Being trained on proprietary information, these models provide a competitive edge for the owner company.
Proposed as a solution to mitigate the privacy implications related to the adoption of deep learning, Federated Learning (FL) enables large numbers of participants to successfully train deep neural networks without having to reveal the actual private training data.
The original model shows an accuracy of 59% under AutoAttack - when trained with additional data with pseudo-labels.
To address this issue, we design EnCoD, a learning-based classifier which can reliably distinguish compressed and encrypted data, starting with fragments as small as 512 bytes.
Recent progress in machine learning has generated promising results in behavioral malware detection.