On the Security of the Automatic Dependent Surveillance-Broadcast Protocol

Automatic dependent surveillance-broadcast (ADS-B) is the communications protocol currently being rolled out as part of next generation air transportation systems. As the heart of modern air traffic control, it will play an essential role in the protection of two billion passengers per year, besides being crucial to many other interest groups in aviation. The inherent lack of security measures in the ADS-B protocol has long been a topic in both the aviation circles and in the academic community. Due to recently published proof-of-concept attacks, the topic is becoming ever more pressing, especially with the deadline for mandatory implementation in most airspaces fast approaching. This survey first summarizes the attacks and problems that have been reported in relation to ADS-B security. Thereafter, it surveys both the theoretical and practical efforts which have been previously conducted concerning these issues, including possible countermeasures. In addition, the survey seeks to go beyond the current state of the art and gives a detailed assessment of security measures which have been developed more generally for related wireless networks such as sensor networks and vehicular ad hoc networks, including a taxonomy of all considered approaches.Comment: Survey, 22 Pages, 21 Figure

IEEE 802.11 i Security and Vulnerabilities

Despite using a variety of comprehensive preventive security measures, the Robust Secure Networks (RSNs) remain vulnerable to a number of attacks. Failure of preventive measures to address all RSN vulnerabilities dictates the need for enhancing the performance of Wireless Intrusion Detection Systems (WIDSs) to detect all attacks on RSNs with less false positive and false negative rates

Preprint: Using RF-DNA Fingerprints To Classify OFDM Transmitters Under Rayleigh Fading Conditions

The Internet of Things (IoT) is a collection of Internet connected devices capable of interacting with the physical world and computer systems. It is estimated that the IoT will consist of approximately fifty billion devices by the year 2020. In addition to the sheer numbers, the need for IoT security is exacerbated by the fact that many of the edge devices employ weak to no encryption of the communication link. It has been estimated that almost 70% of IoT devices use no form of encryption. Previous research has suggested the use of Specific Emitter Identification (SEI), a physical layer technique, as a means of augmenting bit-level security mechanism such as encryption. The work presented here integrates a Nelder-Mead based approach for estimating the Rayleigh fading channel coefficients prior to the SEI approach known as RF-DNA fingerprinting. The performance of this estimator is assessed for degrading signal-to-noise ratio and compared with least square and minimum mean squared error channel estimators. Additionally, this work presents classification results using RF-DNA fingerprints that were extracted from received signals that have undergone Rayleigh fading channel correction using Minimum Mean Squared Error (MMSE) equalization. This work also performs radio discrimination using RF-DNA fingerprints generated from the normalized magnitude-squared and phase response of Gabor coefficients as well as two classifiers. Discrimination of four 802.11a Wi-Fi radios achieves an average percent correct classification of 90% or better for signal-to-noise ratios of 18 and 21 dB or greater using a Rayleigh fading channel comprised of two and five paths, respectively.Comment: 13 pages, 14 total figures/images, Currently under review by the IEEE Transactions on Information Forensics and Securit

Improving a wireless localization system via machine learning techniques and security protocols

The recent advancements made in Internet of Things (IoT) devices have brought forth new opportunities for technologies and systems to be integrated into our everyday life. In this work, we investigate how edge nodes can effectively utilize 802.11 wireless beacon frames being broadcast from pre-existing access points in a building to achieve room-level localization. We explain the needed hardware and software for this system and demonstrate a proof of concept with experimental data analysis. Improvements to localization accuracy are shown via machine learning by implementing the random forest algorithm. Using this algorithm, historical data can train the model and make more informed decisions while tracking other nodes in the future. We also include multiple security protocols that can be taken to reduce the threat of both physical and digital attacks on the system. These threats include access point spoofing, side channel analysis, and packet sniffing, all of which are often overlooked in IoT devices that are rushed to market. Our research demonstrates the comprehensive combination of affordability, accuracy, and security possible in an IoT beacon frame-based localization system that has not been fully explored by the localization research community

A Survey on Wireless Security: Technical Challenges, Recent Advances and Future Trends

This paper examines the security vulnerabilities and threats imposed by the inherent open nature of wireless communications and to devise efficient defense mechanisms for improving the wireless network security. We first summarize the security requirements of wireless networks, including their authenticity, confidentiality, integrity and availability issues. Next, a comprehensive overview of security attacks encountered in wireless networks is presented in view of the network protocol architecture, where the potential security threats are discussed at each protocol layer. We also provide a survey of the existing security protocols and algorithms that are adopted in the existing wireless network standards, such as the Bluetooth, Wi-Fi, WiMAX, and the long-term evolution (LTE) systems. Then, we discuss the state-of-the-art in physical-layer security, which is an emerging technique of securing the open communications environment against eavesdropping attacks at the physical layer. We also introduce the family of various jamming attacks and their counter-measures, including the constant jammer, intermittent jammer, reactive jammer, adaptive jammer and intelligent jammer. Additionally, we discuss the integration of physical-layer security into existing authentication and cryptography mechanisms for further securing wireless networks. Finally, some technical challenges which remain unresolved at the time of writing are summarized and the future trends in wireless security are discussed.Comment: 36 pages. Accepted to Appear in Proceedings of the IEEE, 201

Exploitation of RF-DNA for Device Classification and Verification Using GRLVQI Processing

This dissertation introduces a GRLVQI classifier into an RF-DNA fingerprinting process and demonstrates applicability for device classification and ID verification. Unlike MDA/ML processing, GRLVQI provides a measure of feature relevance that enables Dimensional Reduction Analysis (DRA) to enhance the experimental-to-operational transition potential of RF-DNA fingerprinting. Using 2D Gabor Transform RF-DNA fingerprints extracted from experimentally collected OFDM-based 802.16 WiMAX and 802.11 WiFi device emissions, average GRLVQI classification accuracy of %C greater than or equal to 90% is achieved using full and reduced dimensional feature sets at SNR greater than or equal to 10.0 dB and SNR greater than or equal to 12.0 dB, respectively. Performance with DRA approximately 90% reduced feature sets included %C greater than or equal to 90% for 1) WiMAX features at SNR greater than or equal to 12.0 dB and 2) WiFi features at SNR greater than or equal to 13.0 dB. For device ID verification with DRA approximately 90% feature sets, GRLVQI enabled: 1) 100% ID verification of authorized WiMAX devices and 97% detection of spoofing attacks by rogue devices at SNR=18.0 dB, and 2) 100% ID verification of authorized WiFi devices at SNR=15.0 dB

Privacy-Preserving by Design: Indoor Positioning System Using Wi-Fi Passive TDOA

Indoor localization systems have become increasingly important in a wide range of applications, including industry, security, logistics, and emergency services. However, the growing demand for accurate localization has heightened concerns over privacy, as many localization systems rely on active signals that can be misused by an adversary to track users' movements or manipulate their measurements. This paper presents PassiFi, a novel passive Wi-Fi time-based indoor localization system that effectively balances accuracy and privacy. PassiFi uses a passive WiFi Time Difference of Arrival (TDoA) approach that ensures users' privacy and safeguards the integrity of their measurement data while still achieving high accuracy. The system adopts a fingerprinting approach to address multi-path and non-line-of-sight problems and utilizes deep neural networks to learn the complex relationship between TDoA and location. Evaluation in a real-world testbed demonstrates PassiFi's exceptional performance, surpassing traditional multilateration by 128%, achieving sub-meter accuracy on par with state-of-the-art active measurement systems, all while preserving privacy