| 基于Stacking的恶意网页集成检测方法 |
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| 引用本文： | 朴杨鹤然,任俊玲. 基于Stacking的恶意网页集成检测方法[J]. 计算机应用, 2019, 39(4): 1081-1088. DOI: 10.11772/j.issn.1001-9081.2018091926 |
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| 作者姓名： | 朴杨鹤然 任俊玲 |
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| 作者单位： | 北京信息科技大学信息管理学院,北京,100192;北京信息科技大学信息管理学院,北京,100192 |
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| 基金项目： | 国家自然科学基金资助项目（71571021）；北京市教育委员会科技计划项目（KM201811232019）。 |
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| 摘 要： | 针对目前主流恶意网页检测技术耗费资源多、检测周期长和分类效果低等问题,提出一种基于Stacking的恶意网页集成检测方法,将异质分类器集成的方法应用在恶意网页检测识别领域。通过对网页特征提取分析相关因素和分类集成学习来得到检测模型,其中初级分类器分别使用K近邻(KNN)算法、逻辑回归算法和决策树算法建立,而次级的元分类器由支持向量机(SVM)算法建立。与传统恶意网页检测手段相比,此方法在资源消耗少、速度快的情况下使识别准确率提高了0.7%,获得了98.12%的高准确率。实验结果表明,所提方法构造的检测模型可高效准确地对恶意网页进行识别。
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| 关 键 词： | 恶意网页 机器学习 分类器集成 STACKING |
| 收稿时间： | 2018-09-17 |
| 修稿时间： | 2018-10-31 |
Malicious webpage integrated detection method based on Stacking ensemble algorithm |
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| PIAOYANG Heran,REN Junling. Malicious webpage integrated detection method based on Stacking ensemble algorithm[J]. Journal of Computer Applications, 2019, 39(4): 1081-1088. DOI: 10.11772/j.issn.1001-9081.2018091926 |
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| Authors: | PIAOYANG Heran REN Junling |
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| Affiliation: | School of Information Management, Beijing Information Science & Technology University, Beijing 100192, China |
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| Abstract: | Aiming at the problems of excessive cost of resource, long detection period and low classification effect of mainstream malicious webpage detection technology, a Stacking-based malicious webpage integrated detection method was proposed, with heterogeneous classifiers integration method applying to malicious webpage detection and recognition. By extracting and analyzing the relevant factors of webpage features, and performing classification and ensemble learning, the detection model was obtained. In the detection model, the primary classifiers were constructed based on K-Nearest Neighbors (KNN) algorithm, logistic regression algorithm and decision tree algorithm respectively, and Support Vector Machine (SVM) classifier was used for the construction of secondary classifier. Compared with the traditional malicious webpage detection methods, the proposed method improves the recognition accuracy by 0.7% and obtains a high accuracy of 98.12% in the condition of low resource consumption and high velocity. The experimental results show that the detection model constructed by the proposed method can recognize malicious webpages efficiently and accurately. |
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| Keywords: | malicious webpage machine learning classifier ensemble Stacking |
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