Barbero, Federico;
Pendlebury, Feargus;
Pierazzi, Fabio;
Cavallaro, Lorenzo;
(2022)
Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift.
In:
2022 IEEE Symposium on Security and Privacy (SP).
IEEE: San Francisco, CA, USA.
Preview |
Text
transcending.pdf - Accepted Version Download (2MB) | Preview |
Abstract
Machine learning for malware classification shows encouraging results, but real deployments suffer from performance degradation as malware authors adapt their techniques to evade detection. This phenomenon, known as concept drift, occurs as new malware examples evolve and become less and less like the original training examples. One promising method to cope with concept drift is classification with rejection in which examples that are likely to be misclassified are instead quarantined until they can be expertly analyzed.We propose TRANSCENDENT, a rejection framework built on Transcend, a recently proposed strategy based on conformal prediction theory. In particular, we provide a formal treatment of Transcend, enabling us to refine conformal evaluation theory—its underlying statistical engine—and gain a better understanding of the theoretical reasons for its effectiveness. In the process, we develop two additional conformal evaluators that match or surpass the performance of the original while significantly decreasing the computational overhead. We evaluate TRANSCENDENT on a malware dataset spanning 5 years that removes sources of experimental bias present in the original evaluation. TRANSCENDENT outperforms state-of-the-art approaches while generalizing across different malware domains and classifiers.To further assist practitioners, we showcase optimal operational settings for a TRANSCENDENT deployment and show how it can be applied to many popular learning algorithms. These insights support both old and new empirical findings, making Transcend a sound and practical solution for the first time. To this end, we release TRANSCENDENT as open source, to aid the adoption of rejection strategies by the security community.
| Type: | Proceedings paper |
|---|---|
| Title: | Transcending Transcend: Revisiting Malware Classification in the Presence of Concept Drift |
| Event: | 2022 IEEE Symposium on Security and Privacy (SP) |
| Dates: | 22 May 2022 - 26 May 2022 |
| Open access status: | An open access version is available from UCL Discovery |
| DOI: | 10.1109/SP46214.2022.9833659 |
| Publisher version: | https://doi.org/10.1109/SP46214.2022.9833659 |
| Language: | English |
| Additional information: | This version is the author accepted manuscript. For information on re-use, please refer to the publisher's terms and conditions. |
| Keywords: | Security, machine learning, malware detection |
| UCL classification: | 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 UCL > Provost and Vice Provost Offices > UCL BEAMS UCL |
| URI: | https://discovery.ucl.ac.uk/id/eprint/10142926 |
Archive Staff Only
 |
View Item |
