TY - GEN
T1 - Improved C-fuzzy decision tree with controllable membership characteristics for intrusion detection
AU - Makkithaya, Krishnamoorthi
AU - Subba Reddy, N. V.
AU - Dinesh Acharya, U.
PY - 2008
Y1 - 2008
N2 - As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents an improved c-fuzzy decision tree with controllable membership characteristics for intrusion detection. The tree grows gradually by using fuzzy C-means clustering (FCM) algorithm to split the patterns in a selected node with the maximum heterogeneity into C corresponding children nodes. We use a modified fuzzy C-means algorithm with an extended distance measure to include an additional higher order term, as defined in. We also used a hybrid model to select suitable initial points for the FCM. Experimental results have shown that our improved version performs better resulting in an effective intrusion detection system.
AB - As the number of networked computers grows, intrusion detection is an essential component in keeping networks secure. Various approaches for intrusion detection are currently being in use with each one has its own merits and demerits. This paper presents an improved c-fuzzy decision tree with controllable membership characteristics for intrusion detection. The tree grows gradually by using fuzzy C-means clustering (FCM) algorithm to split the patterns in a selected node with the maximum heterogeneity into C corresponding children nodes. We use a modified fuzzy C-means algorithm with an extended distance measure to include an additional higher order term, as defined in. We also used a hybrid model to select suitable initial points for the FCM. Experimental results have shown that our improved version performs better resulting in an effective intrusion detection system.
UR - https://www.scopus.com/pages/publications/84876778304
UR - https://www.scopus.com/inward/citedby.url?scp=84876778304&partnerID=8YFLogxK
M3 - Conference contribution
AN - SCOPUS:84876778304
SN - 9781615677184
T3 - International Conference on Artificial Intelligence and Pattern Recognition 2008, AIPR 2008
SP - 140
EP - 145
BT - International Conference on Artificial Intelligence and Pattern Recognition 2008, AIPR 2008
T2 - 2008 International Conference on Artificial Intelligence and Pattern Recognition 2008, AIPR 2008
Y2 - 7 July 2008 through 10 July 2008
ER -