Detection of Network Protection Security Vulnerability Intrusion Based on Data Mining

With the rapid development of Internet technology, network security has received more and more attention. Therefore, the detection of network protection security vulnerability intrusion has become an urgent task with some practical and guiding significance. In this paper, intrusion detection system (IDS) is taken as the research object to establish a data mining-based IDS model, the experimental results are obtained, and the relevant experimental conclusions are drawn. At the same time, it is compared with the traditional IDS, and six experiments are carried out. The output results of the detection rate, false negative rate and false positive rate of the two different methods in six experiments are obtained. The experimental conclusions that the network protection security performance of IDS using the data mining is better, and the detection capability of network vulnerability intrusion is stronger are drawn. This study provides a new route for the research on the detection of network protection security vulnerability intrusion.

[1]  Noemı́ López-González,et al.  Divide and conquer! Data-mining tools and sequential multivariate analysis to search for diagnostic morphological characters within a plant polyploid complex (Veronica subsect. Pentasepalae, Plantaginaceae) , 2018, PloS one.

[2]  Walaa Hamouda,et al.  A Critical Review of Practices and Challenges in Intrusion Detection Systems for IoT: Toward Universal and Resilient Systems , 2018, IEEE Communications Surveys & Tutorials.

[3]  G. Wang,et al.  Hyperspectral data mining to identify relevant canopy spectral features for estimating durum wheat growth, nitrogen status, and grain yield , 2017, Comput. Electron. Agric..

[4]  Hachmi Fatma,et al.  A two-stage technique to improve intrusion detection systems based on data mining algorithms , 2013, 2013 5th International Conference on Modeling, Simulation and Applied Optimization (ICMSAO).

[5]  Yvon Savaria,et al.  Memory-efficient string matching for Intrusion Detection Systems using a high-precision pattern grouping algorithm , 2016, 2016 ACM/IEEE Symposium on Architectures for Networking and Communications Systems (ANCS).

[6]  Amin Nikanjam,et al.  Deployment of Wireless Intrusion Detection Systems to Provide the Most Possible Coverage in Wireless Sensor Networks Without Infrastructures , 2017, Wirel. Pers. Commun..

[7]  Luc De Raedt,et al.  Data Mining and Machine Learning Techniques for the Identification of Mutagenicity Inducing Substructures and Structure Activity Relationships of Noncongeneric Compounds , 2004, J. Chem. Inf. Model..

[8]  Jean-Philippe Condomines,et al.  Design of a robust controller/observer for TCP/AQM network: First application to intrusion detection systems for drone fleet , 2017, 2017 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS).

[9]  Nour Moustafa,et al.  UNSW-NB15: a comprehensive data set for network intrusion detection systems (UNSW-NB15 network data set) , 2015, 2015 Military Communications and Information Systems Conference (MilCIS).

[10]  Kasidit Wijitsopon,et al.  An evaluation of data mining classification models for network intrusion detection , 2014, 2014 Fourth International Conference on Digital Information and Communication Technology and its Applications (DICTAP).

[11]  Yu Gong,et al.  Using the pattern-of-life in networks to improve the effectiveness of intrusion detection systems , 2017, 2017 IEEE International Conference on Communications (ICC).

[12]  Yehia Taher,et al.  PACT-ART: Enrichment, Data Mining, and Complex Event Processing in the Internet of Cultural Things , 2016, 2016 12th International Conference on Signal-Image Technology & Internet-Based Systems (SITIS).

[13]  T. Blaschke,et al.  A new GIS-based data mining technique using an adaptive neuro-fuzzy inference system (ANFIS) and k-fold cross-validation approach for land subsidence susceptibility mapping , 2018, Natural Hazards.

[14]  Seyed Amir Naghibi,et al.  Prioritization of landslide conditioning factors and its spatial modeling in Shangnan County, China using GIS-based data mining algorithms , 2018, Bulletin of Engineering Geology and the Environment.

[15]  Sushanta Karmakar,et al.  Enhancing performance of anomaly based intrusion detection systems through dimensionality reduction using principal component analysis , 2016, 2016 IEEE International Conference on Advanced Networks and Telecommunications Systems (ANTS).

[16]  Sadok Ben Yahia,et al.  Security insurance of cloud computing services through cross roads of human-immune and intrusion-detection systems , 2018, 2018 International Conference on Information Networking (ICOIN).

[17]  Elena Sitnikova,et al.  Privacy preservation intrusion detection technique for SCADA systems , 2017, 2017 Military Communications and Information Systems Conference (MilCIS).

[18]  Xu Zou,et al.  [Analysis of on medication rules for Qi-deficiency and blood-stasis syndrome of chronic heart failure based on data mining technology]. , 2017, Zhongguo Zhong yao za zhi = Zhongguo zhongyao zazhi = China journal of Chinese materia medica.

[19]  Zahir Tari,et al.  Dimensionality Reduction for Intrusion Detection Systems in Multi-data Streams—A Review and Proposal of Unsupervised Feature Selection Scheme , 2017 .