计算机科学 ›› 2018, Vol. 45 ›› Issue (11A): 344-348.

• 信息安全 • 上一篇    下一篇

基于直觉模糊集理论的IDS方法研究

邢瑞康, 李成海   

  1. 空军工程大学防空反导学院 西安710051
  • 出版日期:2019-02-26 发布日期:2019-02-26
  • 通讯作者: 邢瑞康(1994-),男,硕士生,主要研究方向为网络信息安全,E-mail:18149236069@163.com
  • 作者简介:李成海(1966-),男,教授,硕士生导师,主要研究方向为网络信息安全等。
  • 基金资助:
    本文受国家自然科学基金(61703426)资助。

Research on Intrusion Detection System Method Based on Intuitionistic Fuzzy Sets

XING Rui-kang, LI Cheng-hai   

  1. Air and Missile Defense College,Air Force Engineering University,Xi’an 710051,China
  • Online:2019-02-26 Published:2019-02-26

摘要: 入侵检测是网络系统安全维护过程中的有效方法之一,主要指通过对网络系统中的各种数据进行收集、分析,进而发现其中存在的可能对系统安全构成威胁的入侵攻击行为,并迅速作出响应的过程。但由于网络空间中的攻击形式多样,具有许多未知和不确定性,因此如何对其中的不确定性进行描述并采取相应的措施成为了构建入侵检测模型的重要一环。直觉模糊理论就是一种针对系统中存在的不确定性问题进行研究的理论。因此,通过对基于直觉模糊集理论的入侵检测方法进行深入研究发现,其对于处理入侵检测系统中大量不确定性问题具有重要的作用和意义。文中对现有文献中3种典型的基于直觉模糊集理论的入侵检测方法进行了相对全面的分析介绍,并进行了适当的对比总结,指出了目前各种方法仍存在的不足和未来的研究方向,这对其进一步的发展具有一定的参考价值。

关键词: 模糊聚类, 模糊推理, 入侵检测, 直觉模糊, 综合评判

Abstract: Intrusion detection refers to the technology that collects and analyzes various kinds of data through several key points in a computer network or a computer system,so as to find and respond to possible intrusion attacks.However,due to the variety of attacks in cyberspace and many uncertainties,how to describe and deal with its objective existenceof uncertainty has become an important part of constructing an intrusion detection system model.Intuitionistic fuzzy set theory is a theory that studies the problem of uncertainty in the system.Therefore,studying intrusion detection methods based on intuitionistic fuzzy set theory plays an important role in dealing with a large number of uncertainties in intrusion detection systems.This paper summarized the typical intrusion detection methods based on intuitionistic fuzzy set theory in existing literatures and made a proper analysis and comparison,pointing out the shortcomings in the current related methods and the future development direction,which provide some reference value for further study.

Key words: Comprehensive evaluation, Fuzzy clustering, Fuzzy reasoning, Intrusion detection, Intuitionistic fuzzy

中图分类号: 

  • TP301.6
[1]ATANASSOV K.Intuitionistic fuzzy sets [J].Fuzzy Sets and Systems,1986,20(1):87-96.
[2]ANDERSON J P.Computer security threat monitoring and surveillance[R].PA 19034,USA,1980,4.
[3]DENNING D E.An intrusion detection model[J].IEEE Transactions on Software Engineering,1987,13(2):222.
[4]SMAHA S E.Haystack:an intrusion detectionsystem[C]∥Ae-rospace Computer Security Applications Conference.Piscataway:IEEE Conference Publications,1988:37.
[5]LUNTTF,JAGANNATHANR,LEER,et al.Knowledge-based intrusion detection[C]∥AI Systems in Government Conference.Piscataway:IEEE Conference Publications,1989:102-103.
[6]HEBERLEIN L T,DIAS G V,LEVITT K N,et al.Anetwork security monitor [C]∥IEEE Computer Society Symposium on Research in Security and Privacy.1990:296-207.
[7]CHEN S S,CHEUNG S,CRAWFORD R H,et al.GrIDS-A Graph Based Intrusion Detection System for Large Networks[C]∥Proceedings of the 19th National Information System Security Conference.1996:56-57.
[8]GUN,BUEHRER.Vague sets are intuitionistic fuzzy sets[J].Fuzzy Sets and Systems,1996,79(3):403-405.
[9]BURILLO P,BUSTINCE H.Intuitionistic fuzzy relations (Part I) [J].Mathware Soft Computing,1995,2:5-38.
[10]BUSTINCE H,BURILLO P.Vague sets are intuitionistic fuzzy sets[J].Fuzzy Sets and Systems,1996,79(3):403-405.
[11]BUSTINCE H,BURILLO P.Correlation of interval-valued intuitionistic fuzzy sets[J].Fuzzy Sets and Systems,1995,74(2):237-244.
[12]EULALIA S,JANUSZ K.A concept of similarity for intuitionistic fuzzy sets and its use in group decision making [C]∥IEEE International Conference on Fuzzy Systems.2004:1129-1134.
[13]EULALIA S,JANUSZ K.Entropy for intuitionistic fuzzy sets [J].Fuzzy Sets and Systems,2001,118(3):467-477.
[14]林琳.直觉模糊集在近似推理与决策中的应用[D].大连:大连理工大学,2006.
[15]雷英杰,王宝树,苗启广.直觉模糊关系及其合成运算[J].系统工程理论与实践,2005,25(2):113-118,133.
[16]XU Z S,CHEN J,WU J J.Clustering algorithm for intuitionistic fuzzy sets [J].Information Sciences,2008(178):3775-3790.
[17]ZHAO F X,MA Z M,YAN L.Fuzzy Clustering Based on Vague Relations[C]∥FSKD 2006.2006:79-88.
[18]雷英杰,王宝树.直觉模糊集时态逻辑算子及扩展运算性质[J].计算机科学,2005,11(32):52-55.
[19]CHEN Y H,MA X L,WU X Y.DDoS Detection Algorithm Based onPreprocessing Network Traffic Predicted Method and Chaos Theory[J].IEEE Communications letters,2013,17(5):1052-1054.
[20]TAN Z Y,JAMDAGNI A,He X J,et al.A System for Denial-of-Service Attack Detection Based on Multivariate Correlation Analysis[J].IEEE Transactions on Parallel and Distributed Systems,2013,25(2):447-456.
[21]FEINSTEIN L,SCHNACKENBERG D,BALUPARI R,et al.Statistical Approaches to DDoS Attack Detection and Response [C]∥Proceedings of the DARPA Information Servivability Conference and Exposition (DISCEX’03).Washington DC:IEEE Computer Society,2003:303-314.
[22]THANASIS V,ALEXANDROS P,CHRISTOS I,et al.Real-time Network Data Analysis Using Time Series Models[J].Simulation Modelling Practice and Theory,2012,29(C):173-180.
[23]张弛,雷英杰,黄孝文.基于直觉模糊推理的入侵检测方法[J].微电子学与计算机,2009,11(26):185-188.
[24]黄孝文,张弛.基于自适应自觉模糊推理的入侵检测方法[J].计算机应用,2010,5(30):1198-1207.
[25]王亚男,叶蓓,雷英杰.基于GA与IFCM聚类算法的入侵检测[J].计算机工程,2013,9(39):170-173.
[26]张国锁,周创明,雷英杰.改进FCM聚类算法及其在入侵检测中的应用[J].计算机应用,2009,29(5):1336-1338.
[27]贺正洪,雷英杰.直觉模糊c-均值聚类算法研究[J].控制与决策,2011,26(6):847-850,856.
[28]贺正洪,雷英杰,王刚.基于直觉模糊聚类的目标识别[J].系统工程与电子技术,2011,33(6):1283-1286.
[29]张弛.基于直觉模糊推理的入侵检测方法研究[D].西安:空军工程大学,2008.
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