基于模糊识别恶意代码检测技术的研究

Based on the Fuzzy Recognition of Malicious Code Detection Technology Research

在恶意代码检测的过程中,假设恶意代码隐藏的比较深,很难对恶意代码特征进行完整、准确的提取.利用传统算法进行恶意代码检测,恶意代码的分布情况都是未知的,没有充分考虑到不同类别代码特征之间的差异性,降低了恶意代码检测的准确性.为此,提出基于模糊识别的恶意代码检测方法.根据支持向量机相关理论,提取恶意代码特征,并将上述特征作为恶意代码识别的依据.建立模糊识别辨别树,计算识别对象属于恶意代码的概率,实现恶意代码的检测.实验结果表明,利用改进算法进行恶意代码检测,能够极大提高检测的准确性.

In the process of malicious code detection ,it is assumed that the malicious code hidden deep ,it is difficult to complete and accurate of malicious code features are extracted .Malicious code detection using the traditional algorithm ,the distribution of malicious code is unknown ,without fully considering the characteristic differences between different categories code ,reduces the accuracy of the malicious code detection .To this end ,the malicious code detection based on fuzzy recognition method is proposed .According to the theory of support vector machine (SVM ) related to extract characteristics of malicious code ,and will be the basis of the above characteristics as malicious code identification .Establish a fuzzy recognition to identify tree ,calculated to identify objects belong to the probability of malicious code ,for detecting malicious code .The experimental results show that the improved algorithm of malicious code detection ,can greatly improve the accuracy of detection .