model ,” he explains .
“ For further precision improvement , the models should undergo periodic retraining to ensure they adapt as fraud patterns change .”
Secondly , by thinking like a fraudster , you can give the model what it needs to know about threats in order to properly recognise them .
Putting a cybersecurity cap on , Ariel explains : “ One can use the information related to the latest tactics and tools shared within dark web circles to train ML models to stay one step ahead in detecting and preventing fraud .
“ For example , once learning that fraudsters use certain RATs to steal users ’ identities during supposedly banking sessions or shady VPNs to hide their IP address , you can train the model to recognise those specific tools and patterns in your traffic and thus reject only fraudsters ,” Ariel concludes .
While ML has long been a cornerstone
146 September 2024