Internet of Things (IoT) deployments are anticipated to reach 29.42 billion by the end of 2030 at an average growth rate of 16% over the next 6 years. These deployments represent an overall growth of 201.4% in operational IoT devices from 2020 to 2030. This growth is alarming because IoT devices have permeated all aspects of our daily lives, and most lack adequate security. IoT-connected systems and infrastructures can be secured using device identification and authentication, two effective identity-based access control mechanisms. Physical Layer Security (PLS) is an alternative or augmentation to cryptographic and other higher-layer security schemes often used for device identification and authentication. PLS does not compromise spectral and energy efficiency or reduce throughput. Specific Emitter Identification (SEI) is a PLS scheme capable of uniquely identifying senders by passively learning emitter-specific features unintentionally imparted on the signals during their formation and transmission by the sender’s radio frequency (RF) front end. This work focuses on image-based SEI because it produces deep learning (DL) models that are less sensitive to external factors and better generalize to different operating conditions. More specifically, this work focuses on reducing the computational cost and memory requirements of image-based SEI with little to no reduction in performance by selecting the most informative portions of each image using entropy. These image portions or tiles reduce memory storage requirements by 92.8% and the DL training time by 81% while achieving an average percent correct classification performance of 91% and higher for SNR values of 15 dB and higher with individual emitter performance no lower than 87.7% at the same SNR. Compared with another state-of-the-art time-frequency (TF)-based SEI approach, our approach results in superior performance for all investigated signal-to-noise ratio conditions, the largest improvement being 21.7% at 9 dB and requires 43% less data.

The number of Internet of Things (IoT) deployments is expected to reach 75.4 billion by 2025. Roughly 70% of all IoT devices employ weak or no encryption; thus, putting them and their connected infrastructure at risk of attack by devices that are wrongly authenticated or not authenticated at all. A physical layer-based security approach–known as Specific Emitter Identification (SEI)–has been proposed and is being pursued as a viable IoT security mechanism. SEI is advantageous because it is a passive technique that exploits inherent and distinct features unintentionally imparted upon the signal during its formation and transmission within and by the IoT device’s Radio Frequency (RF) front end. SEI’s passive exploitation of unintentional signal features removes any need to modify the IoT device, which makes it ideal for existing and future IoT deployments. Despite the amount of SEI research conducted, challenges must be addressed to make SEI a viable IoT security approach. One of these challenges is extracting SEI features from signals collected under multipath fading conditions. Multipath corrupts the inherent SEI exploited features that discriminate one IoT device from another; thus, degrading authentication performance and increasing the chance of attack. This work presents two semi-supervised Deep Learning (DL) equalization approaches and compares their performance with the current state of the art. The two approaches are the Conditional Generative Adversarial Network (CGAN) and the Joint Convolutional Auto-Encoder and Convolutional Neural Network (JCAECNN). Both approaches learn the channel distribution to enable multipath correction while preserving the SEI exploited signal features. CGAN and JCAECNN performance is assessed using a Rayleigh fading channel under degrading SNR, up to thirty-two IoT devices, and two publicly available signal sets. The JCAECNN improves SEI performance by 10% beyond the current state of the art.

Specific Emitter Identification (SEI) is advantageous for its ability to passively identify emitters by exploiting distinct, unique, and organic features unintentionally imparted upon every signal during formation and transmission. These features are attributed to the slight variations and imperfections that exist in the Radio Frequency (RF) front end, thus SEI is being proposed as a physical layer security technique. The majority of SEI work assumes the targeted emitter is a passive source with immutable and difficult-to-mimic signal features. However, Software-Defined Radio (SDR) proliferation and Deep Learning (DL) advancements require a reassessment of these assumptions, because DL can learn SEI features directly from an emitter’s signals and SDR enables signal manipulation. This paper investigates a strong adversary that uses SDR and DL to mimic an authorized emitter’s signal features to circumvent SEI-based identity verification. The investigation considers three SEI mimicry approaches, two different SDR platforms, the presence or lack of signal energy as well as a "decoy" emitter. The results show that "off-the-shelf" DL achieves effective SEI mimicry. Additionally, SDR constraints impact SEI mimicry effectiveness and suggest an adversary’s minimum requirements. Future SEI research must consider adversaries capable of mimicking another emitter’s SEI features or manipulating their own.

It is estimated that the number of Internet-of-Things (IoT) devices will reach 75 billion in the next five years. Most of those currently and soon-to-be deployed devices lack sufficient security to protect themselves and their networks from attacks by malicious IoT devices masquerading as authorized devices in order to circumvent digital authentication approaches. This work presents a physical (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: 1) authorized identity (ID) verification across three trials of six randomly chosen radios at signal-to-noise ratios greater than or equal to 6 dB and 2) 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.

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 IoT will consist of more than seventy five billion devices by the year 2025. 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 mechanisms such as encryption. Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprinting is an SEI technique that has demonstrated success in discriminating radios operating within a noise only channel. This work extends RF-DNA fingerprinting to the discrimination of radios operating under Rayleigh fading conditions through the use of a Nelder-Mead (N-M) simplex-based channel estimator. The N-M estimator estimates the multipath channel directly from the received waveform; thus, eliminating the need for demodulation that is required when using constellation-based estimators. N-M estimator proves superior to three alternative waveform-based estimation approaches under increasing fading paths/reflections and decreasing Signal-to-Noise Ratio (SNR). Radio discrimination performance is maximized through the assessment of: (i) RF-DNA fingerprints generated from the magnitude versus phase representation of the Gabor transform's coefficients, (ii) a statistic-based classifier versus a neural network-based classifier, and (iii) the size of patch used to subdivide the Gabor-based time-frequency response prior to calculation of the RF-DNA fingerprint features. The resulting RF-DNA fingerprinting process achieves an average percent correct classification of 92.3% or greater for Rayleigh fading channels consisting of: two, three, or five reflections/paths at SNR≥15 dB.

Radio-Frequency Distinct Native Attributes (RFDNA) fingerprinting is a Specific Emitter Identification (SEI) approach developed as a mechanism for enhancing wireless network security. RF-DNA fingerprinting exploits unintentional and distinctively unique Physical (PHY) Layer characteristics that are imparted upon the waveform during its generation and transmission. The RF-DNA fingerprinting approach specifically leverages those PHY Layer characteristics that color a fixed, known sequence of waveform symbols (e.g., IEEE 802.11a preamble). This makes the RF-DNA fingerprinting process well suited to matched filter (MF) integration, because (i) both are generated from a fixed sequence and (ii) the MF maximizes SNR while RF-DNA based radio identification performance is degraded as SNR decreases. In this work, the MF is applied prior to signal transformation, which results in four RF-DNA fingerprint generation scenarios: Fast Fourier Transform (FFT) with a MF (FFT-MF), FFT with an All-Pass Filter (FFT- APF), Gabor Transform with a MF (GT-MF), and GT with an APF (GT-APF). Performance of these four scenarios is assessed using average percent correct classification over degrading signal- to-noise channel conditions. When considering classification performance and IoT device constraints (e.g., memory, computation resources and time), RF-DNA fingerprints generated using the FFT-MF scenario proved superior to the other three.

The Internet of Things is a collection of Internet connected devices capable of interacting with the physical world and computer-based systems and is estimated to consist of 20 to 50 billion devices by the year 2020. Due to these numbers and the fact that 70% of the edge devices have no or poor encryption, there is a need for mechanisms by which to secure these devices and associated networks. Specific Emitter Identification (SEI) is one proposed mechanism for IoT security; however, performance within multipath environments has received little attention. This work presents the integration of a novel, Nelder-Mead (N-M)-based channel estimator within the Radio Frequency-Distinct Native Attributes (RF-DNA) fingerprinting process to facilitate serial number discrimination when IoT devices are operating within a multipath environment and degrading Signal-to-Noise Ratio. Percent correct classification performance is used to assess the developed RF-DNA fingerprinting process. Two additional SEI approaches are assessed to facilitate comparative analysis of the serial number discrimination of four IEEE 802.11a Wi-Fi radios using a Rayleigh fading channel. Relative to the Adaptive Compensator (A-C) and N-M SEI approaches, RF-DNA fingerprinting provides the best means for achieving reliable (better than 95%) identification of all radios within a L=2 Rayleigh fading channel at SNR ≥ 21 dB.