ENHANCING NETWORK SECURITY USING 'LEARNING-FROM-SIGNALS' AND FRACTIONAL FOURIER TRANSFORM BASED RF-DNA FINGERPRINTS

Conference · Sat Jan 01 00:00:00 EST 2011

OSTI ID:1031539

Buckner, Mark A ^[1]; Bobrek, Miljko ^[1]; Farquhar, Ethan ^[1]; Harmer, Paul K ^[2]; Temple, Michael A ^[2]

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1. ORNL
2. Air Force Institute of Technology

Wireless Access Points (WAP) remain one of the top 10 network security threats. This research is part of an effort to develop a physical (PHY) layer aware Radio Frequency (RF) air monitoring system with multi-factor authentication to provide a first-line of defense for network security--stopping attackers before they can gain access to critical infrastructure networks through vulnerable WAPs. This paper presents early results on the identification of OFDM-based 802.11a WiFi devices using RF Distinct Native Attribute (RF-DNA) fingerprints produced by the Fractional Fourier Transform (FRFT). These fingerprints are input to a "Learning from Signals" (LFS) classifier which uses hybrid Differential Evolution/Conjugate Gradient (DECG) optimization to determine the optimal features for a low-rank model to be used for future predictions. Results are presented for devices under the most challenging conditions of intra-manufacturer classification, i.e., same-manufacturer, same-model, differing only in serial number. The results of Fractional Fourier Domain (FRFD) RF-DNA fingerprints demonstrate significant improvement over results based on Time Domain (TD), Spectral Domain (SD) and even Wavelet Domain (WD) fingerprints.

• Research Organization: Oak Ridge National Lab. (ORNL), Oak Ridge, TN (United States)
• Sponsoring Organization: USDOE Laboratory Directed Research and Development (LDRD) Program
• DOE Contract Number: DE-AC05-00OR22725
• OSTI ID: 1031539
• Resource Relation: Conference: Wireless Innovation Forum Conference on Communications Technologies and Software Defined Radio (SDR 11-WInnComm), Washington, DC, USA, 20111129, 20111202
• Country of Publication: United States
• Language: English

Related Subjects

99 GENERAL AND MISCELLANEOUS//MATHEMATICS, COMPUTING, AND INFORMATION SCIENCE
AIR
CLASSIFICATION
COMMUNICATIONS
LEARNING
MONITORING
OPTIMIZATION
SECURITY