Channel-aware detection of gray hole attacks in wireless mesh networks

Authors:
Devu Manikantan Shila;Yu Cheng;Tricha Anjali
Affiliations:
Dept. of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago;Dept. of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago;Dept. of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago
Venue:
GLOBECOM'09 Proceedings of the 28th IEEE conference on Global telecommunications
Year:
2009

Citing 5
Cited 4

Mitigating routing misbehavior in mobile ad hoc networks

MobiCom '00 Proceedings of the 6th annual international conference on Mobile computing and networking
CHEMAS: Identify suspect nodes in selective forwarding attacks

Journal of Parallel and Distributed Computing
Wireless Networking

Wireless Networking
How well can the IEEE 802.11 wireless LAN support quality of service?

IEEE Transactions on Wireless Communications
A survey on wireless mesh networks

IEEE Communications Magazine

Mitigating selective forwarding attacks with a channel-aware approach in WMNs

IEEE Transactions on Wireless Communications
An efficient detection model of selective forwarding attacks in wireless mesh networks

IDCS'12 Proceedings of the 5th international conference on Internet and Distributed Computing Systems
A Pay-and-Stay model for tackling intruders in hybrid wireless mesh networks

Simulation
Modified DSR protocol for detection and removal of selective black hole attack in MANET

Computers and Electrical Engineering

Quantified Score

Hi-index	0.00

Visualization

Abstract

Gray hole attacks (a.k.a selective forwarding attacks) are a special case of denial of service (DoS) attack, where a misbehaving mesh router just forwards a subset of the packets it receives but drops the others. In wireless networks, it is particularly hard to detect the presence of such attackers because a packet loss over the wireless link can be due to bad channel quality, medium access collisions, or intentional dropping. In contrast to existing studies, we propose a more practical algorithm known as channel aware detection (CAD) that adopts two strategies, hop-by-hop loss observation and traffic overhearing, to detect the mesh nodes subject to the attack. We derive the optimal detection thresholds by analyzing the false alarm and missed detection probabilities of CAD. We also compare our approach to existing solutions and demonstrate that CAD detects the attackers effectively even in harsh channel conditions.