Poster
| Autor: | Mohammad Saidur Rahman, Matthew Wright, Nate Mathews |
|---|---|
| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry Deep learning Data_MISCELLANEOUS 020206 networking & telecommunications 02 engineering and technology Machine learning computer.software_genre Feature (computer vision) Metric (mathematics) Information leakage 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business computer |
| Zdroj: | CCS |
| DOI: | 10.1145/3319535.3363272 |
| Popis: | The website fingerprinting attack allows a low-resource attacker to compromise the privacy guarantees provided by privacy enhancing tools such as Tor. In response, researchers have proposed defenses aimed at confusing the classification tools used by attackers. As new, more powerful attacks are frequently developed, raw attack accuracy has proven inadequate as the sole metric used to evaluate these defenses. In response, two security metrics have been proposed that allow for evaluating defenses based on hand-crafted features often used in attacks. Recent state-of-the-art attacks, however, use deep learning models capable of automatically learning abstract feature representations, and thus the proposed metrics fall short once again. In this study we examine two security metrics and (1) show how these methods can be extended to evaluate deep learning-based website fingerprinting attacks, and (2) compare the security metrics and identify their shortcomings. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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