应用扩张矩阵理论的攻击特征提取

Attack Feature Extraction Using Extension Matrix Theory

近年来随着因特网的飞速发展,计算机系统也面临着越来越多的安全威胁。国内外不少研究人员为此提出了许多种基于软计算的方法用于检测网络攻击。给出了一种基于扩张矩阵理论的攻击特征提取方法,通过构造攻击子集和正常子集的扩张矩阵,建立其最优特征子集选择的整数规划模型,并利用简单遗传算法求解,最终生成可用于检测特定类型攻击的最优规则。在KDD Cup99数据集上的实验结果表明,该方法具有较高的正确检出率和可接受的低误报率。

With the rapid development of Internet in recent years, computer systems are facing increased number of security threats. Various soft computing based approaches have been proposed to detect computer network attacks. A method for attack feature extraction based on extension matrix theory was given in this paper. By constructing extension matrix on positive and negative examples, the integer programming model for its optimal feature subset selection was built, which will be solved by simple genetic algorithm. Finally optimal rules for detection of specific attack were generated. Experimental results show the achievement of high correct detection rates and acceptable low false positive rates based on benchmark KDD Cup99 data sets.