VoteDroid:基于微调深度学习模型的新型恶意软件检测集合投票分类器

IF 3 4区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Halit Bakır
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引用次数: 0

摘要

在这项工作中,我们提出了基于深度学习模型的新型微调集合投票分类器 VoteDroid,用于检测安卓应用程序中的恶意行为。为此,我们建议采用随机搜索优化算法来决定集合分类器中用作投票分类器的模型结构。我们指定了可用于每个模型的潜在组件,并让随机搜索算法决定模型的结构,包括应使用的每个组件的数量及其在结构上的位置。这种优化方法被用于构建三种不同的深度学习模型,即 CNN-ANN、纯 CNN 和纯 ANN。在为每个深度学习模型选择了最佳结构后,我们使用构建的图像数据集对所选的三个模型进行了训练和测试。之后,我们建议将微调后的三个深度学习模型混合起来,形成一个具有两种不同工作模式的集合投票分类器,即MMR(恶意软件少数规则)和LMR(标签多数规则)。据我们所知,这是首次以这种方式对用于恶意软件检测的集合分类器进行微调和混合。结果表明,所提出的模型很有前途,在所有实验中,分类准确率都超过了 97%。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models

VoteDroid: a new ensemble voting classifier for malware detection based on fine-tuned deep learning models

In this work, VoteDroid a novel fine-tuned deep learning models-based ensemble voting classifier has been proposed for detecting malicious behavior in Android applications. To this end, we proposed adopting the random search optimization algorithm for deciding the structure of the models used as voter classifiers in the ensemble classifier. We specified the potential components that can be used in each model and left the random search algorithm taking a decision about the structure of the model including the number of each component that should be used and its location in the structure. This optimization method has been used to build three different deep learning models namely CNN-ANN, pure CNN, and pure ANN. After selecting the best structure for each DL model, the selected three models have been trained and tested using the constructed image dataset. Afterward, we suggested hybridizing the fine-tuned three deep-learning models to form one ensemble voting classifier with two different working modes namely MMR (Malware Minority Rule) and LMR (Label Majority Rule). To our knowledge, this is the first time that an ensemble classifier has been fine-tuned and hybridized in this way for malware detection. The results showed that the proposed models were promising, where the classification accuracy exceeded 97% in all experiments.

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来源期刊
Multimedia Tools and Applications
Multimedia Tools and Applications 工程技术-工程:电子与电气
CiteScore
7.20
自引率
16.70%
发文量
2439
审稿时长
9.2 months
期刊介绍: Multimedia Tools and Applications publishes original research articles on multimedia development and system support tools as well as case studies of multimedia applications. It also features experimental and survey articles. The journal is intended for academics, practitioners, scientists and engineers who are involved in multimedia system research, design and applications. All papers are peer reviewed. Specific areas of interest include: - Multimedia Tools: - Multimedia Applications: - Prototype multimedia systems and platforms
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