363 citations · 485 across the 18 of their papers we have counts for
4 papers · 1 filter
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…
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…
On the Security of Key Extraction from Measuring Physical Quantities
Matt Edman, Aggelos Kiayias, Qiang Tang +1
Key extraction via measuring a physical quantity is a class of information theoretic key exchange protocols that rely on the physical characteristics of the communication channel t…