activity
20062022
most citedFunctionality-preserving Black-box Optimization of Adversarial Windows Malware

155 citations · 446 across the 9 of their papers we have counts for

collaborators

9 papers

cs.DC2022★ 20 cited

LiDiTE: a Full-Fledged and Featherweight Digital Twin Framework

Enrico Russo, Gabriele Costa, Giacomo Longo +2

The rising of the Cyber-Physical System (CPS) and the Industry 4.0 paradigms demands the design and the implementation of Digital Twin Frameworks (DTFs) that may support the quick…

cs.CR2020★ 133 cited

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…

cs.CR2020★ 2 cited

Never Trust Your Victim: Weaponizing Vulnerabilities in Security Scanners

Andrea Valenza, Gabriele Costa, Alessandro Armando

The first step of every attack is reconnaissance, i.e., to acquire information about the target. A common belief is that there is almost no risk in scanning a target from a remote…

cs.CR2020★ 155 cited

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…

cs.CR2020★ 18 cited

Automating the Generation of Cyber Range Virtual Scenarios with VSDL

Gabriele Costa, Enrico Russo, Alessandro Armando

A cyber range is an environment used for training security experts and testing attack and defence tools and procedures. Usually, a cyber range simulates one or more critical infras…

cs.CR2019★ 24 cited

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…

cs.CR2012★ 4 cited

Security Issues in the Android Cross-Layer Architecture

Alessandro Armando, Alessio Merlo, Luca Verderame

The security of Android has been recently challenged by the discovery of a number of vulnerabilities involving different layers of the Android stack. We argue that such vulnerabili…

cs.CR2010★ 2 cited

Automated Symbolic Analysis of ARBAC-Policies (Extended Version)

A. Armando, S. Ranise

One of the most widespread framework for the management of access-control policies is Administrative Role Based Access Control (ARBAC). Several automated analysis techniques have b…

cs.AI2006★ 88 cited

New results on rewrite-based satisfiability procedures

Alessandro Armando, Maria Paola Bonacina, Silvio Ranise +1

Program analysis and verification require decision procedures to reason on theories of data structures. Many problems can be reduced to the satisfiability of sets of ground literal…