TY - GEN
T1 - Malicious Node Detection in Wireless Sensor Network using Swarm Intelligence Optimization
AU - Al-Maslamani, Noora
AU - Abdallah, Mohamed
N1 - Publisher Copyright:
© 2020 IEEE.
PY - 2020/2
Y1 - 2020/2
N2 - Over the last few years, modern technology has emerged rapidly in almost every aspect of our lives. In the field of wireless communication and the Internet of Things (IoT), Wireless Sensor Networks (WSN) have gained a growing interest from researchers and organizations from all over the globe due to their importance in wireless information transmission. Despite their promising performance and quality of operation, WSNs are vulnerable to a wide range of security attacks. Among these is a sinkhole attack, which presents a severe threat to the security of WSNs. This paper proposes and develops a detection mechanism against sinkhole attack by adopting Swarm Intelligence (SI) optimization algorithm. The proposed mechanism combines a weight estimation technique and Artificial Bee Colony (ABC) optimization algorithm in order to enhance detection accuracy of sinkhole attack. The proposed work has been implemented in MATLAB and extensive simulations have been carried out to evaluate its performance in terms of detection accuracy, detection time, convergence speed, packet overhead, and energy consumption. The results show that our proposed mechanism is efficient and robust in detecting sinkhole attack with high detection accuracy rate.
AB - Over the last few years, modern technology has emerged rapidly in almost every aspect of our lives. In the field of wireless communication and the Internet of Things (IoT), Wireless Sensor Networks (WSN) have gained a growing interest from researchers and organizations from all over the globe due to their importance in wireless information transmission. Despite their promising performance and quality of operation, WSNs are vulnerable to a wide range of security attacks. Among these is a sinkhole attack, which presents a severe threat to the security of WSNs. This paper proposes and develops a detection mechanism against sinkhole attack by adopting Swarm Intelligence (SI) optimization algorithm. The proposed mechanism combines a weight estimation technique and Artificial Bee Colony (ABC) optimization algorithm in order to enhance detection accuracy of sinkhole attack. The proposed work has been implemented in MATLAB and extensive simulations have been carried out to evaluate its performance in terms of detection accuracy, detection time, convergence speed, packet overhead, and energy consumption. The results show that our proposed mechanism is efficient and robust in detecting sinkhole attack with high detection accuracy rate.
KW - Artificial Bee Colony
KW - Sinkhole
KW - Swarm Intelligence
KW - Wireless Sensor Networks
UR - http://www.scopus.com/inward/record.url?scp=85085523944&partnerID=8YFLogxK
U2 - 10.1109/ICIoT48696.2020.9089527
DO - 10.1109/ICIoT48696.2020.9089527
M3 - Conference contribution
AN - SCOPUS:85085523944
T3 - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
SP - 219
EP - 224
BT - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2020 IEEE International Conference on Informatics, IoT, and Enabling Technologies, ICIoT 2020
Y2 - 2 February 2020 through 5 February 2020
ER -