Transfer Learning for Security: Challenges and Future Directions

arXiv:2403.00935v1 Announce Type: cross
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 …

abstract algorithms arxiv challenges cs.cr cs.lg data data mining distribution domain feature future instance machine machine learning mining security space testing training transfer transfer learning type