Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps
By Wei Yang, Deguang Kong, Tao Xie, Carl A. Gunter. 2017
132 citations recorded by the source corpus.
Subfields: Computer Networks and Communications, Software, Signal Processing
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Computer Networks and Communications
- Battista Biggio
- Fabio Roli
- Giorgio Giacinto
- Davide Maiorca
- Ambra Demontis
- Claudia Eckert
- Alina Oprea
- Giorgio Fumera
In Software
- Sheng Wen
- Surya Nepal
- Giorgio Giacinto
- Fabio Pierazzi
- Lorenzo Cavallaro
- Jacopo Cortellazzi
- Jun Zhang
- Feargus Pendlebury
In Signal Processing
- Battista Biggio
- Fabio Roli
- Giorgio Giacinto
- Davide Maiorca
- Ambra Demontis
- Giorgio Fumera
- Claudia Eckert
- Lorenzo Cavallaro
Citations in the corpus
- Exploring Backdoor Poisoning Attacks Against Malware Classifiers (2020, background): “ been extensively explored in the context of computer vision [16], and previous research efforts in this direction have also investigated the applicability of such techniques to malware classification [11,25,30,46,55]. In this work, we focus on poisoning attacks which, until now, has received less at”
- Intriguing Properties of Adversarial ML Attacks in the Problem Space (2019, methodology): “In particular, we identify some major limitations in two recent problem-space attacks [60, 75].”
- Machine Learning in IoT Security: Current Solutions and Future Challenges (2019, unknown): “networks [216]–[218].”
- Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection (2018, methodology): “Therefore, existing works are not of much value in providing proactive solutions to the everevolving adversarial examples in terms of Android malware variants [7]–[9], [16], [37].”
- A Survey on Machine Learning Techniques for Cyber Security in the Last Decade (2020, background): “Research is going on to provide a solution by having adversarial training [600]–[604].”
- Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables (2018, background): “To our knowledge, this is among the first attacks proposed at the byte-level scale, similarly to [14], as most work in adversarial machine learning for malware detection has considered injection and removal of API calls or similar characteristics [3], [6], [9], [11], [12], [16], [24], [27], [28].”