【摘要】 随着移动互联网的快速发展,针对移动手机端的钓鱼攻击越来越普遍。提出一种基于改进的朴素贝叶斯算法的移动平台钓鱼网站检测方案。首先,针对在数据收集过程中会出现空缺值的问题,通过K-means算法对缺失的属性值进行填充,以获得完整的数据集;其次,针对朴素贝叶斯算法计算概率时会出现过低估计的问题,将概率进行适当放大,以解决结果下溢的问题;第三,针对朴素贝叶斯算法容易忽略属性之间的关联性问题,对不同的属性值进行了加权处理,以提高检测的正确率;最后,根据实际情况中钓鱼网站出现概率较小的情况,通过调整钓鱼网站与可信网站的概率比值,以此来进一步提高检测的正确率。实验部署在Android 5.0操作系统上。实验结果表明,改进后的朴素贝叶斯算法能够在较短的时间内有效地检测出针对手机端的钓鱼攻击。更多还原

【Abstract】 With the rapid development of mobile Internet,phishing attacks are becoming more common on mobile phones.This paper proposes an improved naive Bayes algorithm to detect phishing sites.Firstly,for the purpose of ensuring data integrity in the data collection process,we fill in the missing attribute values through the K-means algorithm to obtain a complete data set.Secondly,for the purpose of eliminating low biased estimation of Bayes algorithm,we appropriately enlarge the probability so as to resolve the underflow problem.Thirdly,for the purpose of avoiding neglecting the relationship between attributes,we weight different attribute values so as to improve the correctness rate of detection.Lastly,for the purpose of resolving the small probability of the occurrence of phishing sites in the actual situation,we adjust the probability ratio of phishing sites and trusted sites so as to further improve the correctness rate of detection.Experiments are deployed on the Android 5.0 mobile phone.The experimental results show that our improved naive Bayes algorithm can effectively detect the phishing attacks on the mobile phone with relatively low time.更多还原