计算机科学 ›› 2020, Vol. 47 ›› Issue (4): 298-304.doi: 10.11896/jsjkx.190700132
孙志强, 万良, 丁红卫
SUN Zhi-qiang, WAN Liang, DING Hong-wei
摘要: 针对传统Android恶意软件检测方法检测率低的问题,文中提出一种基于深度收缩降噪自编码网络(Deep Contractive Denoising Autoencoder Network,DCDAN)的Android恶意软件检测方法。首先,逆向分析APK文件获取文件中的权限、敏感API等7类信息,并将其作为特征属性;然后,将特征属性作为深度收缩降噪自编码网络的输入,使用贪婪算法自底向上逐层训练每个收缩降噪自编码网络(Contractive Denoising Autoencoder Network),将训练完成的深度收缩降噪自编码网络用于原始特征的信息抽取,以获取最优的低维表示;最后,使用反向传播算法对获取的低维表示进行训练和分类,实现对Android恶意软件的检测。对深度自编码网络的输入数据添加噪声,使得重构的数据具有更强的鲁棒性,同时加入雅克比矩阵作为惩罚项,增强了深度自编码网络的抗扰动能力。实验结果验证了该方法的可行性和高效性。与传统的检测方法相比,该检测方法有效地提高了对恶意软件检测的准确率并降低了误报率。
中图分类号:
[1]QING S H.Research Progress on Android Security [J].Journal of Software,2016,27(1):45-71. [2]VINOD P,AKKA Z,MAURO C.A machine learning based approach to detect malicious android apps using discriminant system calls[J].Future Generation Computer Systems,2019,94:333-350. [3]HE G F,XU B F,ZHU H T.AppFA:A Novel Approach to Detect Malicious Android Applications on the Network[J].Security and Communication Networks,2018,2018(4):1-15. [4]SAPNA M,KIRAN K.Malicious Application Detection andClassification System for Android Mobiles[J].International Journal of Ambient Computing and Intelligence,2018,9:95-114. [5]MARIOS A,NICOLA D,ANGELO S.Analysis and Evaluation of SafeDroid v2.0,a Framework for Detecting Malicious Android Applications[J].Security and Communication Networks,2018(1):1-15. [6]WANG W,LI Y Y,WANG X,et al.Detecting Android malicious apps and categorizing benign apps with ensemble of classifiers[J].Future Generation Computer Systems,2018,78(3):987-994. [7]JUERGEN S.Deep Learning in Neural Networks:An Overview[J].Neural Networks,2015,61:85-117. [8]PRASHANT K,PANKAJ K.A Novel Approach for Detecting Malware in Android Applications Using Deep Learning[C]//2018 Eleventh International Conference on Contemporary Computing(IC3).IEEE Computer Society,2018,1:1-4. [9]LI D F,WANG Z G,XUE Y B.Fine-grained Android Malware Detection based on Deep Learning[C]//2018 IEEE Conference on Communications and Network Security(CNS).Beijing:IEEE,2018:1-2. |
[1] | 廖勇, 杨馨怡, 夏茂菡, 王博, 李守智, 沈轩帆. 高速移动场景下基于贪婪算法的改进模代数预编码 Improved Tomlinson-Harashima Precoding Based on Greedy Algorithm in High-speed Mobile Scenarios 计算机科学, 2019, 46(8): 121-126. https://doi.org/10.11896/j.issn.1002-137X.2019.08.020 |
[2] | 郑斐峰, 蒋娟, 梅启煌. 最小化集装箱运输成本的配载优化 Study on Stowage Optimization in Minimum Container Transportation Cost 计算机科学, 2019, 46(6): 239-245. https://doi.org/10.11896/j.issn.1002-137X.2019.06.036 |
[3] | 余建军, 吴春明. 基于禁忌遗传优化的离线静态虚拟网映射算法 Offline Static Virtual Network Mapping Algorithm Based on Tabu Search Genetic Optimization 计算机科学, 2019, 46(12): 114-119. https://doi.org/10.11896/jsjkx.181001981 |
[4] | 杜秀丽,顾斌斌,胡兴,邱少明,陈波. 用于图像重构的基于行间支撑集相似度的CoSaMP算法 Support Similarity between Lines Based CoSaMP Algorithm for Image Reconstruction 计算机科学, 2018, 45(4): 306-311. https://doi.org/10.11896/j.issn.1002-137X.2018.04.052 |
[5] | 宁卓,邵达成,陈勇,孙知信. 基于签名与数据流模式挖掘的Android恶意软件检测系统 Android Static Analysis System Based on Signature and Data Flow Pattern Mining 计算机科学, 2017, 44(Z11): 317-321. https://doi.org/10.11896/j.issn.1002-137X.2017.11A.067 |
[6] | 魏霖静,练智超,王联国,侯振兴. 基于词条与语意差异度量的文档聚类算法 Term and Semantic Difference Metric Based Document Clustering Algorithm 计算机科学, 2016, 43(12): 229-233. https://doi.org/10.11896/j.issn.1002-137X.2016.12.042 |
[7] | 刘 梓,宋晓宁,唐振民. 整合原始人脸图像和其虚拟样本的人脸分类算法 Integrating Original Images and its Virtual Samples for Face Recognition 计算机科学, 2015, 42(5): 289-294. https://doi.org/10.11896/j.issn.1002-137X.2015.05.059 |
[8] | 蔡旭,谢正光,蒋小燕,黄宏伟. 基于压缩感知的步长自适应前向后向追踪重建算法 Adaptive Step Length Forward-backward Pursuit Algorithm for Signal Reconstruction Based on Compressed Sensing 计算机科学, 2014, 41(11): 169-174. https://doi.org/10.11896/j.issn.1002-137X.2014.11.033 |
[9] | 杨云,章国安,邱恭安. 认知无线Mesh网络中基于概率的贪婪频谱决策技术研究 Research of Probability-based Greedy Spectrum Decision in Cognitive Wireless Mesh Networks 计算机科学, 2012, 39(Z6): 163-165. |
[10] | 刘曙光 郑崇勋. 前馈神经网络中的反向传播算法及其改进:进展与展望 计算机科学, 1996, 23(1): 76-79. |
|