35 citations · 49 across the 4 of their papers we have counts for
5 papers
Output Randomization: A Novel Defense for both White-box and Black-box Adversarial Models
Daniel Park, Haidar Khan, Azer Khan +2
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model in a "white box" s…
A survey on practical adversarial examples for malware classifiers
Daniel Park, Bülent Yener
Machine learning based solutions have been very helpful in solving problems that deal with immense amounts of data, such as malware detection and classification. However, deep neur…
Towards Obfuscated Malware Detection for Low Powered IoT Devices
Daniel Park, Hannah Powers, Benji Prashker +2
With the increased deployment of IoT and edge devices into commercial and user networks, these devices have become a new threat vector for malware authors. It is imperative to prot…
Thwarting finite difference adversarial attacks with output randomization
Haidar Khan, Daniel Park, Azer Khan +1
Adversarial examples pose a threat to deep neural network models in a variety of scenarios, from settings where the adversary has complete knowledge of the model and to the opposit…
Generation & Evaluation of Adversarial Examples for Malware Obfuscation
Daniel Park, Haidar Khan, Bülent Yener
There has been an increased interest in the application of convolutional neural networks for image based malware classification, but the susceptibility of neural networks to advers…