Process Monitoring System Based on Anomaly Detection Statistics Algorithm

Article Preview

Abstract:

In order to find more efficient and handle the internal network threat with the LAN network threat warning and real-time processing, remote process monitoring and management of computer systems is used in this paper. By getting the system process handle on the local computer, realizes the acquisition of the threat source information of the system. By customization of the application layer protocol AMCP, realizes the efficient information transmission between server and client. In order to enhance the reliability of the system security threat model, the System security threat information is analysised with the anomaly detection algorithm based on statistics. Analysis of the test data shows that: through getting the system process handle on the local computer, the system treating information can be obtained. Through the security threat model based the anomaly detection algorithm based on statistics, network threat is dealed efficiently and real time.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

408-411

Citation:

Online since:

February 2014

Authors:

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2014 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

[1] Anthony Jones, Jim Ohlund. Jingjing studio. Windows network programming technology of [M]. 2000 in March. China Machine Press.

Google Scholar

[2] Jeffrey Richter. Wang Jianhua Zhang Huansheng Hou Likun et al. Windows core programming [M]. 2000 in May. China Machine Press.

Google Scholar

[3] MCGRAW G, MORR ISETT G . Attacking malicious code: a report to the infosec research council[ J ]. I EEE Soft wa re , 2000, 17 (5) : 33241.

DOI: 10.1109/52.877857

Google Scholar

[4] Ren Xiaoping, Cai zixing etc. Anomaly detection method based on kinematics model and nonholonomic constraint of vehicle.J. Cent. South Univ. Technol. (2011)18: 1128-1132.

DOI: 10.1007/s11771-011-0813-4

Google Scholar

[5] JON H, PASCAL M. Can s ource code auditing s oft ware identify com2mon vulnerabilities and be used to evaluate s oft ware security [ C ] / /Proc of the 37 th Annual Hawaii I nternati onal Conference on System Sciences . 2004: 440524414.

DOI: 10.1109/hicss.2004.1265654

Google Scholar

[6] Hui-Xin He, Ning Li, Geng-Feng Zheng etc. Anomal Detection Based on Multi-Detector Fusion Used in Turbine. Journal of Harbin Institute of Technology(New Series), vol. 20, No. 1, (2013).

Google Scholar

[7] Yatagai T , Isohara T , Sasase I. Detection of HTTP2GET flood at tack based on analysis of page access behavior [C] Π Π Proc of 2007 IEEE Pacific Rim Conf on Communications, Computers and Signal Processing. Piscataway , NJ : IEEE , 2007 : 2322235.

DOI: 10.1109/pacrim.2007.4313218

Google Scholar

[8] Wang K, Stolfo S J . Anomalous payload2based networkint rusion detection [ C] Π Π Proc of t he 7t h Int Symp on Recent Advances in Int rusion Detection. Berlin : Springer , 2004 : 2032222.

Google Scholar

[9] Yuji Waizumi, Yohei Sato and Yoshiaki Nemoto, A Network-Based Anomaly Deteciton System Based on Three Different Network Traffic Characteristics. Journal of Communication and Computer 9( 2012) 805-812.

Google Scholar