malware
Luca Demetrio, Battista Biggio, Fabio Roli
While machine learning is vulnerable to adversarial examples, it still lacks systematic procedures and tools for evaluating its security in different application contexts. In this…
Machine Learning Security against Data Poisoning: Are We There Yet?
Antonio Emanuele Cinà, Kathrin Grosse, Ambra Demontis +3
The recent success of machine learning (ML) has been fueled by the increasing availability of computing power and large amounts of data in many different applications. However, the…
Energy-Latency Attacks via Sponge Poisoning
Antonio Emanuele Cinà, Ambra Demontis, Battista Biggio +2
Sponge examples are test-time inputs optimized to increase energy consumption and prediction latency of deep networks deployed on hardware accelerators. By increasing the fraction…
ImageNet-Patch: A Dataset for Benchmarking Machine Learning Robustness against Adversarial Patches
Maura Pintor, Daniele Angioni, Angelo Sotgiu +4
Adversarial patches are optimized contiguous pixel blocks in an input image that cause a machine-learning model to misclassify it. However, their optimization is computationally de…
Fingerprint recognition with embedded presentation attacks detection: are we ready?
Marco Micheletto, Gian Luca Marcialis, Giulia Orrù +1
The diffusion of fingerprint verification systems for security applications makes it urgent to investigate the embedding of software-based presentation attack detection algorithms…
Adversarial EXEmples: A Survey and Experimental Evaluation of Practical Attacks on Machine Learning for Windows Malware Detection
Luca Demetrio, Scott E. Coull, Battista Biggio +3
Recent work has shown that adversarial Windows malware samples - referred to as adversarial EXEmples in this paper - can bypass machine learning-based detection relying on static c…
Functionality-preserving Black-box Optimization of Adversarial Windows Malware
Luca Demetrio, Battista Biggio, Giovanni Lagorio +2
Windows malware detectors based on machine learning are vulnerable to adversarial examples, even if the attacker is only given black-box query access to the model. The main drawbac…
Explaining Vulnerabilities of Deep Learning to Adversarial Malware Binaries
Luca Demetrio, Battista Biggio, Giovanni Lagorio +2
Recent work has shown that deep-learning algorithms for malware detection are also susceptible to adversarial examples, i.e., carefully-crafted perturbations to input malware that…
Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables
Bojan Kolosnjaji, Ambra Demontis, Battista Biggio +4
Machine-learning methods have already been exploited as useful tools for detecting malicious executable files. They leverage data retrieved from malware samples, such as header fie…
Adversarial Detection of Flash Malware: Limitations and Open Issues
Davide Maiorca, Ambra Demontis, Battista Biggio +2
During the past four years, Flash malware has become one of the most insidious threats to detect, with almost 600 critical vulnerabilities targeting Adobe Flash disclosed in the wi…
Evasion Attacks against Machine Learning at Test Time
Battista Biggio, Igino Corona, Davide Maiorca +5
In security-sensitive applications, the success of machine learning depends on a thorough vetting of their resistance to adversarial data. In one pertinent, well-motivated attack s…
DeltaPhish: Detecting Phishing Webpages in Compromised Websites
Igino Corona, Battista Biggio, Matteo Contini +6
The large-scale deployment of modern phishing attacks relies on the automatic exploitation of vulnerable websites in the wild, to maximize profit while hindering attack traceabilit…
Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection
Ambra Demontis, Marco Melis, Battista Biggio +6
To cope with the increasing variability and sophistication of modern attacks, machine learning has been widely adopted as a statistically-sound tool for malware detection. However,…