Mendeley
TY  _  JOUR
ID  -  1394090892844609
TI  -  Fuzzy Voting for Anomaly Detection in Cluster-Based Mobile Ad Hoc Networks
JO  -  Nashriyyah -i Muhandisi -i Barq va Muhandisi -i Kampyutar -i Iran
JA  -  ES
LA  -  fa
SN  -  16823745
AU  -  Rahmanimanesh M.
AU  -  jalili jalili
AD  -  دانشگاه تربيت مدرس
AD  -  
Y1  -  2013
PY  -  2013
VL  -  4
IS  -  
SP  -  86
EP  -  100
KW  -  Mobile ad hoc network
Anomaly detection
AODV analysis
Attack analysis
Fusion of one-class classifiers
Fuzzy voting
DO  -  
N2  -  In this paper, an attack analysis and detection method in cluster-based mobile ad hoc networks with AODV routing protocol is proposed. The proposed method uses the anomaly detection approach for detecting attacks in which the required features for describing the normal behavior of AODV protocol are defined via step by step analysis of AODV protocol and independent of any attack. In order to learn the normal behavior of AODV, a fuzzy voting method is used for combining support vector data description (SVDD), mixture of Gaussians (MoG), and self-organizing maps (SOM) one-class classifiers and the combined model is utilized to partially detect the attacks in cluster members. The votes of cluster members are periodically transmitted to the cluster head and final decision on attack detection is carried out in the cluster head. In the proposed method, a fuzzy voting method is used for aggregating the votes of cluster members in the cluster head by which the performance of the method improves significantly in detecting blackhole, rushing, route error fabrication, packet replication, and wormhole attacks. In this paper, an attack analysis method based on feature sensitivity ranking is also proposed that determines which features are influenced more by the mentioned attacks. This sensitivity ranking leads to the detection of the types of attacks launched on the network.
UR  - rimag.ir/en/Article/28042
L1  - rimag.ir/en/Article/Download/28042
ER  -  
