Search Results for author:
Found 10 papers, 1 papers with code
Evaluating the Robustness of Geometry-Aware Instance-Reweighted Adversarial Training
The original model shows an accuracy of 59% under AutoAttack - when trained with additional data with pseudo-labels.
The Naked Sun: Malicious Cooperation Between Benign-Looking Processes
Recent progress in machine learning has generated promising results in behavioral malware detection.
EnCoD: Distinguishing Compressed and Encrypted File Fragments
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.
Reliable Detection of Compressed and Encrypted Data
We evaluate EnCoD on a dataset of 16 different file types and fragment sizes ranging from 512B to 8KB.
PassFlow: Guessing Passwords with Generative Flows
In this paper, we propose PassFlow, a flow-based generative model approach to password guessing.
FedComm: Federated Learning as a Medium for Covert Communication
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.
TATTOOED: A Robust Deep Neural Network Watermarking Scheme based on Spread-Spectrum Channel Coding
Being trained on proprietary information, these models provide a competitive edge for the owner company.
Minerva: A File-Based Ransomware Detector
Ransomware attacks have caused billions of dollars in damages in recent years, and are expected to cause billions more in the future.
OliVaR: Improving Olive Variety Recognition using Deep Neural Networks
The morphological classification consists of the visual pairwise comparison of different organs of the olive tree, where the most important organ is considered to be the endocarp.
Do You Trust Your Model? Emerging Malware Threats in the Deep Learning Ecosystem
We introduce MaleficNet 2. 0, a novel technique to embed self-extracting, self-executing malware in neural networks.
