Defending against rogue base station attacks using wavelet based fingerprinting

Chouchane, Alaaedine; Rekhis, Slim; Boudriga, Noureddine

doi:10.1109/aiccsa.2009.5069374

Cited by 10 publications

(1 citation statement)

References 2 publications

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“…For example, Radio Frequency Fingerprinting (RFF), which is designed to enhance the authentication reliability of wireless devices, is able to identify them [13]. More specifically, if we establish a database that records RFF of all legitimate BSs, the rogue BS can be distinguished [14]. However, it is greatly challenging for meter collectors with limited computational resource to detect and extract the RFF of all legitimate BSs.…”

Section: Introductionmentioning

confidence: 99%

Rogue Base Stations Detection for Advanced Metering Infrastructure Based on Signal Strength Clustering

Bin¹,

Cai²,

Xiao³

et al. 2020

IEEE Access

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The smart meters and meter collectors in Advanced Metering Infrastructure (AMI), which are installed in every home, rely on wireless Virtual Private Network (VPN) for communicating with Head End System (HES). Therefore, they are prone to suffer from malicious cyber-attack. Usually, based on General Packet Radio Service (GPRS) communicated method is the most popular for meter collectors and consequently they are vulnerable to rogue Base Stations (BS) and get compromised by malicious adversaries further. Thus a Density-based spatial clustering of applications with noise (DBSCAN) method is employed to filter rogue BSs out and prevent meter collectors from attaching to them, because there is a notable difference between Signal Strength (SS) profile of legitimate BSs and rogue BSs, Numerical simulation indicates that the proposed approach is capable of detecting both stationary and moving rogue BSs online within fixed time window effectively. Moreover, the method can be implemented in existing meter collectors with limited computation resource. In conclusion, the proposed approach can enhance the level of cyber security of meter collectors.

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Section: Introductionmentioning

confidence: 99%

Rogue Base Stations Detection for Advanced Metering Infrastructure Based on Signal Strength Clustering

Bin¹,

Cai²,

Xiao³

et al. 2020

IEEE Access

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Security and Energy Analysis on Revised Mutual Authentication Protocol in WiMAX Networks

Padmanabhan

Mariappan

2017

Networking Communication and Data Knowledge Engineering

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Radio frequency fingerprint identification for Internet of Things: A survey

Xie¹,

Peng²,

Zhang³

et al. 2023

Security and Safety

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Radio frequency fingerprint (RFF) identification is a promising technique for identifying Internet of Things (IoT) devices. This paper presents a comprehensive survey on RFF identification, which covers various aspects ranging from related definitions to details of each stage in the identification process, namely signal preprocessing, RFF feature extraction, further processing and RFF identification. Specifically, three main steps of preprocessing are summarized, including carrier frequency offset estimation, noise elimination and channel cancellation. Besides, three kinds of RFFs are categorized, comprising I/Q signal-based, parameter-based and transformation-based features. Meanwhile, feature fusion and feature dimension reduction are elaborated as two main further processing methods. Furthermore, a novel framework is established from the perspective of closed set and open set problems, and the related state-of-the-art methodologies are investigated, including approaches based on traditional machine learning, deep learning and generative models. Additionally, we highlight the challenges faced by RFF identification and point out future research trends in this field.

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Defending against rogue base station attacks using wavelet based fingerprinting

Cited by 10 publications

References 2 publications

Rogue Base Stations Detection for Advanced Metering Infrastructure Based on Signal Strength Clustering

Rogue Base Stations Detection for Advanced Metering Infrastructure Based on Signal Strength Clustering

Security and Energy Analysis on Revised Mutual Authentication Protocol in WiMAX Networks

Radio frequency fingerprint identification for Internet of Things: A survey

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