Cross-validation for detecting label poisoning attacks: a study on random forest algorithm.
(2024)
Presentation / Conference Contribution
YASARATHNA, T.L., MUNASINGHE, L., KALUTARAGE, H. and LE-KHAC, N.-A. 2024. Cross-validation for detecting label poisoning attacks: a study on random forest algorithm. In Pitropakis, N., Katsikas, S., Furnell, S. and Markantonakis, K. (eds.) Proceedings of the 39th International Federation for Information Processing (IFIP) International conference on ICT systems security and privacy protection 2024 (IFIP SEC 2024), 12-14 June 2024, Edinburgh, UK. IFIP Advances in information and communication technology, 710. Cham: Springer [online], pages 451-464. Available from: https://doi.org/10.1007/978-3-031-65175-5_32
The widespread adoption of machine learning (ML) algorithms has revolutionized various aspects of modern life. However, their susceptibility to data poisoning attacks remains a significant concern due to their potential to compromise model integrity... Read More about Cross-validation for detecting label poisoning attacks: a study on random forest algorithm..