CAJ | 학술논문

针对传统朴素贝叶斯分类模型在入侵取证中存在的特征项冗余问题,以及没有考虑入侵行为所涉及的数据属性间的差别问题,提出一种基于改进的属性加权朴素贝叶斯分类方法.用一种改进的基于特征冗余度的信息增益算法对特征项集进行优化,并在此优化结果的基础上,提取出其中的特征冗余度判别函数作为权值引入贝叶斯分类算法中,对不同的条件属性赋予不同的权值.经实验验证,该算法能有效地选择特征向量,降低分类干扰,提高检测精度.
침대전통박소패협사분류모형재입침취증중존재적특정항용여문제,이급몰유고필입침행위소섭급적수거속성간적차별문제,제출일충기우개진적속성가권박소패협사분류방법.용일충개진적기우특정용여도적신식증익산법대특정항집진행우화,병재차우화결과적기출상,제취출기중적특정용여도판별함수작위권치인입패협사분류산법중,대불동적조건속성부여불동적권치.경실험험증,해산법능유효지선택특정향량,강저분류간우,제고검측정도.
@@@@Traditional Naive Bayes classification exists the issues of feature redundancy in intrusion forensics and neglects the difference between data attributes in different intrusion actions. For these issues, an improved Weighted Naive Bayes classification method by setting attribute weights is proposed. A new Information Gain algorithm based on feature redundancy is used to opti-mize the set of feature, then the discriminant of feature redundancy extracted as weights is introduced to Bayes classification algorithm based on this optimization results. The different condition attributes are weighted differently. The experimental results show that the new algorithm can effectively select features, reduce classification interference and improve detection accuracy.