UNVEIL: A large-scale, automated approach to detecting ransomware (keynote)
By Engin Kirda. IEEE International Conference on Software Analysis, Evolution, and Reengineering, 2016
438 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Information Systems, Signal Processing
View Paper: https://www.semanticscholar.org/paper/ccb08a5c512c2197f8d90688a9836ed6ae55ccf5
How researchers see this paper
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In Computer Networks and Communications
- Davide Maiorca
- Giorgio Giacinto
- Wajih Ul Hassan
- Adam Bates
- Fabio Martinelli
- Corrado Aaron Visaggio
- Zhengzhang Chen
- Francesco Mercaldo
In Information Systems
- Giorgio Giacinto
- Davide Maiorca
- Adam Bates
- Fabio Martinelli
- Corrado Aaron Visaggio
- Francesco Mercaldo
- Michele Scalas
- Battista Biggio
In Signal Processing
- Giorgio Giacinto
- Davide Maiorca
- Adam Bates
- Fabio Martinelli
- Corrado Aaron Visaggio
- Francesco Mercaldo
- Wajih Ul Hassan
- Michele Scalas
Citations in the corpus
- NoDoze: Combatting Threat Alert Fatigue with Automated Provenance Triage (2019, background): “For example, since both ransomware and ZIP programs read and write many files in a short period of time, a simple ransomware detector that only checks the behavior of a single process can easily classify ZIP as ransomware [40].”
- On the effectiveness of system API-related information for Android ransomware detection (2018, methodology): “ particular, on Windows platforms) by employing dynamic analysis techniques to perform early detection of the attack. Using such techniques avoid possible damages to the operating system and its les. [23, 24, 25, 26]. 5. Methodologies We now describe the general structure of systems that employ Syst”