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Northeast of large scientific instruments based on a shared network intrusion detection technology research and application

Author: WangYanHua
Tutor: ZhouYanJun
School: Northeast Normal University
Course: Computer Software and Theory
Keywords: Network Security Intrusion Detection Technology Intrusion Detection System Data Mining Outlier mining
CLC: TP393.08
Type: Master's thesis
Year: 2009
Downloads: 29
Quote: 2
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Abstract


With the development of network information technology, the application of the Internet, the site is an indispensable factor. Website development and network security management is to modernize management and participation in international competition in the market an important means. Therefore, website development and network security for the site's overall structure is equally important. Intrusion detection technology is an important area of ??network security, there are already many techniques used in the field. Such as intrusion detection based on expert system technology, based on neural network intrusion detection technology, model-based reasoning intrusion detection technologies. Compared with these technologies will be based on data mining technique based outlier mining anomaly detection techniques for intrusion detection has the following advantages: compared to other anomaly detection technology, based outlier mining anomaly intrusion detection technology does not require the training process, Therefore, to overcome the current anomaly detection in the face of a training sample in normal mode incompleteness brought the high rate of false positives; intrusion relative to the entire network behavior, the data belonging to a few exceptions, it can be considered as dataset to deal with outliers, which better reflect the nature of the invasion. This similarity coefficient based intrusion detection and anomaly detection outlier mining algorithms to large scientific instruments northeast implemented as a shared network test platform, on the basis of intrusion detection techniques for in-depth study. Northeast of large scientific instruments sharing network using B / S three-tier architecture, taking into account the system performance and data security, system maintenance and batch data in function realization process, selectively using C / S two-tier structure. Using component interface. NET Framework developers of the system. In this paper, based on similarity coefficient and outlier mining anomaly detection algorithm main steps are as follows: the input data set, data preprocessing, similarity calculation, finally, calculate the similarity coefficient and, according to the similarity coefficient and the output set of isolated points. The data from this experiment northeast of large scientific instruments sharing network 40 network connection records, algorithm implementation using the C language, experiment-based outlier mining similarity and anomaly detection algorithm, the three types of network connection records nothing The experimental results show that the similarity coefficient based outlier mining and anomaly detection techniques are effective. Finally, the proposed algorithm based on K-means clustering algorithm to compare the proposed algorithm improves the intrusion detection rate, and thus proved the superiority of the proposed algorithm.

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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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