TY - GEN
T1 - MAC Layer Misbehavior Detection Using Time Series Analysis
AU - Cheng, Maggie X.
AU - Ling, Yi
AU - Wu, Wei Biao
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/7/27
Y1 - 2018/7/27
N2 - This paper presents a solution to the real-time detection of MAC layer misbehaviors in IEEE 802.11 networks. Among the wide range of misbehaviors, we focus on the sender side selfish behavior that creates a channel- capturing effect by using favorable parameters, and the receiver side selfish behavior that does not respond with CTS and ACK upon receiving RTS and data packets, which clears the channel for itself and causes its sender to waste resources. These misbehaviors are subtle to detect, and yet can undermine the performance of the well-behaved nodes significantly. This paper shows a powerful real-time detection method that can catch these misbehaviors as soon as they have started. The detection method requires collecting delay, throughput, and packet interval data to generate time series and applying a sequential change point detection algorithm on the data streams as soon as new data points come in. All attacks are simulated in ns-3 and the simulation results verified the effectiveness of the detection method.
AB - This paper presents a solution to the real-time detection of MAC layer misbehaviors in IEEE 802.11 networks. Among the wide range of misbehaviors, we focus on the sender side selfish behavior that creates a channel- capturing effect by using favorable parameters, and the receiver side selfish behavior that does not respond with CTS and ACK upon receiving RTS and data packets, which clears the channel for itself and causes its sender to waste resources. These misbehaviors are subtle to detect, and yet can undermine the performance of the well-behaved nodes significantly. This paper shows a powerful real-time detection method that can catch these misbehaviors as soon as they have started. The detection method requires collecting delay, throughput, and packet interval data to generate time series and applying a sequential change point detection algorithm on the data streams as soon as new data points come in. All attacks are simulated in ns-3 and the simulation results verified the effectiveness of the detection method.
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U2 - 10.1109/ICC.2018.8422724
DO - 10.1109/ICC.2018.8422724
M3 - Conference contribution
AN - SCOPUS:85051432811
SN - 9781538631805
T3 - IEEE International Conference on Communications
BT - 2018 IEEE International Conference on Communications, ICC 2018 - Proceedings
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2018 IEEE International Conference on Communications, ICC 2018
Y2 - 20 May 2018 through 24 May 2018
ER -