基于局部线性嵌入的免疫检测器优化生成算法
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摘要
网络安全已上升到国家安全战略层面,入侵检测技术是其重要的组成部分,已得到广泛关注.在基于免疫的入侵检测研究中,针对传统实值否定选择算法不利于高效分析数据而造成的检测器生成速度慢、检测效率低等问题,引入局部线性嵌入算法,借鉴其能对高维数据进行映射降维的特点,提出一种基于局部线性嵌入的免疫检测器优化生成算法,利用局部线性嵌入对高维数据预处理优化降维,并结合实值否定选择算法生成检测器.将该算法用于检测模型,从而提升检测器的生成速率,并可保证生成的检测器高效地处理高维数据.该算法在降维前后可保证样本的局部线性结构不变,具有可变参数少、计算时间短的特点.实验结果表明,所提出算法在显著提高检测器生成速率和对数据检测效率的基础上,检测性能也表现出很好的水平.
Nowadays, network security has risen to the national security strategy level. As a significant part of network security, the intrusion detection technology has aroused general concern. Based on the research of the immune mechanism intrusion detection, aiming at the problems concerning the slow generation of the detector and the low detection efficiency caused by the traditional real-valued negation selection algorithm is not conducive to the efficient analysis of the data,this paper introduces the local linear embedding algorithm which can be applied to reduce the high dimensional data preprocessing optimization dimension due to the characteristic of map the dimensionality of high-dimensional data, and combines with the real-valued negative selection algorithm to generate the detectors. Then, using this algorithm to detection model can enhance the generation velocity of the detectors and ensure the generated detector to process the high-dimensional data efficiently. The algorithm can ensure the local linear structure of the sample is the same after the dimensionality reduction, and it also has the characteristics of less variable parameters and shorter computation time. The experimental results show that this algorithm can significantly improves the detector generation velocity and the detection efficiency of the data, and it is also outstanding in the detection performance.
Nowadays, network security has risen to the national security strategy level. As a significant part of network security, the intrusion detection technology has aroused general concern. Based on the research of the immune mechanism intrusion detection, aiming at the problems concerning the slow generation of the detector and the low detection efficiency caused by the traditional real-valued negation selection algorithm is not conducive to the efficient analysis of the data,this paper introduces the local linear embedding algorithm which can be applied to reduce the high dimensional data preprocessing optimization dimension due to the characteristic of map the dimensionality of high-dimensional data, and combines with the real-valued negative selection algorithm to generate the detectors. Then, using this algorithm to detection model can enhance the generation velocity of the detectors and ensure the generated detector to process the high-dimensional data efficiently. The algorithm can ensure the local linear structure of the sample is the same after the dimensionality reduction, and it also has the characteristics of less variable parameters and shorter computation time. The experimental results show that this algorithm can significantly improves the detector generation velocity and the detection efficiency of the data, and it is also outstanding in the detection performance.
引文
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[2] Wang Y L, Wu Y, Yi S C, et al. Complex multidimensional scaling algorithm for time-of-arrival-based mobile location:A unified framework[J]. Circuits, Systems, and Signal Processing, 2016, 36(4):1-15.
[3] Ji R R, Liu H C, L J. Toward optimal manifold hashing via discrete locally linear embedding[J]. IEEE Signal Processing Society, 2017, 26(11):5411-5420.
[4]柴争义,王献荣,王亮.用于异常检测的实值否定选择算法[J].吉林大学学报:工学版, 2012, 42(1):176-181.(Chai Z Y, Wang X R, Wang L. Real-valued negative selection algorithm for abnormal detection[J]. J of Jilin University:Engineering Edition, 2012, 42(1):176-181.)
[5] Jain V K, Tapaswi S, Shukla A. RSS fingerprints based distributed semi-supervised locally linear embedding(DSSLLE)location estimation system for indoor WLAN[J]. Wireless Personal Communications, 2013,71(2):1175-1192.
[6] Yang Y, Jiang D. Casing vibration fault diagnosis based on variational mode decomposition, local linear embedding,and support vector machine[J]. Shock and Vibration,2017, 1971:1-14.
[7] Zhang L, Leng Y, Yang J, et al. Supervised locally linear embedding algorithm based on orthogonal matching pursuit[J]. IET Image Processing, 2015, 9(8):626-633.
[8] Peng T, Yang X. Optimize parameter K in locally linear embedding based on spatial distributions[C]. The 12th Int Conf on Fuzzy Systems and Knowledge Discovery.Zhangjia jie IEEE, 2015:1588-1595.
[9] Lipinski P. Training complex decision support systems with differential evolution enhanced by locally linear embedding[C]. European Conf on the Applications of Evolutionary Computation. Berlin Springer. Heidelberg,2014:125-137.
[10] Zhang L, Zhao C. Sparsity divergence index based on locally linear embedding for hyperspectral anomaly detection[J]. J of Applied Remote Sensing, 2016, 10(2):1-24.
[11] Deng T, Deng Y, Shi Y, et al. Research on improved locally linear embedding algorithm[C]. Bio-Inspired Computing-Theories and Applications. Berlin Springer Heidelberg, 2014:88-92.
[12] Roweis S T, Saul L K. Nonlinear dimensionality reduction by locally linear embedding[J]. Science, 2000,290(5500):2323-2326.