A IDS Model Based on HGA and Data Mining

A IDS Model Based on HGA and Data Mining

Lina Lin Dezhi Wei Fuji Chen

Jimei University Chengyi College, Xiamen 361021, China

School of Economics and Management, Fuzhou University, Fuzhou 350116, China

Corresponding Author Email: 
linda_839@126.com, weidezhi@163.com, chenfuji@fzu.edu.cn
Page: 
318-330
|
DOI: 
https://doi.org/10.18280/ama_b.600204
Received: 
17 May 2017
|
Accepted: 
12 June 2017
|
Published: 
30 June 2017
| Citation

OPEN ACCESS

Abstract: 

The paper proposes a IDS that is based on HGA and Data mining. In this model, an improved clustering algorithm is introduced to classify the normal/abnormal behaviour library from behaviour records on the network and in the system. Then it takes the HGA and data mining as a basis to dig out the the invasion rules and put them into the rule base. Finally, Hybrid Detection Module is proposed to detect the intrusion system. The experiment shows that with a high adaptability, the model has enabled to detect unknown intrusion, improve the detection rate and reduce the false detection rate, thus to protect the computer systems from exotic intrusion.

Keywords: 

Data mining, Intrusion detection, HGA, Clustering algorithm, Information gain

1. Introduction
2. Related Work
3. Adaptive IDS Model
4. Experimental Results
5. Conclusions
  References

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