一种基于威胁情报层次特征集成的挖矿恶意软件检测方法
Cryptojacking Malware Hunting: A Method Based on Ensemble Learning of Hierarchical Threat Intelligence Feature
查看参考文献22篇
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文摘
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挖矿恶意软件是近年来出现的一种新型恶意软件,其加密运算模式给受害用户带来巨大损失.通过研究挖矿恶意软件的静态特征,本文提出一种基于威胁情报层次特征集成的挖矿恶意软件检测方法.从挖矿恶意软件威胁情报的角度,本文分别使用字节特征层、PE(Portable Executable)结构特征层和挖矿操作执行特征层训练挖矿恶意软件分类器,利用不同恶意软件特征对恶意软件的检测偏好,使用集成方法在层次特征的基础上组建挖矿恶意软件检测器.在实验评估中,本文使用模拟实验室环境数据集和模拟真实世界数据集进行模型性能测试.实验结果表明,本文所设计的层次特征集成的挖矿恶意软件检测方法在模拟真实世界数据集上取得了97.01%的准确率,相对挖矿恶意软件检测基线方法获取了6.13%的准确率提升. |
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其他语种文摘
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Cryptojacking malware is a new type of malware that has emerged in recent years and poses a significant threat to user host security. By studying static features of cryptojacking malware, a detection method is proposed based on integrating hierarchical threat intelligence features. We train cryptojacking malware detectors using the raw byte feature, PE(Portable Executable)parsing feature, and cryptocurrency mining operation feature, respectively. Then, the ensemble learning is used for combining these detectors to form a cryptojacking malware detector from the perspective of hierarchical threat intelligence. In the experiments, the simulated lab dataset and the simulated real-world dataset are used for performance evaluation. The experimental results show that the proposed method acquires 97.01% accuracy rate, which gets improvements of 6.13% relative to the baseline method. |
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来源
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电子学报
,2022,50(11):2707-2715 【核心库】
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DOI
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10.12263/DZXB.20211333
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关键词
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挖矿恶意软件
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威胁情报
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机器学习
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集成学习
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深度学习
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区块链
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操作码特征
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地址
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1.
武汉大学国家网络安全学院, 空天信息安全与可信计算教育部重点实验室, 湖北, 武汉, 430072
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中国科学院信息工程研究所, 北京, 100093
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中国科学院大学网络空间安全学院, 北京, 100049
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语种
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中文 |
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文献类型
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研究性论文 |
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ISSN
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0372-2112 |
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学科
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自动化技术、计算机技术 |
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基金
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国家自然科学基金
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国家重点研发计划
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文献收藏号
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CSCD:7362394
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参考文献 共
22
共2页
|
|
1.
Tekiner E. Sok: cryptojacking malware.
2021 IEEE European Symposium on Security and Privacy(EuroS&P),2021:120-139
|
CSCD被引
2
次
|
|
|
|
|
2.
Pastrana S. A first look at the crypto-mining malware ecosystem: A decade of unrestricted wealth.
Proceedings of the Internet Measurement Conference(IMC),2019:73-86
|
CSCD被引
1
次
|
|
|
|
|
3.
安天.
六小时处置挖矿蠕虫的内网大规模感染事件,2019
|
CSCD被引
1
次
|
|
|
|
|
4.
Yazdinejad A. Cryptocurrency malware hunting: A deep recurrent neural network approach.
Applied Soft Computing,2020,96:106630
|
CSCD被引
5
次
|
|
|
|
|
5.
Naseem F. MINOS: a lightweight real-time cryptojacking detection system.
Proceedings of the 28th Network and Distributed System Security Symposium,2021:21-25
|
CSCD被引
1
次
|
|
|
|
|
6.
Konoth R K.
Malicious cryptocurrency miners: Status and outlook,2019
|
CSCD被引
1
次
|
|
|
|
|
7.
Kolter J Z. Learning to detect and classify malicious executables in the wild.
Journal of Machine Learning Research,2006,7(12):2721-2744
|
CSCD被引
20
次
|
|
|
|
|
8.
Nataraj L. Malware images: visualization and automatic classification.
Proceedings of the 8th International Symposium on Visualization for Cyber Security,2011:1-7
|
CSCD被引
33
次
|
|
|
|
|
9.
Kim J Y. Zero-day malware detection using transferred generative adversarial networks based on deep autoencoders.
Information Sciences,2018,460:83-102
|
CSCD被引
16
次
|
|
|
|
|
10.
Saxe J. Deep neural network based malware detection using two dimensional binary program features.
2015 10th International Conference on Malicious and Unwanted Software(MALWARE),2015:11-20
|
CSCD被引
3
次
|
|
|
|
|
11.
Raff E. Malware detection by eating a whole exe.
Workshops at the Thirty-Second AAAI Conference on Artificial Intelligence,2018:268-276
|
CSCD被引
3
次
|
|
|
|
|
12.
Raff E. Classifying sequences of extreme length with constant memory applied to malware detection.
Proceedings of the AAAI Conference on Artificial Intelligence,2021:9386-9394
|
CSCD被引
1
次
|
|
|
|
|
13.
Schultz M G. Data mining methods for detection of new malicious executables.
Proceedings 2001 IEEE Symposium on Security and Privacy(S&P),2000:38-49
|
CSCD被引
1
次
|
|
|
|
|
14.
Shafiq M Z. Pe-miner: mining structural information to detect malicious executables in realtime.
Recent Advances in Intrusion Detection 12th International Symposium(RAID),2009:121-141
|
CSCD被引
1
次
|
|
|
|
|
15.
Anderson H S.
Ember: an open dataset for training static pe malware machine learning models,2018
|
CSCD被引
5
次
|
|
|
|
|
16.
Microsoft Threat Intelligence Center.
Threat actor leverages coin miner techniques to stay under the radar-here's how to spot them,2020
|
CSCD被引
1
次
|
|
|
|
|
17.
Chan K H R.
ReduNet: a whitebox deep network from the principle of maximizing rate reduction,2021
|
CSCD被引
1
次
|
|
|
|
|
18.
Van Belle V. Explaining support vector machines: a color based nomogram.
PloS ONE,2016,11(10):e0164568
|
CSCD被引
2
次
|
|
|
|
|
19.
Kirasich K. Random forest vs logistic regression: binary classification for heterogeneous datasets.
SMU Data Science Review,2018,1(3):9
|
CSCD被引
1
次
|
|
|
|
|
20.
Aghakhani H. When malware is packin'heat; limits of machine learning classifiers based on static analysis features.
27th Annual Network and Distributed System Security Symposium,2020
|
CSCD被引
1
次
|
|
|
|
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