19,687 research outputs found
Security and Privacy Issues in Wireless Mesh Networks: A Survey
This book chapter identifies various security threats in wireless mesh
network (WMN). Keeping in mind the critical requirement of security and user
privacy in WMNs, this chapter provides a comprehensive overview of various
possible attacks on different layers of the communication protocol stack for
WMNs and their corresponding defense mechanisms. First, it identifies the
security vulnerabilities in the physical, link, network, transport, application
layers. Furthermore, various possible attacks on the key management protocols,
user authentication and access control protocols, and user privacy preservation
protocols are presented. After enumerating various possible attacks, the
chapter provides a detailed discussion on various existing security mechanisms
and protocols to defend against and wherever possible prevent the possible
attacks. Comparative analyses are also presented on the security schemes with
regards to the cryptographic schemes used, key management strategies deployed,
use of any trusted third party, computation and communication overhead involved
etc. The chapter then presents a brief discussion on various trust management
approaches for WMNs since trust and reputation-based schemes are increasingly
becoming popular for enforcing security in wireless networks. A number of open
problems in security and privacy issues for WMNs are subsequently discussed
before the chapter is finally concluded.Comment: 62 pages, 12 figures, 6 tables. This chapter is an extension of the
author's previous submission in arXiv submission: arXiv:1102.1226. There are
some text overlaps with the previous submissio
Deep Neural Network and Data Augmentation Methodology for off-axis iris segmentation in wearable headsets
A data augmentation methodology is presented and applied to generate a large
dataset of off-axis iris regions and train a low-complexity deep neural
network. Although of low complexity the resulting network achieves a high level
of accuracy in iris region segmentation for challenging off-axis eye-patches.
Interestingly, this network is also shown to achieve high levels of performance
for regular, frontal, segmentation of iris regions, comparing favorably with
state-of-the-art techniques of significantly higher complexity. Due to its
lower complexity, this network is well suited for deployment in embedded
applications such as augmented and mixed reality headsets
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