Username   Password       Forgot your password?  Forgot your username? 

ISSUES BY YEAR

Volume 15 - 2019

No.1 January 2019
No.1 January 2019
No.2 February 2019
No.2 February 2019
No.3 March 2019
No.3 March 2019
No.4 April 2019
No.4 April 2019
No.5 May 2019
No.5 May 2019
No.6 June 2019
No.6 June 2019

Volume 14 - 2018

No.1 January 2018
No.1 January 2018
No.3 March 2018
No.3 March 2018
No.4 April 2018
No.4 April 2018
No.5 May 2018
No.5 May 2018
No.6 June 2018
No.6 June 2018
No.7 July 2018
No.7 July 2018
No.8 August 2018
No.8 August 2018
No.9 September 2018
No.9 September 2018
No.10 October 2018
No.10 October 2018
No.11 November 2018
No.11 November 2018
No.12 December 2018
No.12 December 2018

Volume 13 - 2017

No.4 July 2017
No.4 July 2017
No.5 September 2017
No.5 September 2017
No.7 November 2017
No.7 November 2017
No.8 December 2017
No.8 December 2017

Volume 12 - 2016

Volume 11 - 2015

Volume 10 - 2014

Volume 9 - 2013

Volume 8 - 2012

Volume 7 - 2011

Volume 6 - 2010

Volume 5 - 2009

Volume 4 - 2008

Volume 3 - 2007

Volume 2 - 2006

 

Intrusion Anomaly Detection based on Sequence

Volume 14, Number 2, February 2018, pp. 300-309
DOI: 10.23940/ijpe.18.02.p11.300309

Gangyue Lei

Hunan College of Information, ChangSha, 410200, China


 


 

Abstract:

For single event sequences, a new anomaly detection method based on SV-LFSP (Short Variable-Length Frequent Sequence Pattern) is presented in this paper. Considering the structure character of procedure calling sequences generated by computer programs, the method defines SV-LFSP and contains three fundamental elements in the program flow, sequence, iteration and selection. To build the SV-LFSP library, the SV-LFSP generation algorithm is used. Essentially, this algorithm follows the idea of TEIRESIAS, with an additional redundancy controlling mechanism. Event flow chart, which has the capability of describing program behavior accurately, is a visual version of the SV-LFSP library. This new method is superior to previously provided frequent episode pattern matching algorithms for compact detection models, with high detection efficiency and low time delays.

 

References: 12

    1. Saihua Cai, “Research on Component Security Anomaly Detection Method Based on Monitoring Log Mining”, Jiangsu University, 2016
    2. Jing Du, Yuanyuan Chen, “Anomaly Detection Based on Hidden Markov Model (HMM)”, Journal of Taiyuan University of Science and Technology, vol.9, pp. 16-19, 2008.
    3. A. Hofmeyr, A. Somayaji, and S. Forrest, “Intrusion Detection System Using Sequences of System Calls”, Journal of Computer Security, vol.6, no.3, pp.151-180, 1998
    4. Yu Ji, “Study on the Key Problems in the Process of Sequential Pattern Discovery”, HeFei University of Technology, 2008
    5. Guoyuan Lin, “Research on Anomaly Detection Based on Host Behavior”, Nanjing University, 2011
    6. Hongli Li, “Research on Behavior Matching and Evaluation of Time Series”, The PLA Information Engineering University, 2014
    7. Shangzhe Shi, “Anomaly Detection Based on Hidden Markov Model”, Yangzhou University, 2012
    8. Ying Sun, “Research and Implementation of the Key Problems in the Process of Sequential Pattern Discovery”, HeFei University of Technology, 2005
    9. Kai Xiong, “Research on Frequent Sequence and Closed Sequence Mining Method Based on Minimum Position”, Northeastern University, 2012
    10. Jifeng Yu, “Anomaly Detection Research of Web Application Based on Data Mining”, Huazhong University of Science and Technology, 2011
    11. Yang Yang, “Research on Intrusion Detection Technology Based on Linux Process Behavior”, University of Electronic Science and technology of China, 2014
    12. Jing Zhao, “Research and Application of Network Protocol Anomaly Detection Model”, Beijing Jiaotong University, 2010

       

      Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

       

      CURRENT ISSUE

      Prev Next

      Degradation Index Extraction and Degradation Trend Prediction for Rolling Bearing

      Xin Zhang, Jianmin Zhao, Xianglong Ni, Haiping Li, and Fucheng Sun

      Read more

      Explore One Factor of Affecting Software Reliability Demonstration Testing Result

      Zhenyu Ma, Wei Wu, Wei Zhang, Jianping Wang, and Fusheng Liu

      Read more

      On-Condition Maintenance Decision on EMU Bogie

      Yonghua Li, Hongjie Yu, Yuehua Gao, and Xiaojia Liang

      Read more

      Lubrication Characteristics Analysis of a Rotor Bearing for Space Application

      Shouqing Huang, Shouwen Liu, Xiaokai Huang, and Fangyong Li

      Read more

      Analog Circuit Fault Prognostic Approach using Optimized RVM

      Chaolong Zhang, Yigang He, Shanhe Jiang, Lanfang Zhang, and Xiaolu Wang

      Read more

      Speech Enhancement Algorithms with Adaptive Methods

      Chunli Wang, Peiyi Yang, Quanyu Wang, Lili Niu, and Huaiwei Lu

      Read more

      Edge Detection Method based on Lifting B-Spline Dyadic Wavelet

      Zhibin Hu, Caixia Deng, Yunhong Shao, and Cui Wang

      Read more

      Word Sense Disambiguation based on Maximum Entropy Classifier

      Chunxiang Zhang, Xuesong Zhou, Xueyao Gao, and Bo Yu

      Read more
      This site uses encryption for transmitting your passwords. ratmilwebsolutions.com