all InfoSec news
Efficient Malware Analysis Using Metric Embeddings. (arXiv:2212.02663v1 [cs.LG])
Dec. 7, 2022, 2:10 a.m. | Ethan M. Rudd, David Krisiloff, Scott Coull, Daniel Olszewski, Edward Raff, James Holt
cs.CR updates on arXiv.org arxiv.org
In this paper, we explore the use of metric learning to embed Windows PE
files in a low-dimensional vector space for downstream use in a variety of
applications, including malware detection, family classification, and malware
attribute tagging. Specifically, we enrich labeling on malicious and benign PE
files using computationally expensive, disassembly-based malicious
capabilities. Using these capabilities, we derive several different types of
metric embeddings utilizing an embedding neural network trained via contrastive
loss, Spearman rank correlation, and combinations thereof. We …
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
Information Technology Specialist II: Network Architect
@ Los Angeles County Employees Retirement Association (LACERA) | Pasadena, CA
Cybersecurity Skills Challenge -- Sponsored by DoD
@ Correlation One | United States
Security Operations Center (SOC) Analyst
@ GK Cybersecurity Group | Remote
Cyber Consultant
@ Frazer-Nash Consultancy | Gloucester, England, United Kingdom
Senior Vulnerability Management Reporting & Analytics Developer
@ Baker Hughes | IN-KA-BANGALORE-NEON BUILDING WEST TOWER
Product Security Architect
@ ChargePoint | Italy