基于粗糙集与改进LSSVM的入侵检测算法研究

提出了基于粗糙集和改进最小二乘支持向量机的入侵检测算法。算法利用粗糙集理论的可辨识矩阵对样本属性进行约简,减少样本维数;利用稀疏化算法对最小二乘支持向量机进行改进,使其既具备稀疏化特性又具备快速检测的特点,提高了数据样本分类的准确性。结合算法不仅充分发挥粗糙集对数据有效约简和支持向量机准确分类的优点,同时克服了粗糙集在噪声环境中泛化性较差,支持向量机识别有效数据和冗余数据的局限性。通过实验证明,基于粗糙集和改进最小二乘支持向量机的入侵检测算法的检测精度高,误报率和漏报率较低,检测时间短,验证了算法的实效性。

Study on intrusion detection algorithm based on rough set theory and improved LSSVM

This thesis proposes the intrusion detection algorithm based on rough set and the improved least squares support vector machine. The algorithm reduces sample attributes by discernible matrix using rough set theory, reduces the dimen- sion of the data samples. It improves the least squares support vector machine by a sparse algorithm, so it can improve the veracity of data sample classification with the sparse characteristic and rapid detection. On the one hand the combined algorithm has the advantages that rough set can reduce the data effectively and the support vector machine can classify accurately, and on the other hand it avoids the poor generalization while the rough set is in the noise environment and overcomes the limitations when support vector machine identifies effective data and redundant data. Experimental results show that intrusion detection algorithm based on rough set and the improved least squares support vector machine has high detection accuracy, low false positive rate and false negative rate and short detection time which show the validity of the algorithm.