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Research on Key Technology of LTE Intrusion Detection Systems

Author: LiuLi
Tutor: PiDeChang
School: Nanjing University of Aeronautics and Astronautics
Course: Applied Computer Technology
Keywords: Data Mining Intrusion Detection System BOOTSTRAPPING Manifold learning BP artificial neural network
CLC: TP393.08
Type: Master's thesis
Year: 2009
Downloads: 48
Quote: 0
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Abstract


With the traditional mobile network can not meet the growing data capacity, LTE (Long Term Evolution) technology has become more sophisticated as the latest mobile technology standards, and will soon be widely used. But the face of network security issues become increasingly prominent. In traditional encryption and firewall technology has been unable to fully meet the security needs at the same time, the intrusion detection technology as a new means of security, more and more shows its importance. Intrusion detection system to recognize and respond to computer and network resources on the malicious use of behavior, it is an initiative to protect the network from hacker attacks security technology, a new generation of network security following the traditional security measures such as firewalls, data encryption protection technology. A new method of attack will continue to appear in the new network environment, the mobile network research in the field of intrusion detection brought new issues. A simple mobile phone firewall has been unable to effectively prevent this type of intrusion, intrusion detection system so this will be set up in the LTE eNodeB (base station node) platform by the eNodeB status and monitoring of network traffic, effectively detect the intrusion behavior. Data mining technology for intrusion detection, designs and implements a critical part of the intrusion detection system based on data mining. The article first introduces the LTE-specific system architecture and key technologies; then expounded the concept of intrusion detection and related technologies, intrusion detection systems Research; analysis and data mining techniques in intrusion detection system. On this basis, designed and implemented an intrusion detection system based on data mining. The system is mainly divided into two parts: (1) based on a single eNodeB traffic detection. Plugin interface library of Wireshare developed all packets can be captured to meet the 3GPP protocol, monitoring the eNodeB the network behavior, and the specific network packets written to the log, this paper presents a part of speech for the reference value of the text mining algorithms, effective association rule mining text string and string. Based on Bootstrapping algorithm ideology, both reduce the dependence of the pre-processing stage for stemming, capable of handling the Chinese words that appear in the log. The method increases the understanding of the log text from top to bottom, the validity of the association rules, the application log mining, effectively improve the efficiency of mining association rules to the rule base. (2) Based on the multi-eNodeB characterized in value is detected. ENodeB specific Traces system, the current record eNodeB all the eigenvalues ??of the UDP packet, using the protocol identification and double-buffered read and write traffic, this paper presents modified Laplace dimensionality reduction to a based on nonlinear popular learning algorithm , define a class and between-class distance metric alternative measure of the Euclidean distance between sample points in the original algorithm, retain the Labrador Las characteristics mapping the high efficiency, effectively reduces the value of the multi-feature classification consideration, then the dimensionality reduction characteristic value as the input of BP neural network to reduce neural network training time to explore the relationship between the eigenvalues ??and intrusions.

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CLC: > Industrial Technology > Automation technology,computer technology > Computing technology,computer technology > Computer applications > Computer network > General issues > Computer Network Security
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