A cooperative intrusion detection model based on granular computing

We firstly analyze the method for four attack types, including Probing, DoS (Denial of Service), R2L (Remote to Local) and U2R (User to Root). Based on resource addresses and destination addresses of the network packages, attacks can be divided into four cases, which are respectively one host-one host, one host-many hosts, many hosts-one host and many hosts-many hosts. Specifically, the granular computing method is applied in intrusion detection. A cooperative intrusion detection model is proposed based on granular computing. The construction for an intrusion detection agent is presented.

[1]  Hu Jun.  An overview of granular computing , 2007 .

[2]  Naiqi Wu,et al.  Cooperative Intrusion Detection Model Based on State Transition Analysis , 2007, CSCWD.

[3]  MengChu Zhou,et al.  M-M Role-Transfer Problems and Their Solutions , 2009, IEEE Trans. Syst. Man Cybern. Part A.

[4]  Yiyu Yao,et al.  Granular computing: Past, present and future , 2008, 2008 IEEE International Conference on Granular Computing.

[5]  Yiyu Yao,et al.  Granular computing for data mining , 2006, SPIE Defense + Commercial Sensing.

[6]  Hongle Du,et al.  A Cooperative Network Intrusion detection Based on Fuzzy SVMs , 2010, J. Networks.

[7]  Fan Shi-dong.  The Calculation of Knowledge Granulation and Its Application , 2002 .

[8]  Yanqing Zhang,et al.  Constructive granular systems with universal approximation and fast knowledge discovery , 2005, IEEE Transactions on Fuzzy Systems.

[9]  Yao,et al.  Three Perspectives of Granular Computing , 2006 .

[10]  Miao Duoqian,et al.  Application of Granular Computing to Artificial Neural Network , 2006 .

[11]  Ronald R. Yager,et al.  Some learning paradigms for granular computing , 2006, 2006 IEEE International Conference on Granular Computing.

[12]  Wu Yu.  Granular Computing in Incomplete Information Systems , 2005 .

[13]  Huang,et al.  A Granular Computing Model Based on Tolerance relation , 2005 .

[14]  Christopher Leckie,et al.  A survey of coordinated attacks and collaborative intrusion detection , 2010, Comput. Secur..

[15]  Han Xie.  Anomaly intrusion detection based on quotient space granularity clustering , 2010 .

[16]  Andrzej Skowron,et al.  Information granules: Towards foundations of granular computing , 2001 .

[17]  Yiyu Yao,et al.  A Partition Model of Granular Computing , 2004, Trans. Rough Sets.

[18]  Andrzej Bargiela,et al.  The roots of granular computing , 2006, 2006 IEEE International Conference on Granular Computing.

[19]  Bu Dong.  Principle of Granularity in Clustering and Classification , 2002 .

[20]  Abraham Kandel,et al.  Granular neural networks for numerical-linguistic data fusion and knowledge discovery , 2000, IEEE Trans. Neural Networks Learn. Syst..