Network Intrusion Prediction Model based on RBF Features Classification

According to the relationship between feature subset and parameters of RBF neural network, in order to improve the intrusion detection accuracy, it proposed an improved particle swarm optimization neural network of network intrusion detection model. Network feature subset and parameters of RBF neural network were regarded as a particle, through collaboration and information exchange between particles to find the optimal feature subset and parameters of RBF neural network, so as to establish the optimal network intrusion detection model, and using KDD Cup 99 data sets to carry out simulation experiment. The simulation results showed that, IPSO-RBF neural network reduced the feature dimensions, and the better parameters of RBF neural network was obtained then, which is a kind of network intrusion detection model with high detection accuracy and high speed.

[1]  Zhihan Lv,et al.  Multimedia cloud transmission and storage system based on internet of things , 2017, Multimedia Tools and Applications.

[2]  Dingde Jiang,et al.  Joint time-frequency sparse estimation of large-scale network traffic , 2011, Comput. Networks.

[3]  Jinxing Hu,et al.  XEarth: A 3D GIS platform for managing massive city information , 2015, 2015 IEEE International Conference on Computational Intelligence and Virtual Environments for Measurement Systems and Applications (CIVEMSA).

[4]  Zhihan Lv,et al.  Change detection method for remote sensing images based on an improved Markov random field , 2017, Multimedia Tools and Applications.

[5]  Kaveh Pahlavan,et al.  Effects of calibration RFID tags on performance of inertial navigation in indoor environment , 2015, 2015 International Conference on Computing, Networking and Communications (ICNC).

[6]  Zhihan Lv,et al.  Stereoscopic image quality assessment method based on binocular combination saliency model , 2016, Signal Process..

[7]  Song Zhang,et al.  Fast log-Gabor-based nonlocal means image denoising methods , 2014, 2014 IEEE International Conference on Image Processing (ICIP).

[8]  Jinxing Hu,et al.  Traffic Management and Forecasting System Based on 3D GIS , 2015, 2015 15th IEEE/ACM International Symposium on Cluster, Cloud and Grid Computing.

[9]  Zhihan Lv,et al.  Game On, Science - How Video Game Technology May Help Biologists Tackle Visualization Challenges , 2013, PloS one.

[10]  Zhihan Lv,et al.  Touch-less interactive augmented reality game on vision-based wearable device , 2015, Personal and Ubiquitous Computing.

[11]  Kaveh Pahlavan,et al.  A Cyber Physical Test-Bed for Virtualization of RF Access Environment for Body Sensor Network , 2013, IEEE Sensors Journal.

[12]  Ke Wang,et al.  A convergence of key‐value storage systems from clouds to supercomputers , 2016, Concurr. Comput. Pract. Exp..

[13]  Zhihan Lv,et al.  A Self-Assessment Stereo Capture Model Applicable to the Internet of Things , 2015, Sensors.

[14]  Jianxiong Zhou,et al.  A Low-Power and Portable Biomedical Device for Respiratory Monitoring with a Stable Power Source , 2015, Sensors.

[15]  Zhihan Lv,et al.  Towards a face recognition method based on uncorrelated discriminant sparse preserving projection , 2017, Multimedia Tools and Applications.

[16]  Wei Wu,et al.  Rapid Delaunay triangulation for randomly distributed point cloud data using adaptive Hilbert curve , 2016, Comput. Graph..

[17]  Zhihan Lv,et al.  Multimodal Hand and Foot Gesture Interaction for Handheld Devices , 2014, TOMM.

[18]  Fei Liu,et al.  CC-KF: Enhanced TOA Performance in Multipath and NLOS Indoor Extreme Environment , 2014, IEEE Sensors Journal.

[19]  Zhen Yang,et al.  Modeling Framework for Mining Lifecycle Management , 2014, J. Networks.

[20]  Xu Ying,et al.  Collaborative Multi-hop Routing in Cognitive Wireless Networks , 2015, Wireless Personal Communications.

[21]  Yishuang Geng,et al.  Distributed Community Detection Optimization Algorithm for Complex Networks , 2014, J. Networks.

[22]  Wenhua Huang,et al.  Identification Method of Attack Path Based on Immune Intrusion Detection , 2014, J. Networks.