Deep Neural Network and Transfer Learning for Accurate Hardware-Based Zero-Day Malware Detection

被引:12
作者
He, Zhangying [1 ]
Rezaei, Amin [1 ]
Homayoun, Houman [2 ]
Sayadi, Hossein [1 ]
机构
[1] Calif State Univ, Long Beach, CA 90032 USA
[2] Univ Calif Davis, Davis, CA 95616 USA
来源
PROCEEDINGS OF THE 32ND GREAT LAKES SYMPOSIUM ON VLSI 2022, GLSVLSI 2022 | 2022年
关键词
Deep Learning; Hardware-Based Malware Detection; Machine Learning; Transfer Learning; Zero-Day Attack;
D O I
10.1145/3526241.3530326
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
In recent years, security researchers have shifted their attentions to the underlying processors' architecture and proposed Hardware-Based Malware Detection (HMD) countermeasures to address inefficiencies of software-based detection methods. HMD techniques apply standard Machine Learning (ML) algorithms to the processors' low-level events collected from Hardware Performance Counter (HPC) registers. However, despite obtaining promising results for detecting known malware, the challenge of accurate zero-day (unknown) malware detection has remained an unresolved problem in existing HPC-based countermeasures. Our comprehensive analysis shows that standard ML classifiers are not effective in recognizing zero-day malware traces using HPC events. In response, we propose Deep-HMD, a two-stage intelligent and flexible approach based on deep neural network and transfer learning, for accurate zero-day malware detection based on image-based hardware events. The experimental results indicate that our proposed solution outperforms existing ML-based methods by achieving a 97% detection rate (F-Measure and Area Under the Curve) for detecting zero-day malware signatures at run-time using the top 4 hardware events with a minimal false positive rate and no hardware redesign overhead.
引用
收藏
页码:27 / 32
页数:6
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