HARD-Lite: A Lightweight Hardware Anomaly Realtime Detection Framework Targeting Ransomware
Author:
Affiliation:
1. Department of Electrical and Computer Engineering, Clarkson University, Potsdam, NY, USA
Funder
Nicklas-Ignite Research Fellowship, Clarkson University
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Electrical and Electronic Engineering,Hardware and Architecture
Link
http://xplorestaging.ieee.org/ielx7/8919/10363413/10208245.pdf?arnumber=10208245
Reference38 articles.
1. UNVEIL: A large-scale, automated approach to detecting ransomware;Kharaz
2. SSD-assisted Ransomware Detection and Data Recovery Techniques
3. A Multi-Classifier Network-Based Crypto Ransomware Detection System: A Case Study of Locky Ransomware
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1. An intelligent ransomware attack detection and classification using dual vision transformer with Mantis Search Split Attention Network;Computers and Electrical Engineering;2024-10
2. Ransomware Detection Using Machine Learning: A Review, Research Limitations and Future Directions;IEEE Access;2024
3. Top Cyber Threats: The Rise of Ransomware;Lecture Notes in Computer Science;2024
4. A Comparison of One-class and Two-class Models for Ransomware Detection via Low-level Hardware Information;2023 Asian Hardware Oriented Security and Trust Symposium (AsianHOST);2023-12-13
5. Guest Editorial Special Issue on the Asian Hardware Oriented Security and Trust Symposium (AsianHOST 2022);IEEE Transactions on Circuits and Systems I: Regular Papers;2023-12
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