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amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth
Mosquera, Alejandro
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{
"publisher": "Zenodo",
"DOI": "10.5281/zenodo.5534783",
"language": "eng",
"title": "amsqr at MLSEC-2021: Thwarting Adversarial Malware Evasion with a Defense-in-Depth",
"issued": {
"date-parts": [
[
2021,
9,
28
]
]
},
"abstract": "<p>This paper describes the author's participation in the 3rd edition of the Machine Learning Security Evasion Competition (MLSEC-2021) sponsored by CUJO AI, VM-Ray, MRG-Effitas, Nvidia and Microsoft. As in the previous year the goal was not only developing measures against adversarial attacks on a pre-defined set of malware samples but also finding ways of bypassing other teams' defenses in a simulated cloud environment. The submitted solutions were ranked second in both defender and attacker tracks.</p>",
"author": [
{
"family": "Mosquera, Alejandro"
}
],
"type": "article",
"id": "5534783"
}
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| All versions | This version | |
|---|---|---|
| Views | 339 | 339 |
| Downloads | 220 | 220 |
| Data volume | 18.6 MB | 18.6 MB |
| Unique views | 279 | 279 |
| Unique downloads | 199 | 199 |
Indexed in
- Publication date:
- September 28, 2021
- DOI:
-
- Keyword(s):
- Adversarial machine learning Malware detection MLSEC Static malware detection
- License (for files):
- Creative Commons Attribution 4.0 International