Research And Implementation Of E-government Network Intrusion Detection System

In recent years,China’s e-government has developed vigorously,not only changing the traditional government services and management methods,but also breaking the barriers in space and time,providing convenient and efficient government services for the public.However,in the current network environment,information security accidents occur frequently at home and abroad,and the network security situation is grim.The security problems faced by the e-government field cannot be ignored.As a platform for handling government services,the importance of e-government system is self-evident.In view of the current security situation of our country’s e-government system,this essay analyzes the functional requirements of e-government network security and the security risks faced,and proposes targeted solutions in combination with machine learning technology.This essay first introduces the security requirements and risks of e-government network,summarizes the relevant theoretical knowledge of intrusion detection technology,and then briefly introduces the working principle and process of BP neural network and cluster analysis.According to the current e-government network is facing many types of network intrusion threats,this essay designs an intrusion detection model based on chaotic elite particle swarm optimization algorithm and an intrusion detection model based on adaptive cuckoo algorithm and fuzzy C-means clustering,and finally designs and implements an e-government network intrusion detection system on this basis.The main work of this essay is as follows:(1)This essay designs an intrusion detection model of e-government network based on chaotic elite particle swarm optimization algorithm.The model combines particle swarm optimization algorithm with chaotic search strategy and elite selection strategy with BP neural network,dynamically adjusts the weights and thresholds of BP neural network,reduces the impact of local extremum on the model,and improves the detection accuracy.In the experiment,NSL-KDD data set was selected for testing,and part of the data was selected for testing after processing the data by the methods of feature digitization,normalization and dimensionality reduction.The experiment shows that the detection accuracy of the e-government network intrusion detection model proposed in this essay is higher,the accuracy can reach 99.10%,which is 4.92%and 6.04% higher than the intrusion detection model based on genetic algorithm and particle swarm optimization,and the false alarm rate is also lower.The classification of network attacks is also more accurate,the overall performance is significantly improved,and the security protection performance of the e-government network is effectively enhanced.(2)This essay designs an e-government network intrusion detection model based on adaptive cuckoo algorithm and fuzzy C-means clustering.This essay describes the principle and process of fuzzy C-means algorithm.Aiming at the shortcomings of fuzzy C-means algorithm and the advantages of cuckoo optimization algorithm in evolutionary computation,an intrusion detection model based on improved cuckoo optimization fuzzy C-means algorithm is proposed.This essay designs an improved cuckoo algorithm by dynamically adjusting the step size and adaptive adjusting the discovery probability.The improved cuckoo algorithm is combined with the fuzzy C-means clustering algorithm to optimize the initial clustering center and solve the problem of its initial value sensitivity.Compared with the traditional clustering intrusion detection method,the detection accuracy of the improved cuckoo optimized fuzzy C-means intrusion detection model proposed in this essay can reach 94.33%,and the classification effect is better.(3)According to the actual application scenario,the demand analysis is made according to the engineering principle,and the e-government network intrusion detection system is designed and implemented.The system mainly includes user login,intrusion detection,statistical analysis,data management and other functions.The two intrusion detection models proposed in this essay are designed and implemented in the system.After uploading data,you can select the appropriate intrusion detection model for detection.Finally,through the statistical analysis function,the administrator can fully understand the data information and analyze the current system security.