TY  - JOUR
T1  - Dirichlet Distribution Based Trust Model for Malicious Node Detection in
Wireless Sensor Network
AU - Rani, V. Uma AU - Sundaram, K. Soma 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 12
SP  - 4191
EP  - 4199
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.4191.4199
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.4191.4199
KW  - Trust
KW  -Dirichlet distribution
KW  -malicious node
KW  -wireless sensor network
KW  -sliding window
KW  -sensor nodes
AB  - In recent days, misbehaving node or malicious node detection in Wireless Sensor Networks (WSN)
becomes essential, due to its distributed nature and its increasing demand in various applications. Malicious
attacks damages communication between sensor nodes causing the loss of packets, reduced forwarding
behaviour of nodes and creating insecure data transmission. Trust model is one of the solutions to provide
security in WSN but most of the trust models are susceptible to bad mouthing and ballot attack. In this study,
we propose a Dirichlet Distribution based Model (DDTM) to detect malicious attacks, like black hole attack,
selective forwarding attack and on/off attack. DDTM uses trinomial Dirichlet distribution for trust evaluation
of sensor nodes. DDTM uses Dirichlet fusion rule to combine the opinions gathered from neighbouring nodes
and standard deviation rule to overcome bad mouthing and the ballot attack of the trust models. Further, in our
proposed DDTM, we include a penalty scheme and a dynamic sliding window scheme to find attacks quickly
and provide malicious behaviour feedback to the routing model for secure data transmission. The results of
proposed DDTM shows an increased ability compared to present trust models to detect node based attacks
and an increase in packet delivery ratio of wireless sensor networks.
ER  -