25 citations · 35 across the 5 of their papers we have counts for
6 papers · 1 filter
Deep Transfer Learning for Static Malware Classification
Li Chen
We propose to apply deep transfer learning from computer vision to static malware classification. In the transfer learning scheme, we borrow knowledge from natural images or object…
Towards resilient machine learning for ransomware detection
Li Chen, Chih-Yuan Yang, Anindya Paul +1
There has been a surge of interest in using machine learning (ML) to automatically detect malware through their dynamic behaviors. These approaches have achieved significant improv…
ADAGIO: Interactive Experimentation with Adversarial Attack and Defense for Audio
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen +3
Adversarial machine learning research has recently demonstrated the feasibility to confuse automatic speech recognition (ASR) models by introducing acoustically imperceptible pertu…
Shield: Fast, Practical Defense and Vaccination for Deep Learning using JPEG Compression
Nilaksh Das, Madhuri Shanbhogue, Shang-Tse Chen +5
The rapidly growing body of research in adversarial machine learning has demonstrated that deep neural networks (DNNs) are highly vulnerable to adversarially generated images. This…
Vertex nomination: The canonical sampling and the extended spectral nomination schemes
Jordan Yoder, Li Chen, Henry Pao +5
Suppose that one particular block in a stochastic block model is of interest, but block labels are only observed for a few of the vertices in the network. Utilizing a graph realize…
HeNet: A Deep Learning Approach on Intel Processor Trace for Effective Exploit Detection
Li Chen, Salmin Sultana, Ravi Sahita
This paper presents HeNet, a hierarchical ensemble neural network, applied to classify hardware-generated control flow traces for malware detection. Deep learning-based malware det…