Real-Time Misbehavior Detection in IEEE 802.11e Based WLANs

TL;DR

This paper introduces a real-time hybrid-share misbehavior detector for IEEE 802.11e WLANs that effectively identifies selfish node misbehavior by analyzing successful transmissions, addressing heterogeneity challenges.

Contribution

It presents a novel hybrid-share detector tailored for IEEE 802.11e WLANs, with mathematical analysis and high detection accuracy in heterogeneous environments.

Findings

Effective detection of contention window and AIFS based misbehavior

High detection accuracy with short detection window

Mathematical analysis of false positive and detection rates

Abstract

The Enhanced Distributed Channel Access (EDCA) specification in the IEEE 802.11e standard supports heterogeneous backoff parameters and arbitration inter-frame space (AIFS), which makes a selfish node easy to manipulate these parameters and misbehave. In this case, the network-wide fairness cannot be achieved any longer. Many existing misbehavior detectors, primarily designed for legacy IEEE 802.11 networks, become inapplicable in such a heterogeneous network configuration. In this paper, we propose a novel real-time hybrid-share (HS) misbehavior detector for IEEE 802.11e based wireless local area networks (WLANs). The detector keeps updating its state based on every successful transmission and makes detection decisions by comparing its state with a threshold. We develop mathematical analysis of the detector performance in terms of both false positive rate and average detection rate. Numerical results show that the proposed detector can effectively detect both contention window based and AIFS based misbehavior with only a short detection window.