通信学报 ›› 2016, Vol. 37 ›› Issue (6): 169-184.doi: 10.11959/j.issn.1000-436x.2016127
许艳萍1,马兆丰1,王中华2,钮心忻1,杨义先1
出版日期:
2016-06-25
发布日期:
2017-08-04
基金资助:
Yan-ping XU1,Zhao-feng MA1,Zhong-hua WANG2,Xin-xin NIU1,Yi-xian YANG1
Online:
2016-06-25
Published:
2017-08-04
Supported by:
摘要:
针对Android智能终端安全问题,构建Android智能终端安全分层体系。首先从远程防盗、生物身份验证和硬件安全模块方面阐述了Android设备安全的安全威胁及保护措施,然后从无线安全网络、病毒传播查杀和防钓鱼攻击说明了Android网络安全的隐患及防范,之后从内核安全、本地库运行时环境安全和应用框架安全角度介绍了Android操作系统安全的研究内容,接着从静态检测和应用行为动态检测、应用加固和应用评估方面展示了Android应用安全的研究成果,接下来着眼于数据本身总结了Android数据的追踪、加密和备份等安全保护技术,最后结合实际需求展望了Android安全未来在安全增强框架、智能应用行为分析等方向的发展。
许艳萍,马兆丰,王中华,钮心忻,杨义先. Android智能终端安全综述[J]. 通信学报, 2016, 37(6): 169-184.
Yan-ping XU,Zhao-feng MA,Zhong-hua WANG,Xin-xin NIU,Yi-xian YANG. Survey of security for Android smart terminal[J]. Journal on Communications, 2016, 37(6): 169-184.
表2
机器学习算法的特点和优缺点"
机器学习算法 | 算法应用特点 | 算法优点 | 算法缺点 |
朴素贝叶斯(naive Bayes) | 在统计数据的基础上,依据样本特征,计算各个类别的概率,实现分类 | 算法简单、高效,对缺失数据不太敏感,误差率小;数据较少时仍然有效 | 独立性假设有时不合理,影响准确率 |
支持向量机(SVM) | 训练数据分布在N维平面上,通过训练,找到分类间的边界 | 解决小样本、非线性及高维模式识别的优势明显 | 对大规模训练样本难以实施;噪声数据影响准确率 |
决策树(DT) | 通过训练数据构建决策树,高效地对未知数据分类 | 灵活,高效;在面对数值缺失、变量数较多等问题时稳健 | 难于处理大训练集;对连续性数据比较难预测;易产生过拟合问题 |
K-最近邻(KNN) | 一个样本在特征空间中的K个最相邻的样本中的大多数属于某一个类别 | 精度高,对异常值不敏感,无数据输入假定 | 计算量大;样本不平衡时,准确率低 |
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