IIoT Deep Malware Threat Hunting: From Adversarial Example Detection to Adversarial Scenario Detection-Reference-Cited by-同舟云学术

IIoT Deep Malware Threat Hunting: From Adversarial Example Detection to Adversarial Scenario Detection

Author:

Esmaeili Bardia¹^ORCID,Azmoodeh Amin¹,Dehghantanha Ali¹^ORCID,Karimipour Hadis²^ORCID,Zolfaghari Behrouz¹,Hammoudeh Mohammad³^ORCID

Affiliation:

1. Cyber Science Laboratory, School of Computer Science, University of Guelph, Guelph, ON, Canada

2. Electrical and Computer Engineering, University of Calgary, Calgary, AB, Canada

3. Department of Information and Computer Science, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

Reference24 articles.

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2. Intriguing properties of neural networks;szegedy;Int Conf on Learning Representations,2014

4. Hashtran-dnn: A framework for enhancing robustness of deep neural networks against adversarial malware samples;li,2018

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