Research On Immune Detection Model Based On Locally Linear Embedding

In the 21 st century,the Internet is in a period of rapid growth,and it is also closely related to our daily life.The Internet information is more convenient and more efficient to get.Although the Internet is convenient to us,some seriously security issues still need to be solved.Incalculable losses will be caused if the enterprise system or personal computer information obtained by criminals.The issue about how to protect the information security has caused serious concern.The network transmission data can be monitored by the system which called Intrusion detection.However,with the development of the internet,the amount of data that needs to be tested is increased.It is the reason which makes the Intrusion detection system's detection rate slow.So the experts and scholars at home and abroad are focus on intrusion detection based on immune mechanisms.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.This algorithm which is used in the detection model can enhance the generation velocity of the detectors and ensure the generated detector to process the high-dimensional data efficiently.For the problem about the result of the detection,which can influenced by selecting different values of the near-point parameter k,the operating results can be improved by introducing adaptive method to use more reasonable k.The algorithm can ensure the local linear structure of the sample is the same after the dimensionality reduction.And the simulation experiments were performed on the KDD CUP 1999 dataset.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.LLE algorithm which is based on immune intrusion detection in the initial stage of high-dimensional data preprocessing reduces the system in the process of testing in order to improve the system real time computation.This is significant for the actual application of immune intrusion detection.