all InfoSec news
Transfer Learning for Security: Challenges and Future Directions
March 5, 2024, 3:11 p.m. | Adrian Shuai Li, Arun Iyengar, Ashish Kundu, Elisa Bertino
cs.CR updates on arXiv.org arxiv.org
Abstract: Many machine learning and data mining algorithms rely on the assumption that the training and testing data share the same feature space and distribution. However, this assumption may not always hold. For instance, there are situations where we need to classify data in one domain, but we only have sufficient training data available from a different domain. The latter data may follow a distinct distribution. In such cases, successfully transferring knowledge across domains can significantly …
algorithms arxiv challenges cs.cr cs.lg data data mining distribution domain feature future instance machine machine learning may mining security share space testing training transfer
More from arxiv.org / cs.CR updates on arXiv.org
Jobs in InfoSec / Cybersecurity
CyberSOC Technical Lead
@ Integrity360 | Sandyford, Dublin, Ireland
Cyber Security Strategy Consultant
@ Capco | New York City
Cyber Security Senior Consultant
@ Capco | Chicago, IL
Sr. Product Manager
@ MixMode | Remote, US
Security Compliance Strategist
@ Grab | Petaling Jaya, Malaysia
Cloud Security Architect, Lead
@ Booz Allen Hamilton | USA, VA, McLean (1500 Tysons McLean Dr)