Improved Wireless Security through Physical Layer Protocol Manipulation and Radio Frequency Fingerprinting

Wireless networks are particularly vulnerable to spoofing and route poisoning attacks due to the contested transmission medium. Traditional bit-layer defenses including encryption keys and MAC address control lists are vulnerable to extraction and identity spoofing, respectively. This dissertation explores three novel strategies to leverage the wireless physical layer to improve security in low-rate wireless personal area networks. The first, physical layer protocol manipulation, identifies true transceiver design within remote devices through analysis of replies in response to packets transmitted with modified physical layer headers. Results herein demonstrate a methodology that correctly differentiates among six IEEE 802.15.4 transceiver classes with greater than 99% accuracy, regardless of claimed bit-layer identity. The second strategy, radio frequency fingerprinting, accurately identifies the true source of every wireless transmission in a network, even among devices of the same design and manufacturer. Results suggest that even low-cost signal collection receivers can achieve greater than 90% authentication accuracy within a defense system based on radio frequency fingerprinting. The third strategy, based on received signal strength quantification, can be leveraged to rapidly locate suspicious transmission sources and to perform physical security audits of critical networks. Results herein reduce mean absolute percentage error of a widely-utilized distance estimation model 20% by examining signal strength measurements from real-world networks in a military hospital and a civilian hospital

Physical Layer Discrimination of Electronic Control Units Using Wired Signal Distinct Native Attribute (WS-DNDA)

The Controller Area Network (CAN) bus is a communication system used in automobiles to connect the electronic components required for critical vehicle operations. These components are called Electronic Control Units (ECU) and each one exercises one or more functions within the vehicle. ECUs can provide autonomous safety features and increased comfort to drivers but these advancements may come at the expense of vehicle security. Researchers have shown that the CAN bus can be hacked by compromising authorized ECUs or by physically connecting unauthorized devices to the bus. Physical layer (PHY) device fingerprinting has emerged as one of the accepted approaches to establishing vehicle security. This paper uses a fingerprinting method called Wired Signal Distinct Native Attribute (WS-DNA) and classification algorithm called Multiple Discriminant Analysis Maximum Likelihood (MDA/ML) to achieve ECU discrimination which includes device classification and verification

Advances in SCA and RF-DNA Fingerprinting Through Enhanced Linear Regression Attacks and Application of Random Forest Classifiers

Radio Frequency (RF) emissions from electronic devices expose security vulnerabilities that can be used by an attacker to extract otherwise unobtainable information. Two realms of study were investigated here, including the exploitation of 1) unintentional RF emissions in the field of Side Channel Analysis (SCA), and 2) intentional RF emissions from physical devices in the field of RF-Distinct Native Attribute (RF-DNA) fingerprinting. Statistical analysis on the linear model fit to measured SCA data in Linear Regression Attacks (LRA) improved performance, achieving 98% success rate for AES key-byte identification from unintentional emissions. However, the presence of non-Gaussian noise required the use of a non-parametric classifier to further improve key guessing attacks. RndF based profiling attacks were successful in very high dimensional data sets, correctly guessing all 16 bytes of the AES key with a 50,000 variable dataset. With variable reduction, Random Forest still outperformed Template Attack for this data set, requiring fewer traces and achieving higher success rates with lower misclassification rate. Finally, the use of a RndF classifier is examined for intentional RF emissions from ZigBee devices to enhance security using RF-DNA fingerprinting. RndF outperformed parametric MDA/ML and non-parametric GRLVQI classifiers, providing up to GS =18.0 dB improvement (reduction in required SNR). Network penetration, measured using rogue ZigBee devices, show that the RndF method improved rogue rejection in noisier environments - gains of up to GS =18.0 dB are realized over previous methods

Air Force Institute of Technology Research Report 2012

This report summarizes the research activities of the Air Force Institute of Technology’s Graduate School of Engineering and Management. It describes research interests and faculty expertise; lists student theses/dissertations; identifies research sponsors and contributions; and outlines the procedures for contacting the school. Included in the report are: faculty publications, conference presentations, consultations, and funded research projects. Research was conducted in the areas of Aeronautical and Astronautical Engineering, Electrical Engineering and Electro-Optics, Computer Engineering and Computer Science, Systems and Engineering Management, Operational Sciences, Mathematics, Statistics and Engineering Physics