基于SVM和粗糙集理论的用户概貌攻击检测方法  

Approach of Detecting User Profile Attacks Based on SVM and Rough Set Theory

作　　者：张付志[1] 王波[2] 

机构地区：[1]燕山大学信息科学与工程学院,河北秦皇岛066004 [2]河北省计算机虚拟技术与系统集成重点实验室,河北秦皇岛066004

出　　处：《小型微型计算机系统》2014年第1期108-113,共6页Journal of Chinese Computer Systems

基　　金：河北省自然科学基金项目(F2011203219;F2013203124)资助;河北省高等学校科学技术研究重点项目(ZH2012028)资助

摘　　要：针对现有用户概貌攻击检测算法存在准确率不高的问题,提出一种基于SVM和粗糙集理论的用户概貌攻击检测方法.首先,基于项目类别提出一种特征提取方法;然后,结合所提特征与已有特征分别联合填充规模后通过SVM进行二分类检测,再把各个特征检测结果合并构成信息表;最后,利用粗糙集理论生成的决策规则对信息表进行决策,得到最终的检测结果.实验结果表明,本文提出的方法有效提高了攻击检测的准确率.Aiming at the problem that the precision of the existing user profile attack detection algorithms is not high, based on SVM and rough set theory we propose an approach to detect user profile attack. Firstly, we propose an attribute extraction method based on the item category. Then, we use SVM to perform the detection based on combing the fill size with the existing attributes and the proposed attributes respectively. The detection results are used to generate the information table. Finally, we use the decision rules generated by rough set theory to determine the information table to generate the final test results. The experimental results show that the proposed method can effectively improve the precision of the attack detection.

关 键 词：协同过滤 用户概貌攻击 攻击检测 SVM 粗糙集理论 

分 类 号：TP391[自动化与计算机技术—计算机应用技术]