چکیده (انگلیسی):

Intrusion Detection System (IDS) provides additional security for the most vulnerable Mobile Adhoc
Networks (MANET). Use of Fuzzy Inference System (FIS) in the design of IDS is proven to be efficient in
detecting routing attacks in MANETs. Clustering is a vital means in the detection process of FIS based
hybrid IDS. This study describes the design of such a system to detect black hole attack in MANET that
uses Adhoc On-Demand Distance Vector (AODV) routing protocol. It analyses the effect of two clustering
algorithms and also prescribes the suitable clustering algorithm for the above-mentioned IDS. MANETs
with various traffic scenarios were simulated and the data set required for the IDS is extracted. A hybrid
IDS is designed using Sugeno type-2 FIS to detect black hole attack. From the experimental results, it is
derived that the subtractive clustering algorithm produces 97% efficient detection while FCM offers 91%. It
has been found that the subtractive clustering algorithm is more fit and efficient than the Fuzzy C-Means
clustering (FCM) for the FIS based detection system.