Modeling, Evaluation and Detection of Jamming Attacks in Time-Critical Wireless Applications
Abstract
Recently, wireless networking for emerging cyber-physical systems, in particular the smart grid, has been drawing increasing attention in that it has broad applications for time-critical message delivery among electronic devices on physical infrastructures. However, the shared nature of wireless channels unavoidably exposes the messages in transit to jamming attacks which broadcast radio interference to affect the network availability of electronic equipments. An important, yet open research question is how to model and detect jamming attacks in such wireless networks, where communication traffic is more time-critical than that in conventional data-service networks, such as cellular and WiFi networks. In this paper, we aim at modeling and detecting jamming attacks against time-critical wireless networks with applications to the smart grid. In contrast to communication networks where packets-oriented metrics, such as packet loss and throughput are used to measure the network performance, we introduce a new metric, message invalidation ratio, to quantify the performance of time-critical applications. Our modeling approach is inspired by the similarity between the behavior of a jammer who attempts to disrupt the delivery of a time-critical message and the behavior of a gambler who intends to win a gambling game. Therefore, by gambling-based modeling and real-time experiments, we find that there exists a phase transition phenomenon for successful time-critical message delivery under a variety of jamming attacks. That is, as the probability that a packet is jammed increases from 0 to 1, the message invalidation ratio first increases slightly, then increases dramatically to 1. Based on analytical and experimental results, we design the Jamming Attack Detection based on Estimation (JADE) scheme to achieve robust jamming detection, and implement JADE in a wireless network for power substations in the smart grid.
Document Details
- Document Type
- Technical Report
- Publication Date
- Aug 01, 2014
- Accession Number
- ADA613932
Entities
People
- Cliff Wang
- Wenye Wang
- Zhuo Lu
Organizations
- North Carolina State University