Cavallaro, L;
Gray, J;
Sgandurra, D;
(2021)
Identifying Authorship Style in Malicious Binaries: Techniques, Challenges & Datasets.
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Abstract
Attributing a piece of malware to its creator typically requires threat intelligence. Binary attribution increases the level of difficulty as it mostly relies upon the ability to disassemble binaries to identify authorship style. Our survey explores malicious author style and the adversarial techniques used by them to remain anonymous. We examine the adversarial impact on the state-of-the-art methods. We identify key findings and explore the open research challenges. To mitigate the lack of ground truth datasets in this domain, we publish alongside this survey the largest and most diverse meta-information dataset of 15,660 malware labeled to 164 threat actor groups.
| Type: | Working / discussion paper |
|---|---|
| Title: | Identifying Authorship Style in Malicious Binaries: Techniques, Challenges & Datasets |
| Open access status: | An open access version is available from UCL Discovery |
| Publisher version: | https://doi.org/10.48550/arXiv.2101.06124 |
| Language: | English |
| Additional information: | This work is licensed under an Attribution 4.0 International License (CC BY 4.0). |
| UCL classification: | UCL UCL > Provost and Vice Provost Offices > UCL BEAMS UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10133169 |
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