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¶
This reference passed the adversarial substantiation pipeline: it was checked to exist and to support the claim it is attached to. See how references were verified.
Registry ID ref:e721514b62f5 · see in the full table