Detection Technology for Hostile Attacks to Campus Wireless Network

Lei Ma; Hong Xue Yang

doi:10.4028/www.scientific.net/AMM.644-650.3287

Paper Titles

Human Error Simulation of Manufacturing Cell Based on Human-Machine Integrated Model
p.3270

RETRACTED: A Methodology for the Exploration of 802.11B
p.3275

Research on Data Aggregation Application Based on Mashup
p.3279

Research on ASIC Firewall Based on State Detection Technology
p.3283

Detection Technology for Hostile Attacks to Campus Wireless Network
p.3287

Research on the Detection Method of the Malicious Attacks on Campus Network
p.3291

Detecting Overlapping Communities with MDS and Local Expansion FCM
p.3295

Comparison Analysis of RESTful and SOAP-WSDL Applied in the Image Management System
p.3300

Study on Digital Content Representation from Direct Label Graph to RDF/OWL Language into Semantic Web
p.3304

HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 644-650Detection Technology for Hostile Attacks to Campus...

Detection Technology for Hostile Attacks to Campus Wireless Network

Abstract:

The detection technology for malicious attacks to campus wireless network is analyzed. In this paper, a detection method based on PrefixSpan algorithm for malicious attacks to campus wireless network is adopted. Aiming at hostile attacks environment to the wireless network campus, the detection method is proposed to reduce the amount of computation and memory consumption, and through incremental learning to achieve training of large data, and improve performance of mining further. Experimental results show that the proposed algorithm for hostile attacks detection to campus wireless network, can effectively improve accuracy and security of detection, and achieved the desired results.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 644-650)

Pages:

3287-3290

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.644-650.3287

Citation:

Cite this paper

Online since:

September 2014

Authors:

Lei Ma*, Hong Xue Yang

Keywords:

Attack Detection, Data Mining (DM), Safety

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

* - Corresponding Author

References

[1] Liu Xiangqi, Chong Xiangting, Zhen Chenggang. Intrusion Detection System Based on Data Mining [J]. Information System Engineering , 2013, 04: 44-45，50.

Google Scholar

[2] Zhao Yanjun, Wei Mingjun. The application of Improved Data Mining Algorithm in Intrusion Detection System [J]. Computer Engineering and Applications , 2013, 18: 69-72，115.

Google Scholar

[3] Qi Aihua, Song Shucai. The application of data MiningTechnology inComputer Intrusion Detection [J]. Bulletin of Science and Technology , 2013, 10: 19-20，23.

Google Scholar

[4] Cho S, Pearlman W A. A Full-featured, Error-resilient, Scalable Wavelet Video Codec Based on the Set Partitioning in Hierarchical Trees (SPIHT) Algorithm. IEEE Trans. on CAS for VT, 2002, 12(3): 157-171.

DOI: 10.1109/76.993437

Google Scholar

[5] Xie QingXiu, Regional Conflict Hot Blind Localization Algorithm Based on Wireless Network [J]. Bulletin of Science and Technology, 2013, 29(12): 97-99.

Google Scholar

[6] Yu Guanghua. Switching Frequency Spectrum Optimization Simulation in Congitive Wireless Network[J]. Computer Simulation, 2014, 3(31): 312-315.

Google Scholar