Internet of Things (IoT) deployments are projected to reach 30.9 billion by 2025, most of which will employ little or no encryption. Specific Emitter Identification (SEI) is being put forward as a lightweight security approach intended to solve the risks of unencrypted or weakly encrypted IoT deployments. SEI exploits waveform distortions that are intrinsic, distinct, and imparted during normal device operations, which permits the employment of SEI without needing to modify the IoT device. However, SEI performs very poorly when the waveforms used to train it are collected at a different time from those it is being tasked with classifying. A solution to this cross-collection problem must be found before SEI can be deployed as a viable IoT security solution. This paper assesses cross-collection SEI when waveform time offset is and is not present, the intentional waveform structure is removed, and SEI is performed using handcrafted and DL-based approaches. The results show removing the intentional waveform structure improves SEI performance by as much as 8% versus using the raw received time domain samples. The results also show that cross-collection SEI accuracy is highest when using a Graphical Neural Network (GNN).

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) has proven to be an effective means for passively identifying emitters using unique and distinct features that are unintentionally imparted to waveforms during their formation and transmission. Primarily, the assumption is that the to-be-identified emitters are passive devices incapable or unwilling to resist SEI. However, cost-effective software-defined radios and open-source deep learning algorithms are leading investigators to challenge this assumption. They show that previously exploited features can be modified to reduce or defeat SEI. Recently, RF-Veil has been proposed to combat such attacks by providing emitters with an active means to obfuscate their waveform features. The result is an eavesdropping and impersonation attack resilient SEI process. Despite RF-Veil's security and privacy focus, it is fair to assume that nefarious actors will attempt to abuse it to thwart legitimate SEI security processes. Therefore, this work investigates the identification of nefarious emitters that employ RF-Veil to thwart legitimate SEI security processes. The results show that there is an inherent Residual Phase Offset (RPO) present in preambles that are not removed in traditional phase offset correction. Removing RPO improves SEI performance when using the phase representation of IQ samples and significantly reduces RF-Veil's negative impact on SEI.

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.

Internet of Things (IoT) deployments continue at an accelerated rate and are projected to reach 75 billion by 2025. Most IoT devices employ weak or no encryption at all due to constrained onboard resources, prohibitive manufacturing costs, and challenges due to implementing and managing encryption at scale. Specific Emitter Identification (SEI) is an approach intended to address the security risks associated with unencrypted or weakly encrypted IoT deployments. SEI passively exploits inherent, unintentional waveform features that are imparted during normal device operations, thus it does not require modification of the device being identified. Recently, Deep Learning (DL)-based SEI has garnered a lot of interest due to its ability to learn discriminatory features directly from an emitter's digitally sampled waveforms, thus eliminating feature engineering commonly associated with SEI. DL-based SEI uses the entirety of the digitally sampled waveform, which includes the unintentional as well as intentional waveform structure. The intentional waveform structure does not convey emitter specific features, thus it is information not used by the DL-based SEI process. In fact, this work shows that the intentional waveform structure acts as a confuser that inhibits the DL network's ability to learn the features needed to discern one emitter from another and its negative impacts worsen as the number of emitters increases. Our work shows that frequency-domain removal of the intentional waveform structure coupled with a Long Short-Term Memory (LSTM) results in superior SEI performance.

Specific Emitter Identification (SEI) was conceived to detect, characterize, and identify radars using their transmitted signals. SEI’s success is linked to the imperfections of an emitter’s Radio Frequency (RF) front-end, which imparts unique "coloration" to the signal during its formation and transmission without impeding normal transceiver operations. Recent works propose Deep Learning (DL) based SEI due to its demonstrated successes in image and facial recognition, as well as its ability to learn radio-specific features directly from the sampled signals. This removes the needless, handcrafted feature engineering of traditional SEI. However, signal energy, its impacts, and its susceptibility to adversary mimicry has received little attention by DL-based SEI works. This work is the first to investigate the impacts and threats posed to DL-based SEI by the presence, lack, or manipulation of signal energy. Our work shows that Long Short-Term Memory (LSTM)-based SEI provides the highest average percent correct classification performance of 89.9% and the lowest rate, 0.68%, at which an adversary can circumvent the SEI process by manipulating the energy of its signals.

Internet of Things (IoT) deployments continue to grow at an accelerated rate, thus presenting a growing surface over which nefarious actors can conduct attacks. This disturbing revelation is exacerbated by the fact that roughly 70% of all IoT devices employ weak or no encryption. Deep learning (DL)-based Specific Emitter Identification (SEI) has been put forward as a possible approach by which to secure IoT devices and related infrastructures. This work presents a DL-based SEI approach that remains robust under degrading signal-to-noise ratio (SNR) conditions while greatly reducing the complexity that is typically associated with DL-based approaches. The presented approach achieves an average percent classification performance of 97% or higher for SNR values greater than or equal to 6 dB.