| 改进贝叶斯算法在未知恶意软件识别中的研究 |
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| 引用本文： | 赖英旭, 杨震. 改进贝叶斯算法在未知恶意软件识别中的研究[J]. 北京工业大学学报, 2011, 37(5): 766-772. |
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| 作者姓名： | 赖英旭 杨震 |
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| 作者单位： | 1.北京工业大学 计算机学院, 北京 100124 |
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| 基金项目： | 国家重点基础研究发展计划资助项目(2007CB311100); 北京市教委基金资助项目(KM200810005030); 北京市自然科学基金资助项目(4102012); 国家自然科学基金资助项目(61001178) |
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| 摘 要： | 为改进朴素贝叶斯(naive Bayes,NB)算法在识别未知恶意代码过程中学习速度慢的缺点,在分析研究朴素贝叶斯算法、复合贝叶斯(multi-naive Bayes,MNB)算法的基础上,提出了一种改进贝叶斯(half-increm entnaive Bayes,HNB)算法.算法采用特征集增量学习方式,在保证分类精度不降低的前提下,学习速度提高约30%.实际样本测试表明,分类精度达到了96%,其中对已知恶意代码的分类精度达到99%.
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| 关 键 词： | 分类器 数据挖掘 贝叶斯算法 |
| 收稿时间： | 2009-04-03 |
Unknown Malicious Detection Based on Improved Bayes Algorithm |
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| LAI Ying-xu, YANG Zhen. Unknown Malicious Detection Based on Improved Bayes Algorithm[J]. Journal of Beijing University of Technology, 2011, 37(5): 766-772. |
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| Authors: | LAI Ying-xu YANG Zhen |
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| Affiliation: | 1.College of Computer Science, Beijing University of Technology, Beijing 100124, China |
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| Abstract: | The detection of unknown malicious executables is beyond the capability of many existing detection approaches.Machine learning or data mining method can identify new or unknown malicious executables with some degree of success.Bayes or improved Bayes algorithm has the detection capability of unknown malicious excutables;however,it takes more time to study.A new improved algorithm is proposed in this paper.The new classifier based on strings achieve has high detection rates and can be expected to perform as ... |
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| Keywords: | classifier data mining bayes methods |
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