Visible to the public Accurate Marking Method of Network Attacking Information Based on Big Data Analysis

TitleAccurate Marking Method of Network Attacking Information Based on Big Data Analysis
Publication TypeConference Paper
Year of Publication2019
AuthorsLi, Peng, Min, Xiao-Cui
Conference Name2019 International Conference on Intelligent Transportation, Big Data Smart City (ICITBS)
Keywordsaccurate marking method, adaptive learning model, Attack, attack information chain, attack information tagging, Big Data, Big Data analysis method, Big Data fusion tracking recognition, big data security in the cloud, Chained Attacks, cloud computing, computer network security, Conferences, Data analysis, information marking, learning (artificial intelligence), Metrics, Network, network attack detection ability, network attack information nodes, network attack nodes, network attacking information, network offensive information, network security defense, open network environment, pubcrawl, resilience, Resiliency, Scalability, security of data, smart cities, task scheduling method, telecommunication scheduling, Transportation

In the open network environment, the network offensive information is implanted in big data environment, so it is necessary to carry out accurate location marking of network offensive information, to realize network attack detection, and to implement the process of accurate location marking of network offensive information. Combined with big data analysis method, the location of network attack nodes is realized, but when network attacks cross in series, the performance of attack information tagging is not good. An accurate marking technique for network attack information is proposed based on big data fusion tracking recognition. The adaptive learning model combined with big data is used to mark and sample the network attack information, and the feature analysis model of attack information chain is designed by extracting the association rules. This paper classifies the data types of the network attack nodes, and improves the network attack detection ability by the task scheduling method of the network attack information nodes, and realizes the accurate marking of the network attacking information. Simulation results show that the proposed algorithm can effectively improve the accuracy of marking offensive information in open network environment, the efficiency of attack detection and the ability of intrusion prevention is improved, and it has good application value in the field of network security defense.

Citation Keyli_accurate_2019