Name
Abstract | ||
|---|---|---|
In this paper, we conduct the largest to-date analysis of browser extensions, by investigating 922,684 different extension versions collected in the past six years, and using this data to discover malicious versions of extensions. We propose a two-stage system that first identifies malicious extensions based on anomalous extension ratings and locates the code that was added to a benign extension in order to make it malicious. We encode these code deltas according to the APIs that they abuse and search our historical dataset for other similar deltas of extensions which have not yet been flagged, neither by users nor by Chrome's Web Store. We were able to discover 143 malicious extensions belonging to 21 malicious clusters, exhibiting a wide range of abuse, from history stealing and ad injection, to the hijacking of new tabs and search engines. Our results show that our proposed techniques operate in an abuse-agnostic way and can identify malicious extensions that are evading detection.
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Year | DOI | Venue |
|---|---|---|
2020 | 10.1145/3372297.3423343 | CCS '20: 2020 ACM SIGSAC Conference on Computer and Communications Security
Virtual Event
USA
November, 2020 |
DocType | ISBN | Citations |
Conference | 978-1-4503-7089-9 | 1 |
PageRank | References | Authors |
0.36 | 12 | 3 |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Nikolaos Pantelaios | 1 | 1 | 0.36 |
| Nick Nikiforakis | 2 | 865 | 53.35 |
| Alexandros Kapravelos | 3 | 324 | 20.58 |