Synergistic learning invasion detection method used for data gridding

A technology of intrusion detection and data grid, applied in the direction of data exchange network, digital transmission system, electrical components, etc., can solve the problem of stand-alone and achieve the effect of improving security and resisting attacks

Active Publication Date: 2009-05-13
NANJING UNIV OF POSTS & TELECOMM
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Problems solved by technology

[0005] In the field of intrusion detection, integrated learning is also a research hotspot in recent years. It integrates many independently trained weak lea

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  • Synergistic learning invasion detection method used for data gridding
  • Synergistic learning invasion detection method used for data gridding
  • Synergistic learning invasion detection method used for data gridding

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Experimental program
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Embodiment Construction

[0075] 1. Ordinary node intrusion detection process

[0076] The local intrusion analysis engine on a normal node is composed of two BP neural networks. The two networks are trained independently. One of them is designated as the main detector and the other as the auxiliary detector. The two detectors cooperate to detect the network data packets collected locally, and the main detector not only performs local detection, but also participates in cooperative intrusion detection with the main detector on other nodes. For local network data, only when the primary and secondary detectors are judged to be normal, it is determined to be normal data. If the judgment results of the two detectors are different, it will be sent as a suspected data sample by the cooperative communication server to the node directly connected. A central node of , the collaborative intrusion analysis engine on the central node provides collaborative detection services. As long as any common node finds an i...

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Abstract

A method of collaborative learning intrusion detection applied in data grid, which draws advantages of current popular technologies like distributed detection in intrusion detection domain and ensemble learning etc., combines method of abnormal detection and feature detection, adopts BP neural network collaborative learning for complementary advantages, and makes intrusion detection system based on the method applies to data grid environment. Deploy a plurality of strong detectors integrated by BP neural network in center-nod according to security requirement of different kinds of nods in data grid by the invention, and collect new intrusion sample feature come from everywhere whenever possible to make sure about the security of important copy in center-nod. Deploy collaborative intrusion analysis engine in center-nod meanwhile to provide collaborative detection service for all normal nods. A plurality of normal nods are organized by center-nod for collaborative calculation and suspected data can be detected which cannot be judged by original signal nod so as to improve security of normal nod.

Description

technical field [0001] The invention is an intrusion detection method based on cooperative learning of BP neural network applied to data grid. It is mainly used to detect attacks on data nodes in the grid from the network, and belongs to the cross field of data grid technology and intrusion detection technology. Background technique [0002] With the rapid development of high-speed network technology and computing grid technology in recent years, people's demand for large-scale data sharing has become increasingly strong. Some current storage technologies, such as network-attached storage NAS, storage area network SAN, cluster storage, object Storage, etc., due to its closedness, independence, relatively high cost, and insufficient storage and expansion capabilities, it is difficult to share an increasingly large amount of data under the WAN. On the other hand, there is still a large amount of idle storage space on the WAN not being used effectively. Data grid is an ideal ...

Claims

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Application Information

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IPC IPC(8): H04L9/36H04L12/56H04L29/06
Inventor 王汝传周何骏任勋益付雄邓松季一木易侃杨明慧
Owner NANJING UNIV OF POSTS & TELECOMM
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