Dissertation > Excellent graduate degree dissertation topics show

SOM-Based Intrusion Detection Algorithm

Author: ZhangAiMing
Tutor: FuXiaoQing
School: Huazhong University of Science and Technology
Course: Information Security
Keywords: Intrusion Detection Neural Networks SOFM
CLC: TP393.08
Type: Master's thesis
Year: 2007
Downloads: 72
Quote: 1
Read: Download Dissertation

Abstract


Intrusion detection system as an important part of the framework of the current network security system , and its main goal is normal behavior and intrusion distinguish . Neural networks used in the intrusion detection problems faced by the expert system to solve the traditional rule-based or model , but also bring the problem of intrusion detection system false alarm rate and undetected rate higher , at the same time , based on neural network intrusion detection systems have a large amount of slow learning process , network computing problems . These problems with the characteristic data is input to the neural network has a relationship . Based on the study of the basic characteristics of the neural network and basic algorithms , it is found that the neural network input data dimension increases will inevitably bring about the expansion of the structure of the neural network will bring increased amount of network computing . Meanwhile, in the recognition of this model , the role played by these input data are not the same , and some characteristics of the output results impact , some relatively small , and some even no effect . According to the self-organizing feature map neural network learning algorithm , to export a characteristic of the network : the physical location of neighboring neurons activated similar pattern , and it is proved . For one mode , the competitiveness of the competitive layer neurons to activate nerve yuan as the center , in a certain range , and gradually reduce . The category of the input pattern using a specific area of the active neurons to characterize. Take advantage of the characteristics of self- organizing feature map neural network to select a range of combinations of features , the use of these features combine to intrusion detection . Removing unnecessary to select the feature data , the feature data can be effectively reduced the amount of calculation of the network , and proved. The experimental results show that feature selection can improve the current use of artificial neural networks for intrusion detection false alarm rate and undetected high defect . Based on a model of self- organizing feature map neural network intrusion detection system , describes the working process of the system , and the function of each module is described in detail , as well as the links between the various modules .

Related Dissertations

  1. High Speed Frequency Measurment and Non-Linearity Correction of Frequency Modulated Capacitive Displacement Sensor,TH822
  2. Research on Anti-periodic Solutions of Delayed Cellular Neural Networks without Assuming Global Lipschitz Conditions,TP183
  3. Designs and Applications of Fuzzy Synthetic Evaluation Models Based on Parallel Algorithms,TP18
  4. Intrusion detection based on the ultrasonic echo envelope in the military security patrols,E919
  5. Research on Intrusion Detection Technology of Wireless Sensor Networks Based on Behavior Trust,TP212.9
  6. Spectrum Variation of Vegetation in Yanzhou Coal Mine Area and Heavy Metal Stress Characteristic,X173
  7. Association rule mining based Intrusion Detection System Research and Implementation,TP393.08
  8. The Research on Intrusion Detection System Based on Machine Learning,TP393.08
  9. Multi- license plate location method based on CNN's Intelligent Transportation Systems Research,TP391.41
  10. Research on the Models of GPS Height Fitting Based on BP Neural Network and Their Applications,P228.4
  11. Research of QoS Optimization Based on Neural Network Prediction for Ethernet Passive Optical Network,TN929.1
  12. Sensitivity Study and Application of Complex Variabal Weight Function Neural Network,TP183
  13. Researches on the Application of the Neuron-MOS in the Neural Network,TP183
  14. A Reduction Method for Artificial Neural Network Inputs Based on An Improved Genetic Algorithm,TP18
  15. On-line Monitoring of Tool Wear for Mass Production,TG71
  16. Modeling of Photovoltaic Cells and Its Application in Energy Prediction,TM914.4
  17. Sensitivity Analysis and Application of Orthogonal Weight Function Neural Network,TP183
  18. Research on Evaluating the Feature Fatigue’s Effect on Customer Equip,F224
  19. Delay Neutral System Stability Analysis of Neural Networks,O175.13
  20. One kind of structure in the depth of learning prototype classification method,TP18
  21. The Design of Identification System for Power Marketing Services,TP391.41

CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > Computer Network Security
© 2012 www.DissertationTopic.Net  Mobile