Transcend¶
Jordaney, R., Sharad, K., Dash, S. K., Wang, Z., Papini, D., Nouretdinov, I., & Cavallaro, L. (2017). Transcend: Detecting Concept Drift in Malware Classification Models. 26th USENIX Security Symposium (USENIX Security 17), 625-642.
Cited by¶
1 citation across 1 artifact.
Each citation links to the sentence it supports in the citing article.
Primes¶
- Data Drift
- In cybersecurity, intrusion signatures and anti-fraud rules lose grip as adversaries adapt — a particularly fast, adversarial variant of drift.
This sourceShows signature/classification models for malware lose grip as adversaries adapt (adversarial concept drift), and detects model aging during deployment — the fast, adversarial variant of drift.
- In cybersecurity, intrusion signatures and anti-fraud rules lose grip as adversaries adapt — a particularly fast, adversarial variant of drift.
Verification¶
Does it exist? Not checked yet. This entry carries no identifier to resolve. It was extracted from the citation as written in the article, normalized, and deduplicated against the rest of the registry.
Does it back the claim? Not recorded. The single citation of this work carries no recorded support check.
Support is checked per citation rather than per work — the same source can be cited soundly in one article and wrongly in another. Per-citation recording began recently, so a citation with no recorded check is a gap in the record rather than evidence it went unchecked.
See how references were verified.
Registry ID ref:e721514b62f5 · see in the full table