Using Spectral Fingerprints to Improve Wireless Network Security

Suski, William; Temple, Michael A.; Mendenhall, Michael J.; Mills, Robert F.

doi:10.1109/glocom.2008.ecp.421

Cited by 129 publications

(108 citation statements)

References 10 publications

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“…Due to the assumption of equal prior probabilities (P (c i ) = 1/C) for all classes, P (c i ) can be neglected when evaluating (8). Since the conditional probability is being calculated for a given fingerprintF, the denominator remains constant across all c i and can be neglected as well.…”

Section: Device Identificationmentioning

confidence: 97%

WiMAX mobile subscriber verification using Gabor-based RF-DNA fingerprints

Reising

Temple

2012

2012 IEEE International Conference on Communications (ICC)

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Considerable effort has been put forth to exploit physical layer attributes to augment network bit-level security mechanisms. RF-DNA fingerprints possess such attributes and can be used to uniquely identify authorized users and mitigate unauthorized network activity. These attributes are unique to a given electronic device and difficult to replicate for cloning, spoofing, etc. Device discrimination (identification) of WiMAX devices has been successfully demonstrated using a one-to-many comparison against a pool of unknown device fingerprints. The work here now addresses device authentication using a one-toone comparison against the specific fingerprint associated with a claimed bit-level identity (MAC, SIM, IMEI, etc). The concept is demonstrated using Gabor-based RF-DNA extracted from neartransient burst responses of 802.16e WiMAX mobile subscriber devices-device identification of better than 96% is achieved with verification EER ≤ 1.6% for SN R ≥ −3 dB.

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Section: Device Identificationmentioning

confidence: 97%

WiMAX mobile subscriber verification using Gabor-based RF-DNA fingerprints

Reising

Temple

2012

2012 IEEE International Conference on Communications (ICC)

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“…Wireless Network Security can be improved Using Spectral Fingerprints [3]. The system use radio frequency fingerprints for classification to provide hardware specific identification for detection and mitigation of spoofing.…”

Section: Wireless Intrusion Detection Mechanismsmentioning

confidence: 99%

Survey on Wireless Intrusion Detection Mechanisms

Jacob¹,

Rasmi²

2016

IJCA

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Wireless communication is technology's one of the promising contributions to mankind. Wireless networks are widely used to transfer sensitive data such as bank details, credit card details, emails etc nowadays. The intrusion detection system for wired network cannot be applied directly for wireless networks. Wireless network does not have specific boundaries since the nodes are moving. Wireless networks are more vulnerable to attacks such as denial of service, spoofing attacks, session hijacking etc. It is easy for the attacker to intrude into the system since no physical connection is needed. This paper deals with the survey of some of the methods used in intrusion detection in wireless network.

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“…The errors introduced by the modulator of the transmitter are utilized in steady state-based (modulation based) RF fingerprinting. Many researchers have explored ways to form unique RF fingerprint from these errors [9,12,23,24,[33][34][35][36][37][38]. Mainly, the offset in Inphase/Quadrature (I/Q) components, frequency error, phase and magnitude errors of the frames, Power Spectral Density coefficients of preambles or variant of these features are used in the modulation-based fingerprinting.…”

Section: Rf Fingerprinting Backgroundmentioning

confidence: 99%

“…The preamble signal is made up of a fixed training sequence, which is used for timing/frequency acquisition, diversity selection and channel estimation [37]. The IEEE 802.11a preamble signal is 16 microseconds long and consists of 10 short and 2 long training sequences [40].…”

Section: Data Collectionmentioning

confidence: 99%