To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach co...
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To improve the similarity measurement between users, a similarity measurement approach incorporating clusters of intrinsic user groups（ SMCUG） is proposed considering the social information of users. The approach constructs the taxonomy trees for each categorical attribute of users. Based on the taxonomy trees, the distance between numerical and categorical attributes is computed in a unified framework via a proper weight. Then, using the proposed distance method, the nave k-means cluster method is modified to compute the intrinsic user groups. Finally, the user group information is incorporated to improve the performance of traditional similarity measurement. A series of experiments are performed on a real world dataset, M ovie Lens. Results demonstrate that the proposed approach considerably outperforms the traditional approaches in the prediction accuracy in collaborative filtering.
面对ISP主干网,为了检测威胁其管理域内用户安全的僵尸网络、钓鱼网站以及垃圾邮件等恶意活动,实时监测流经主干网边界的DNS交互报文,并从域名的依赖性和使用位置两个方面刻画DNS活动行为模式,而后,基于有监督的多分类器模型,提出面向ISP主干网的上层DNS活动监测算法DAOS(binary classifier for DNS activity observation system).其中,依赖性从用户角度观察域名的外在使用情况,而使用位置则关注区域文件中记录的域名内部资源配置.实验结果表明:该算法在不依赖先验知识的前提下,经过两小时的DNS活动观测,可以达到90.5%的检测准确率,以及2.9%的假阳性和6.6%的假阴性.若持续观察1周,准确率可以上升到93.9%,假阳性和假阴性也可以下降到1.3%和4.8%.
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