155 citations · 381 across the 6 of their papers we have counts for
6 papers
Longitudinal Study of the Prevalence of Malware Evasive Techniques
Lorenzo Maffia, Dario Nisi, Platon Kotzias +3
By their very nature, malware samples employ a variety of techniques to conceal their malicious behavior and hide it from analysis tools. To mitigate the problem, a large number of…
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…
WAF-A-MoLE: Evading Web Application Firewalls through Adversarial Machine Learning
Luca Demetrio, Andrea Valenza, Gabriele Costa +1
Web Application Firewalls are widely used in production environments to mitigate security threats like SQL injections. Many industrial products rely on signature-based techniques,…
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…
Coinductive subtyping for abstract compilation of object-oriented languages into Horn formulas
Davide Ancona, Giovanni Lagorio
In recent work we have shown how it is possible to define very precise type systems for object-oriented languages by abstractly compiling a program into a Horn formula f. Then type…