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Anomaly Detection based on Fuzzy Rules

Volume 14, Number 2, February 2018, pp. 376-385
DOI: 10.23940/ijpe.18.02.p19.376385

Wenjiang Jiaoa, Qingbin Lib

aSchool of Computer Science and Technology, Shandong University, Jinan, 250100, China
bShandong University, Jinan, 250012, China



Abstract:

Essentially, the fuzzy assert rule library is the fuzzy decision tree. A fuzzy decision tree growth algorithm based on local dynamic optimization is present. Following the idea of the greedy strategy, the algorithm ensures that once a continuous attribute is chosen as a branch node, the membership functions of this attribute after fuzzifying is dynamically optimized. On the other hand, according to fuzzy logic, enhanced Apriori algorithm is present to all the fuzzy frequent item sets composed of fuzzified attributes of multiple events. Then, the fuzzy frequent item sets are transformed into fuzzy association rules that compose the fuzzy association rule library. As for a multiple event sequence, eight different detection algorithms are provided and tested on the same platform. Experiments show that two new algorithms using the fuzzy decision tree and fuzzy association rule library detection models get the highest score.

 

References: 16

    1. Songling Fu, “Distributed Online Social Network Data Storage and Optimization Research”, National Defense Science and Technology University, 2014.
    2. Xiaoshi Fan, Ying Lei and Yanan Wang, Intuitionistic Fuzzy Inference Method in Traffic Anomaly Detection”, Chinese Journal of Electronics and Information Technology, (4): 2218-2224, 2015.
    3. Cunchen Li, “Research and Application of Mass Data Distributed Storage Technology”, Beijing University of Posts and Telecommunications, 2013.
    4. Cikou Liu, Feng Wang and Mingchuan Yang, “Research on Distributed Storage Technology for Large Data”, Telecommunications Technology", vol.5, pp.33-36, 2015.
    5. Nerbu Li, “Distributed Anomaly Detection of Data Mining and Multi Stage Intrusion Alert Correlation Based on Research”, Jilin University, 2010.
    6. Shuai Liu, “Research on Multi-level Anomaly Behavior Analysis and Detection Technology for Network Data Stream”, The PLA Information Engineering University, 2015.
    7. Yang Pan, “Research on the Application of Hadoop Technology in Distributed Data Storage”, Dalian Maritime University, 2015.
    8. Yizhou Qian, “Distributed Real-time Database with High Performance Data Storage Cloud Retrieval Mechanism”, Zhejiang University, 2012.
    9. Shanqi Tao, “Research and Implementation of Intrusion Detection System for Mining Association Rules Based on Snort”, Nanjing University of Aeronautics, 2009.
    10. Yu Wang, “Data Redundancy and Maintenance Technology in Distributed Storage System”, South China University of Technology, 2011.
    11. Dongsheng Xu, Xiaoyan Ai, “Anomaly Intrusion Detection Based on Genetic Optimization and Fuzzy Rule Mining”, Computer applications, vol.6, pp. 2227-2229, 2009.
    12. Zhengmin Xia, “Study on Network Traffic Analysis and Anomaly Detection Based on Fractal”, Shanghai Jiao Tong University, 2012.
    13. Zhuoluo Yang, “Research and Implementation of Distributed Storage Technology in Data Warehouse”, Kunming University of Science and Technology, 2012.
    14. Zhenqian Yang and Yongdan Yang, “Development and Application of Distributed Storage Technology for Large Data. Electronics and Software Engineering”, vol.2, pp.201-210, 2016.
    15. Ling Zhang, “Research on Intrusion Detection Model Based on Rough Set and Artificial Immune”, Beijing University of Posts and Telecommunications, 2014.
    16. Yuping Zhou, “Research on the Key Technology of Intrusion Detection Based on Intelligent Soft Computing”, Donghua University, 2010.

       

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      Attachments:
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