889 citations · 1.1k across the 14 of their papers we have counts for
17 papers
Adversarial Pruning: A Survey and Benchmark of Pruning Methods for Adversarial Robustness
Giorgio Piras, Maura Pintor, Ambra Demontis +3
Recent work has proposed neural network pruning techniques to reduce the size of a network while preserving robustness against adversarial examples, i.e., well-crafted inputs induc…
HO-FMN: Hyperparameter Optimization for Fast Minimum-Norm Attacks
Raffaele Mura, Giuseppe Floris, Luca Scionis +6
Gradient-based attacks are a primary tool to evaluate robustness of machine-learning models. However, many attacks tend to provide overly-optimistic evaluations as they use fixed l…
A Longitudinal Study of Cryptographic API: a Decade of Android Malware
Adam Janovsky, Davide Maiorca, Dominik Macko +2
Cryptography has been extensively used in Android applications to guarantee secure communications, conceal critical data from reverse engineering, or ensure mobile users' privacy.…
Do Gradient-based Explanations Tell Anything About Adversarial Robustness to Android Malware?
Marco Melis, Michele Scalas, Ambra Demontis +4
While machine-learning algorithms have demonstrated a strong ability in detecting Android malware, they can be evaded by sparse evasion attacks crafted by injecting a small set of…
Automotive Cybersecurity: Foundations for Next-Generation Vehicles
Michele Scalas, Giorgio Giacinto
The automotive industry is experiencing a serious transformation due to a digitalisation process and the transition to the new paradigm of Mobility-as-a-Service. The next-generatio…
PowerDrive: Accurate De-Obfuscation and Analysis of PowerShell Malware
Denis Ugarte, Davide Maiorca, Fabrizio Cara +1
PowerShell is nowadays a widely-used technology to administrate and manage Windows-based operating systems. However, it is also extensively used by malware vectors to execute paylo…
Poisoning Behavioral Malware Clustering
Battista Biggio, Konrad Rieck, Davide Ariu +4
Clustering algorithms have become a popular tool in computer security to analyze the behavior of malware variants, identify novel malware families, and generate signatures for anti…
Towards Adversarial Malware Detection: Lessons Learned from PDF-based Attacks
Davide Maiorca, Battista Biggio, Giorgio Giacinto
Malware still constitutes a major threat in the cybersecurity landscape, also due to the widespread use of infection vectors such as documents. These infection vectors hide embedde…
On the Effectiveness of System API-Related Information for Android Ransomware Detection
Michele Scalas, Davide Maiorca, Francesco Mercaldo +3
Ransomware constitutes a significant threat to the Android operating system. It can either lock or encrypt the target devices, and victims are forced to pay ransoms to restore thei…
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…
Explaining Black-box Android Malware Detection
Marco Melis, Davide Maiorca, Battista Biggio +2
Machine-learning models have been recently used for detecting malicious Android applications, reporting impressive performances on benchmark datasets, even when trained only on fea…
IntelliAV: Building an Effective On-Device Android Malware Detector
Mansour Ahmadi, Angelo Sotgiu, Giorgio Giacinto
The importance of employing machine learning for malware detection has become explicit to the security community. Several anti-malware vendors have claimed and advertised the appli…
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…
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,…
Novel Feature Extraction, Selection and Fusion for Effective Malware Family Classification
Mansour Ahmadi, Dmitry Ulyanov, Stanislav Semenov +2
Modern malware is designed with mutation characteristics, namely polymorphism and metamorphism, which causes an enormous growth in the number of variants of malware samples. Catego…
Security Evaluation of Support Vector Machines in Adversarial Environments
Battista Biggio, Igino Corona, Blaine Nelson +5
Support Vector Machines (SVMs) are among the most popular classification techniques adopted in security applications like malware detection, intrusion detection, and spam filtering…