The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware.These malicious applications have become a serious conc...The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware.These malicious applications have become a serious concern to the security of Android systems.To address this problem,researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples.However,most existing studies have focused on the classification task and overlooked the feature selection process,which is crucial to reduce the training time and maintain or improve the classification results.The current paper proposes a new Android malware detection and classification approach that identifies the most important features to improve classification performance and reduce training time.The proposed approach consists of two main steps.First,a feature selection method based on the Attention mechanism is used to select the most important features.Then,an optimized Light Gradient Boosting Machine(LightGBM)classifier is applied to classify the Android samples and identify the malware.The feature selection method proposed in this paper is to integrate an Attention layer into a multilayer perceptron neural network.The role of the Attention layer is to compute the weighted values of each feature based on its importance for the classification process.Experimental evaluation of the approach has shown that combining the Attention-based technique with an optimized classification algorithm for Android malware detection has improved the accuracy from 98.64%to 98.71%while reducing the training time from 80 to 28 s.展开更多
【目的】为满足地理实习中空间数据实时采集与管理的需求,提升野外实习工作效率,研发了一套基于Android平台的地理实习数据采集系统。【方法】在地理实习教学需求分析的基础上,以Android智能手机为移动终端,基于客户端—服务器体系结构...【目的】为满足地理实习中空间数据实时采集与管理的需求,提升野外实习工作效率,研发了一套基于Android平台的地理实习数据采集系统。【方法】在地理实习教学需求分析的基础上,以Android智能手机为移动终端,基于客户端—服务器体系结构的分布式模式,利用ArcGIS Runtime SDK for Android二次开发接口集成Android开发、物联网数据实时采集、SQLite数据库以及移动地图展示等移动GIS技术进行系统设计与研发。【结果】实现了用户登录、地图浏览、地理位置获取与显示、地理要素采集与管理、现场照片上传、退出系统等功能,并应用于地理实习教学。【结论】该系统不仅可用于地理实习、野外调查等场景,而且可用于实时采集、存储和显示地理数据。此外,该系统还具有较好的可扩展性和兼容性,也可以适用于其他外业工作场景。展开更多
隐私政策文档声明了应用程序需要获取的隐私信息,但不能保证清晰且完全披露应用获取的隐私信息类型,目前对应用实际敏感行为与隐私政策一致性分析的研究仍存在不足。针对上述问题,提出一种Android应用敏感行为与隐私政策一致性分析方法...隐私政策文档声明了应用程序需要获取的隐私信息,但不能保证清晰且完全披露应用获取的隐私信息类型,目前对应用实际敏感行为与隐私政策一致性分析的研究仍存在不足。针对上述问题,提出一种Android应用敏感行为与隐私政策一致性分析方法。在隐私政策分析阶段,基于Bi-GRU-CRF(Bi-directional Gated Recurrent Unit Conditional Random Field)神经网络,通过添加自定义标注库对模型进行增量训练,实现对隐私政策声明中的关键信息的提取;在敏感行为分析阶段,通过对敏感应用程序接口(API)调用进行分类、对输入敏感源列表中已分析过的敏感API调用进行删除,以及对已提取过的敏感路径进行标记的方法来优化IFDS(Interprocedural,Finite,Distributive,Subset)算法,使敏感行为分析结果与隐私政策描述的语言粒度相匹配,并且降低分析结果的冗余,提高分析效率;在一致性分析阶段,将本体之间的语义关系分为等价关系、从属关系和近似关系,并据此定义敏感行为与隐私政策一致性形式化模型,将敏感行为与隐私政策一致的情况分为清晰的表述和模糊的表述,将不一致的情况分为省略的表述、不正确的表述和有歧义的表述,最后根据所提基于语义相似度的一致性分析算法对敏感行为与隐私政策进行一致性分析。实验结果表明,对928个应用程序进行分析,在隐私政策分析正确率为97.34%的情况下,51.4%的Android应用程序存在应用实际敏感行为与隐私政策声明不一致的情况。展开更多
基金This work was funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under Grant No.(DGSSR-2023-02-02178).
文摘The dominance of Android in the global mobile market and the open development characteristics of this platform have resulted in a significant increase in malware.These malicious applications have become a serious concern to the security of Android systems.To address this problem,researchers have proposed several machine-learning models to detect and classify Android malware based on analyzing features extracted from Android samples.However,most existing studies have focused on the classification task and overlooked the feature selection process,which is crucial to reduce the training time and maintain or improve the classification results.The current paper proposes a new Android malware detection and classification approach that identifies the most important features to improve classification performance and reduce training time.The proposed approach consists of two main steps.First,a feature selection method based on the Attention mechanism is used to select the most important features.Then,an optimized Light Gradient Boosting Machine(LightGBM)classifier is applied to classify the Android samples and identify the malware.The feature selection method proposed in this paper is to integrate an Attention layer into a multilayer perceptron neural network.The role of the Attention layer is to compute the weighted values of each feature based on its importance for the classification process.Experimental evaluation of the approach has shown that combining the Attention-based technique with an optimized classification algorithm for Android malware detection has improved the accuracy from 98.64%to 98.71%while reducing the training time from 80 to 28 s.
文摘【目的】为满足地理实习中空间数据实时采集与管理的需求,提升野外实习工作效率,研发了一套基于Android平台的地理实习数据采集系统。【方法】在地理实习教学需求分析的基础上,以Android智能手机为移动终端,基于客户端—服务器体系结构的分布式模式,利用ArcGIS Runtime SDK for Android二次开发接口集成Android开发、物联网数据实时采集、SQLite数据库以及移动地图展示等移动GIS技术进行系统设计与研发。【结果】实现了用户登录、地图浏览、地理位置获取与显示、地理要素采集与管理、现场照片上传、退出系统等功能,并应用于地理实习教学。【结论】该系统不仅可用于地理实习、野外调查等场景,而且可用于实时采集、存储和显示地理数据。此外,该系统还具有较好的可扩展性和兼容性,也可以适用于其他外业工作场景。
文摘隐私政策文档声明了应用程序需要获取的隐私信息,但不能保证清晰且完全披露应用获取的隐私信息类型,目前对应用实际敏感行为与隐私政策一致性分析的研究仍存在不足。针对上述问题,提出一种Android应用敏感行为与隐私政策一致性分析方法。在隐私政策分析阶段,基于Bi-GRU-CRF(Bi-directional Gated Recurrent Unit Conditional Random Field)神经网络,通过添加自定义标注库对模型进行增量训练,实现对隐私政策声明中的关键信息的提取;在敏感行为分析阶段,通过对敏感应用程序接口(API)调用进行分类、对输入敏感源列表中已分析过的敏感API调用进行删除,以及对已提取过的敏感路径进行标记的方法来优化IFDS(Interprocedural,Finite,Distributive,Subset)算法,使敏感行为分析结果与隐私政策描述的语言粒度相匹配,并且降低分析结果的冗余,提高分析效率;在一致性分析阶段,将本体之间的语义关系分为等价关系、从属关系和近似关系,并据此定义敏感行为与隐私政策一致性形式化模型,将敏感行为与隐私政策一致的情况分为清晰的表述和模糊的表述,将不一致的情况分为省略的表述、不正确的表述和有歧义的表述,最后根据所提基于语义相似度的一致性分析算法对敏感行为与隐私政策进行一致性分析。实验结果表明,对928个应用程序进行分析,在隐私政策分析正确率为97.34%的情况下,51.4%的Android应用程序存在应用实际敏感行为与隐私政策声明不一致的情况。
