TY - GEN
T1 - A Novel Deep Learning-based Framework for Blackhole Attack Detection in Unsecured RPL Networks
AU - Choukri, Wijdan
AU - Lamaazi, Hanane
AU - Benamar, Nabil
N1 - Publisher Copyright:
© 2022 IEEE.
PY - 2022
Y1 - 2022
N2 - The routing protocol for low-power and lossy networks (RPL) was developed specifically for constrained communication. Considering its constrained nature, RPL-based Networks can be accessible by trusted and untrusted global users via the Internet and can be subject to serious attacks. Routing attacks are especially difficult to be identified when they occur. However, Deep Learning techniques can be leveraged in detecting network intrusions. This paper comes up with a new deep learning-based framework for routing attack detection in unsecured RPL networks. It allows analyzing and processing the network traffic, extracting features, and defining target-based intrusion thresholds, which leads to the detection of routing attacks. The proposed model is compared to the baseline Machine learning methods. Extensive simulation results confirm the efficiency of our proposed model with a reliable error rate and a detection accuracy up to 98.70%.
AB - The routing protocol for low-power and lossy networks (RPL) was developed specifically for constrained communication. Considering its constrained nature, RPL-based Networks can be accessible by trusted and untrusted global users via the Internet and can be subject to serious attacks. Routing attacks are especially difficult to be identified when they occur. However, Deep Learning techniques can be leveraged in detecting network intrusions. This paper comes up with a new deep learning-based framework for routing attack detection in unsecured RPL networks. It allows analyzing and processing the network traffic, extracting features, and defining target-based intrusion thresholds, which leads to the detection of routing attacks. The proposed model is compared to the baseline Machine learning methods. Extensive simulation results confirm the efficiency of our proposed model with a reliable error rate and a detection accuracy up to 98.70%.
KW - Black-Hole attack
KW - Deep learning
KW - Deep Neural Network
KW - IoT
KW - RPL
UR - https://www.scopus.com/pages/publications/85146417984
UR - https://www.scopus.com/pages/publications/85146417984#tab=citedBy
U2 - 10.1109/3ICT56508.2022.9990664
DO - 10.1109/3ICT56508.2022.9990664
M3 - Conference contribution
AN - SCOPUS:85146417984
T3 - 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022
SP - 457
EP - 462
BT - 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 International Conference on Innovation and Intelligence for Informatics, Computing, and Technologies, 3ICT 2022
Y2 - 20 November 2022 through 21 November 2022
ER -