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Fuzzy hidden conditional random field model based network intrusion detection method and system

A hidden conditional random field, network intrusion detection technology, applied in transmission systems, digital transmission systems, data exchange networks, etc., can solve problems such as long-distance correlation of observation sequence uncertainty

Inactive Publication Date: 2014-10-22
CHONGQING UNIV
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  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

This method aims at the characteristics of network attacks and the problems existing in existing network intrusion detection methods. In order to make accurate detection of network intrusion behaviors, it can solve the observation sequence uncertainty and long-distance correlation caused by inaccurate and fuzzy information. problems, and to improve the detection rate and training speed when the training data set is small, to ensure a better effect of network intrusion detection

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  • Fuzzy hidden conditional random field model based network intrusion detection method and system
  • Fuzzy hidden conditional random field model based network intrusion detection method and system
  • Fuzzy hidden conditional random field model based network intrusion detection method and system

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Embodiment 1

[0071] This embodiment provides a network intrusion detection method based on a fuzzy implicit conditional random field model, including the following steps:

[0072] Step 1: Use network data collection tools to collect network instances, and randomly select normal network instances and abnormal network instances as training data sets for the fuzzy latent conditional random field model;

[0073] Step 2: Preprocessing the collected network instances;

[0074] Step 3: Perform feature selection on the preprocessed network instance;

[0075] Step 4: The fuzzy implicit conditional random field model uses the selected features to train and generate a detection model;

[0076] Step 5: Use the detection model generated in step 4 to detect the network instance in actual operation;

[0077] Step 6: Perform corresponding processing on the network intrusion detection, block the network instance when it is detected as an abnormal network instance; allow the network instance to run when i...

Embodiment 2

[0111] Such as figure 1 Shown: the intrusion detection system based on fuzzy implicit conditional random field of the present invention includes a network instance collection module, an instance preprocessing module, a feature selection module, a detection model generation module, an instance detection module and a result processing module.

[0112] The training data set module is used to collect network instances using network data collection tools, and randomly select normal network instances and abnormal network instances as the training data sets of the fuzzy implicit conditional random field model;

[0113] The preprocessing module is used to preprocess the collected network instances;

[0114] The feature selection module is used to perform feature selection on the preprocessed network instance;

[0115] The detection model generating module is used for the fuzzy implicit conditional random field model to generate a detection model using selected feature training;

[011...

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Abstract

The invention discloses a fuzzy hidden conditional random field model based network intrusion detection method which aims at solving the technical problem of improving the existing network intrusion detection effect. According to the fuzzy hidden conditional random field model based network intrusion detection method, a network data collection tool is used to collect network examples, normal network examples and abnormal network examples are selected randomly to be served as a training dataset for a fuzzy hidden conditional random field model, and the examples are mutually independent. The fuzzy hidden conditional random field model is established for network intrusion detection through the training dataset, the actually running network examples are input to the established detection model, a corresponding intrusion detection effect is output, and the network examples are accurately detected in real time. The fuzzy hidden conditional random field model based network intrusion detection method can accurately and rapidly detect unkown type network intrusion behaviors and is good in actual popularization and application prospect.

Description

technical field [0001] The invention relates to a network intrusion detection method, in particular to a network intrusion detection method and system based on a random field model with fuzzy implicit conditions. Background technique [0002] In the early days of Internet construction, the network structure and attack methods were relatively simple, and the network security system was mainly based on protection, relying on firewalls, encryption, identity authentication and other means to achieve. With the rapid development of Internet technology and the gradual wide application, hacker attack methods are becoming more and more complex and diverse. Static security defense technologies such as traditional operating system reinforcement and simple firewall policies are far from meeting the needs of modern high-security networks. Therefore, it is imperative to design an effective intrusion detection method based on the three-dimensional depth and multi-layer defense of network s...

Claims

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Application Information

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IPC IPC(8): H04L29/06H04L12/26
Inventor 罗钧李义军高增辉
Owner CHONGQING UNIV