• 中国精品科技期刊
  • CCF推荐A类中文期刊
  • 计算领域高质量科技期刊T1类
Advanced Search
Zhang Bing, Wen Zheng, Wei Xiaoyu, Ren Jiadong. InterDroid: An Interpretable Android Malware Detection Method for Conceptual Drift[J]. Journal of Computer Research and Development, 2021, 58(11): 2456-2474. DOI: 10.7544/issn1000-1239.2021.20210560
Citation: Zhang Bing, Wen Zheng, Wei Xiaoyu, Ren Jiadong. InterDroid: An Interpretable Android Malware Detection Method for Conceptual Drift[J]. Journal of Computer Research and Development, 2021, 58(11): 2456-2474. DOI: 10.7544/issn1000-1239.2021.20210560

InterDroid: An Interpretable Android Malware Detection Method for Conceptual Drift

Funds: This work was supported by the National Natural Science Foundation of China (61802332, 61807028, 61772449) and the Doctoral Foundation Program of Yanshan University (BL18012).
More Information
  • Published Date: October 31, 2021
  • Aiming at the problems in Android malware detection, which are high subjectivity of feature definition, poor interpretability of feature selection process, and lack of temporal instability of training model detection accuracy, an interpretable Android malware detection method for concept drift called InterDroid is proposed. Firstly, four characteristics of the detection model: permission, API package name, intention and Dalvik bytecode are inferred through the high-quality artificial Android malware analysis report. And InterDroid training and comparison algorithm are obtained through automatic machine learning algorithm TPOT (tree-based tipeline optimization tool), thus abandoning the complicated process of model selection and parameter adjustment in traditional methods. After that, the traditional feature wrapper method is improved by integrating the model interpretation algorithm SHAP (shapley additive explanations), and the feature set with high contribution to the classification results is obtained for detection model training. Finally, the existence of concept drift in Android malware detection is proved by the double tests of MWU(Mann-Whitney U) and machine learning model. Based on the JDA(joint distribution adaptation), the accuracy of the detection model for Android malware in the new era is improved. The experimental results show that the feature screened by InterDroid is stable and interpretable. At the same time, the feature-representation transfer module in InterDroid can improve the detection accuracy of Android malware in 2019 and 2020 by 46% and 44%.
  • Related Articles

    [1]Bai Tian, Xiao Mingyu. Computational Complexity of Feedback Set and Subset Feedback Set Problems: A Survey[J]. Journal of Computer Research and Development, 2025, 62(1): 104-118. DOI: 10.7544/issn1000-1239.202330693
    [2]Chen Yewang, Cao Hailu, Chen Yi, Kang Zhao, Lei Zhen, Du Jixiang. Survey on DBSCAN Acceleration Algorithms for Large Scale Data[J]. Journal of Computer Research and Development, 2023, 60(9): 2028-2047. DOI: 10.7544/issn1000-1239.202220311
    [3]Liu Jiefang, Wang Shitong, Wang Jun, Deng Zhaohong. Core Vector Regression for Attribute Effect Control on Large Scale Dataset[J]. Journal of Computer Research and Development, 2017, 54(9): 1979-1991. DOI: 10.7544/issn1000-1239.2017.20160519
    [4]Wang Hongkun, Cao Yi, Xiao Li. Image-Based Interactive Visualization of Large-Scale Data Sets[J]. Journal of Computer Research and Development, 2017, 54(4): 855-860. DOI: 10.7544/issn1000-1239.2017.20151056
    [5]Dong Rongsheng, Zhang Xinkai, Liu Huadong, Gu Tianlong. Representation and Operations Research of k\+2-MDD in Large-Scale Graph Data[J]. Journal of Computer Research and Development, 2016, 53(12): 2783-2792. DOI: 10.7544/issn1000-1239.2016.20160589
    [6]Zhou Enqiang, Zhang Wei, Lu Yutong, Hou Hongjun, Dong Yong. A Cache Approach for Large Scale Data-Intensive Computing[J]. Journal of Computer Research and Development, 2015, 52(7): 1522-1530. DOI: 10.7544/issn1000-1239.2015.20148073
    [7]Yu Jing, Liu Yanbing, Zhang Yu, Liu Mengya, Tan Jianlong, Guo Li. Survey on Large-Scale Graph Pattern Matching[J]. Journal of Computer Research and Development, 2015, 52(2): 391-409. DOI: 10.7544/issn1000-1239.2015.20140188
    [8]Ying Wenhao, Xu Min, Wang Shitong, Deng Zhaohong. Fast Adaptive Clustering by Synchronization on Large Scale Datasets[J]. Journal of Computer Research and Development, 2014, 51(4): 707-720.
    [9]Song Huaiming, An Mingyuan, Wang Yang, Yuan Chunyang, Sun Ninghui. Duplication Elimination in Large Scale Data Intensive Systems[J]. Journal of Computer Research and Development, 2010, 47(4): 581-588.
    [10]Pan Rui, Zhu Daming, and Ma Shaohan. Research on Computational Complexity and Approximation Algorithm for General Facility Location Problem[J]. Journal of Computer Research and Development, 2007, 44(5): 790-797.
  • Cited by

    Periodical cited type(26)

    1. 陈威,吕莉,肖人彬,谭德坤,潘正祥. 面向混合数据的对称邻域和微簇合并密度峰值聚类算法. 智能系统学报. 2025(01): 172-184 .
    2. 马福民,宫婷,杨帆,张腾飞. 基于Zipf分布的网格密度峰值聚类算法. 控制与决策. 2024(02): 577-587 .
    3. 刘继,杨金瑞. 基于网格近邻优化的密度峰值聚类算法. 计算机应用研究. 2024(04): 1058-1063 .
    4. 丁雨,张瀚霖,罗荣,孟华. 基于信念子簇切割的模糊聚类算法. 计算机应用. 2024(04): 1128-1138 .
    5. 杨金瑞,刘继. 基于网格的半监督密度峰值聚类算法. 软件工程. 2024(05): 1-6 .
    6. 仵匀政,杜韬,周劲,陈迪,王心耕. 基于三阶张量的大规模数据谱聚类集成算法. 大数据. 2024(03): 133-148 .
    7. 王丽娟,徐晓,丁世飞. 面向密度峰值聚类的高效相似度度量. 山东大学学报(工学版). 2024(03): 12-21+29 .
    8. 夏金平,高莲,李鹏,陈昌川. 自适应多元优化局部放电识别算法研究. 电子测量技术. 2024(14): 10-17 .
    9. 刘昊双,张永,曹莹波. 基于K-means聚类的子结构相关适配迁移学习方法. 电信科学. 2023(03): 124-134 .
    10. 马振明,安俊秀. 基于空间向量搜索的密度峰值聚类算法. 计算机工程与应用. 2023(15): 123-131 .
    11. 陈刚,王志坚,徐胜超. 基于强化学习的移动边缘计算任务卸载方法. 计算机测量与控制. 2023(10): 306-311+316 .
    12. 薛状状,李鹏,樊卫北,张宏俊,孟凡朔. 基于动态加权张量距离的多聚类算法. 计算机应用. 2023(11): 3449-3456 .
    13. 李文全,毛伊敏,彭新东. 基于犹豫模糊集的凝聚式层次聚类算法. 计算机应用. 2023(12): 3755-3763 .
    14. 徐晓,丁世飞,丁玲. 密度峰值聚类算法研究进展. 软件学报. 2022(05): 1800-1816 .
    15. 张华,杨磊. 基于密度梯度的滑动窗口数据流任意形状聚类. 计算机仿真. 2022(04): 316-320 .
    16. 颜烨,张学文,王立婧. Spark平台上利用网络加权Voronoi图的分散迭代社区聚类并行化研究. 计算机应用与软件. 2021(03): 14-21+38 .
    17. 何云斌,董恒,万静. 移动型数据与静态型数据的混合聚类算法. 哈尔滨理工大学学报. 2021(02): 26-34 .
    18. 杜淑颖,施天豪,丁世飞. 基于电子分层模型和凝聚策略的密度峰值聚类. 南京理工大学学报. 2021(04): 385-393 .
    19. 田彦彦,孙静. 分块自适应加权改进大规模模糊聚类. 机械设计与制造. 2021(09): 279-282 .
    20. 宋紫阳,张菁,刘小康,刘传修. 基于微簇融合的密度峰值聚类算法. 传感器与微系统. 2021(10): 132-135 .
    21. 张唯一,张菁. Shapley值法在配电网供电能力计算中的应用. 传感器与微系统. 2021(10): 155-157 .
    22. 朱二周,孙悦,张远翔,高新,马汝辉,李学俊. 一种采用新型聚类方法的最佳类簇数确定算法. 软件学报. 2021(10): 3085-3103 .
    23. 何云斌,董恒,万静,李松. 基于密度峰值和近邻优化的聚类算法. 计算机科学与探索. 2020(04): 554-565 .
    24. 任昌鸿,安军. 改进PSO结合DSA技术的无线传感器网络均衡密度聚类方法. 计算机应用与软件. 2020(08): 122-129 .
    25. 何倩,李双富,黄焕,徐红. 一种海量数据快速聚类算法. 北京邮电大学学报. 2020(03): 118-124 .
    26. 高蕴梅. MGTL-SAE精细特征学习的图像资源快速检索. 情报资料工作. 2020(05): 79-87 .

    Other cited types(22)

Catalog

    Article views (555) PDF downloads (351) Cited by(48)

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return
    Return