论文信息 - Application research of a data stream clustering algorithm in network security defense

Application research of a data stream clustering algorithm in network security defense

The traditional intrusion detection system feature model is based on static data mining. Its mining algorithm relies on too many assumptions, which makes it difficult for intrusion detection systems to adapt to dynamic and real-time system detection requirements. Using attenuated sliding window technology, data stream mining technology and fusion technology with intrusion detection system, a data flow clustering algorithm based on attenuated sliding window is designed to improve and optimize the feature pattern extraction method of intrusion detection system to solve the dynamics of intrusion detection system. Through algorithm design, algorithm application and intrusion detection system simulation verification, the feasibility and accuracy of the algorithm and the optimized intrusion detection system are proved.

Xiaoyang Wang | Lin Zhu | Canshi Zhu

[1] Hoai Bac Le,et al. A Novel Parallel Algorithm for Frequent Itemsets Mining in Large Transactional Databases , 2018, ICDM.

[2] Yu Liu,et al. A KNNS based anomaly detection method applied for UAV flight data stream , 2015, 2015 Prognostics and System Health Management Conference (PHM).

[3] Pramod Bangalore,et al. An artificial neural network‐based condition monitoring method for wind turbines, with application to the monitoring of the gearbox , 2017 .

[4] Jin Wang,et al. Strategies for data stream mining method applied in anomaly detection , 2018, Cluster Computing.

[5] Datong Liu,et al. A hybrid approach for UAV flight data estimation and prediction based on flight mode recognition , 2018, Microelectron. Reliab..

[6] M. Ancona,et al. Cluster computing , 2003, Eleventh Euromicro Conference on Parallel, Distributed and Network-Based Processing, 2003. Proceedings..

[7] P. Srinivasa Pai,et al. Comparison of wavelet based denoising schemes for gear condition monitoring: An Artificial Neural Network based Approach , 2018 .

[8] Asaf Shabtai. Anomaly Detection Using the Knowledge-based Temporal Abstraction Method , 2016, ArXiv.

[9] Mikhail L. Zymbler. Parallel Algorithm for Frequent Itemset Mining on Intel Many-core Systems , 2018, J. Comput. Inf. Technol..

[10] G. P. Saradhi Varma,et al. FIDOOP-DP : Implementation of Data Partitioning in Frequent Itemset on Bigdata using Hadoop Pseudo Distributed Environment , 2017 .