Physical layer security (PHY-security) takes the advantages of channel randomness nature of transmission media to achieve communication confidentiality and authentication. Wiretap coding and signal processing technologies are expected to play vital roles in this new security mechanism. PHY-security has attracted a lot of attention due to its unique features and the fact that our daily life relies heavily on wireless communications for sensitive and private information transmissions. Compared to conventional cryptography that works to ensure all involved entities to load proper and authenticated cryptographic information, PHY-security technologies perform security functions without considering about how those security protocols are executed. In other words, it does not require to implement any extra security schemes or algorithms on other layers above the physical layer. This survey introduces the fundamental theories of PHY-security, covering confidentiality and authentication, and provides an overview on the state-of-the-art works on PHY-security technologies that can provide secure communications in wireless systems, along with the discussions on challenges and their proposed solutions. Furthermore, at the end of this paper, the open issues are identified as our future research directions.

Physical Layer Security for Next Generation Wireless Networks: Theories, Technologies, and Challenges

Radio frequency (RF) fingerprint is the inherent hardware characteristics and has been employed to classify and identify wireless devices in many Internet of Things applications. This paper extracts novel RF fingerprint features, designs a hybrid and adaptive classification scheme adjusting to the environment conditions, and carries out extensive experiments to evaluate the performance. In particular, four modulation features, namely differential constellation trace figure, carrier frequency offset, modulation offset and I/Q offset extracted from constellation trace figure, are employed. The feature weights under different channel conditions are calculated at the training stage. These features are combined smartly with the weights selected according to the estimated signal to noise ratio at the classification stage. We construct a testbed using universal software radio peripheral platform as the receiver and 54 ZigBee nodes as the candidate devices to be classified, which are the most ZigBee devices ever tested. Extensive experiments are carried out to evaluate the classification performance under different channel conditions, namely line-of-sight (LOS) and nonline-of-sight scenarios. We then validate the robustness by carrying out the classification process 18 months after the training, which is the longest time gap. We also use a different receiver platform for classification for the first time. The classification error rate is as low as 0.048 in LOS scenario, and 0.1105 even when a different receiver is used for classification 18 months after the training. Our hybrid classification scheme has thus been demonstrated effective in classifying a large amount of ZigBee devices.

Design of a Hybrid RF Fingerprint Extraction and Device Classification Scheme

This paper proposes a novel deep learning-based radio frequency fingerprint (RFF) identification method for internet of things (IoT) terminal authentications. Differential constellation trace figure (DCTF), a two-dimensional (2D) representation of differential relationship of signal time series, is utilized to extract RFF features without requiring any synchronization. A convolutional neural network (CNN) is then designed to identify different devices using DCTF features. Compared to the existing CNN-based RFF identification methods, the proposed DCTF-CNN possesses the merits of high identification accuracy, zero prior information and low complexity. Experimental results have demonstrated that the proposed DCTF-CNN can achieve an identification accuracy as high as 99.1% and 93.8% under SNR levels of 30 dB and 15 dB, respectively, when classifying 54 target ZigBee devices, which significantly outperforms the existing RFF identification methods.

Deep Learning Based RF Fingerprint Identification Using Differential Constellation Trace Figure

Radio frequency (RF) fingerprinting techniques have been used as an extra security layer for wireless devices. Unique fingerprints are used to identify wireless devices in order to avoid spoofing or impersonating attacks. These unique features can be extracted from imperfections of analog components during the manufacturing. This paper presents a general review of recent progress on RF fingerprinting techniques. Several studies are investigated for RF fingerprinting using different parts of a signal. The majority of these studies have been focused on the transient part of the signal. For this purpose, the transient signal must be extracted precisely. A number of common techniques of transient extraction are theoretically analyzed in this review. Then, some other approaches using the modulated part of the signal are also discussed. For all these approaches, the applied methodologies, the classification algorithms and a taxonomy of features are described. A comprehensive overview of the methods in RF fingerprinting is presented to demonstrate the state-of-the-art works.

A Review of Radio Frequency Fingerprinting Techniques

With the increasing popularity of the Internet of Things (IoT), device identification, and authentication has become a critical security issue. Recently, radio frequency (RF) fingerprint-based identification schemes have attracted wide attention as they extract the inherent characteristics of hardware circuits which is very hard to forge. However, existing RF fingerprint-based approaches face the problems of unstable region of interest (ROI), high-cost feature design, and incomplete automation. To address these problems, this paper proposes a multisampling convolutional neural network (MSCNN) to extract RF fingerprint from the selected ROI for classifying ZigBee devices. A signal-to-noise ratio (SNR) adaptive ROI selection algorithm is also developed to alleviate the effect of semi-steady behavior of ZigBee devices owing to sleep mode switching. The proposed MSCNN uses multiple downsampling transformations for multiscale feature extraction and classification automatically. To validate and evaluate the performance of our proposed method, we design a testbed consisting of one low-cost universal software radio peripheral (USRP) as the receiver and 54 CC2530 devices as targets for identification. Extensive experiments are conducted to demonstrate the feasibility and reliability of MSCNN both in the line-of-sight (LOS) scenarios and non-LOS (NLOS) scenarios. The classification accuracy is as high as 97% under the LOS scenarios around SNR = 30 dB. Our scheme is robust over a wide range of SNRs under the LOS scenarios as well as under the NLOS scenarios.

A Robust RF Fingerprinting Approach Using Multisampling Convolutional Neural Network

Unauthorized network access and spoofing attacks at wireless access points (WAPs) have been traditionally addressed using bit-centric security measures and remain a major information technology security concern. This has been recently addressed using RF fingerprinting methods within the physical layer to augment WAP security. This paper extends the RF fingerprinting knowledge base by: 1) identifying and removing less-relevant features through dimensional reduction analysis (DRA) and 2) providing a first look assessment of device identification (ID) verification that enables the detection of rogue devices attempting to gain network access by presenting false bit-level credentials of authorized devices. DRA benefits and rogue device rejection performance are demonstrated using discrete Gabor transform features extracted from experimentally collected orthogonal frequency division multiplexing-based wireless fidelity (WiFi) and worldwide interoperability for microwave access (WiMAX) signals. Relative to empirically selected full-dimensional feature sets, performance using DRA-reduced feature sets containing only 10% of the highest ranked features (90% reduction), includes: 1) maintaining desired device classification accuracy and 2) improving authorized device ID verification for both WiFi and WiMAX signals. Reliable burst-by-burst rogue device rejection of better than 93% is achieved for 72 unique spoofing attacks and improvement to 100% is demonstrated when an accurate sample of the overall device population is employed. DRA-reduced feature set efficiency is reflected in DRA models requiring only one-tenth the number of features and processing time.

Authorized and Rogue Device Discrimination Using Dimensionally Reduced RF-DNA Fingerprints

It is estimated that the number of IoT devices will reach 75 billion in the next five years. Most of those currently, and to be deployed, lack sufficient security to protect themselves and their networks from attack by malicious IoT devices that masquerade as authorized devices to circumvent digital authentication approaches. This work presents a PHY layer IoT authentication approach capable of addressing this critical security need through the use of feature reduced Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprints and Support Vector Machines (SVM). This work successfully demonstrates 100%: (i) authorized ID verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB, and (ii) rejection of all rogue radio ID spoofing attacks at signal-to-noise ratios greater than or equal to 3 dB using RF-DNA fingerprints whose features are selected using the Relief-F algorithm.

Pre-print: Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM.

In an effort to augment existing bit-level network security mechanisms, a significant amount of research has been conducted in the area of physical layer device discrimination. One such physical layer device discrimination technique, known as RF-DNA fingerprinting, has successfully demonstrated serial number device discrimination. This work extends the RF-DNA fingerprinting state-of-the-art by investigating the impact the existence of carrier frequency offset or the lack thereof has on RF-DNA fingerprint based device discrimination performance. A comparative assessment was conducted using various cases in which carrier frequency offset values were: 1) removed, 2) unique, 3) random, and 4) combinations thereof. This assessment included the use of RF-DNA fingerprints extracted from collected IEEE 802.11a WiFi preambles in which carrier frequency offset values were present. This work shows that RF-DNA fingerprints associated with devices whose preambles contained carrier frequency offset values which were unique, when compared to the values associated with the other devices, resulted in that device being easily discriminated from the others.

Assessment of the impact of CFO on RF-DNA fingerprint classification performance

Advancement in communication technologies and the Internet of Things (IoT) is driving adoption in smart cities that aims to increase operational efficiency and improve the quality of services and citizen welfare, among other potential benefits. The privacy, reliability, and integrity of communications must be ensured so that actions can be appropriate, safe, accurate, and implemented promptly after receiving actionable information. In this work, we present a multi-tier methodology consisting of an authentication and trust-building/distribution framework designed to ensure the safety and validity of the information exchanged in the system. Blockchain protocols and Radio Frequency-Distinct Native Attributes (RF-DNA) combine to provide a hardware-software codesigned system for enhanced device identity and overall system trustworthiness. Our threat model accounts for counterfeiting, breakout fraud, and bad mouthing of one entity by others. Entity trust (e.g., IoT devices) depends on quality and level of participation, quality of messages, lifetime of a given entity in the system, and the number of known "bad" (non-consensus) messages sent by that entity. Based on this approach to trust, we are able to adjust trust upward and downward as a function of real-time and past behavior, providing other participants with a trust value upon which to judge information from and interactions with the given entity. This approach thereby reduces the potential for manipulation of an IoT system by a bad or byzantine actor.

A Hardware-Software Codesign Approach to Identity, Trust, and Resilience for IoT/CPS at Scale

Electric power utilities employ “smart” re-closing devices capable of capturing electrical disturbance (ED) waveforms in real-time. These waveforms are digitally sampled, transmitted back to the utility, and stored in text files that comply with the IEEE COMmon format for TRAnsient Data Exchange (COMTRADE) standard. An average of 2,100 ED events are recorded, by 850 devices, on a monthly basis within Electric Power Board’s distribution network. Due to operational restraints such as: time, money, and person power, only 2% of these recorded events are currently analyzed by power utility personnel. The remaining 98% of recorded events remain a significant loss in information, which could be vital in fully understanding the operating condition within the distribution network. This work presents a hierarchical automated classification process that assigns ED events into one of three “categories”: valid data , switching events , and faults/power quality (PQ) disturbances . This process correctly assigned 92.9% of the tested 140 COMTRADE files into one of these categories. In addition to this categorization, this work presents an automated approach for the classification of magnetic inrush events. A classification rate of 90.63% is demonstrated using 32 transformer inrush COMTRADE files.

Donald R. Reising

Papers

Authorized and Rogue Device Discrimination Using Dimensionally Reduced RF-DNA Fingerprints

Pre-print: Radio Identity Verification-based IoT Security Using RF-DNA Fingerprints and SVM.

Assessment of the impact of CFO on RF-DNA fingerprint classification performance

A Hardware-Software Codesign Approach to Identity, Trust, and Resilience for IoT/CPS at Scale

Automated Identification of Electrical Disturbance Waveforms Within an Operational Smart Power Grid