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.
Economic incentives encourage malware authors to constantly develop new, increasingly complex malware to steal sensitive data or blackmail individuals and companies into paying large ransoms.
The original model shows an accuracy of 59% under AutoAttack - when trained with additional data with pseudo-labels.
To this date, CAPTCHAs have served as the first line of defense preventing unauthorized access by (malicious) bots to web-based services, while at the same time maintaining a trouble-free experience for human visitors.
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.
Machine Learning (ML) algorithms are used to train computers to perform a variety of complex tasks and improve with experience.