DeepIncept: Diversify Performance Counters with Deep Learning to Detect Malware
Author:
Affiliation:
1. Old Dominion University,Department of Computer Science,Norfolk,VA,USA
2. University of Arizona,Department of Electrical and Computer Engineering,Tucson,AZ,USA
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx7/10473777/10473787/10473871.pdf?arnumber=10473871
Reference21 articles.
1. Malware Detection in Embedded Systems Using Neural Network Model for Electromagnetic Side-Channel Signals
2. 2SMaRT: A Two-Stage Machine Learning-Based Approach for Run-Time Specialized Hardware-Assisted Malware Detection
3. DeepPower: Non-intrusive and Deep Learning-based Detection of IoT Malware Using Power Side Channels
4. ThingNet: A Lightweight Real-time Mirai IoT Variants Hunter through CPU Power Fingerprinting
5. IoT botnet detection via power consumption modeling
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