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

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.