Radio Frequency Fingerprint Based Wireless Transmitter Identification Against Malicious Attacker: An Adversarial Learning Approach

Han, Hao; Cui, Li; Li, Wen; Huang, Luying; Cai, Yuan; Cai, Jihao; Zhang, Yuli

doi:10.1109/wcsp49889.2020.9299859

Cited by 15 publications

(6 citation statements)

References 7 publications

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“…Extensive simulations then demonstrate that the proposed GAN can help to avoid complex jamming attacks and outperform conventional DRLbased approaches with incomplete spectrum information. The lack of training data problem of conventional physical layer security approaches is also discussed and addressed by using GAN in [81], [91], [85], and [92].…”

Section: Channel Equalizationmentioning

confidence: 99%

Effect of heavy metals on duckweed growth

Nguyen¹,

Hoang²,

Pham³

et al. 2022

CTUJS

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In this study, three clones of the duckweed genus Lemna, collected from three different regions of Vietnam Binh Thuan Bac Giang and Ben Tre were used to evaluate the growth and development in a culture medium containing different concentrations of As3+, Cd2+, or Pb2+. During 14 days of incubation, the growth rate was determined daily by measuring the surface area of all plants of each sample. Experimental results show that the three Lemna clones could grow in nutrient medium contaminated by heavy metal at a concentration of 0.3-0.5 mg/L for As3+, 0.15-0.3 mg/L for Cd2+, and 0.15 mg/L for Pb2+. At these concentrations, the division rate of the treated clones was several times higher than that of the controls. The highest increase in the surface area was recorded for Lemna BTN (68.47 times higher when cultured in medium supplemented in 0.3 mg/L Pb2+ than under control conditions). The surface area of Lemna BTR cultured in a medium supplemented with 0.3 mg/L As3+ was 54.65 times higher than in medium without arsenic. The obtained results showed that Lemna BGG and Lemna BTR can tolerate arsenic and cadmium pollution while Lemna BTN tolerates lead pollution.

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Section: Channel Equalizationmentioning

confidence: 99%

Effect of heavy metals on duckweed growth

Nguyen¹,

Hoang²,

Pham³

et al. 2022

CTUJS

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“…SEI processes can be assigned to two general categories, which are designated here as (i) constellation-based and (ii) signal-based. Constellation-based SEI processes extract discriminatory emitter features (e.g., phase and amplitude shifts from the ideal point) from an individual constellation point, collection of constellation points, or distributions associated with the employed digital modulation scheme's two-dimensional, complex plane scatter diagram [98,[108][109][110]. A digital modulation scheme's constellation is a byproduct of the demodulation process, and its corresponding points are generated by sampling each signal symbol at a specific time.…”

Section: The Essence Of Specific Emitter Identificationmentioning

confidence: 99%

“…Traditionally, SEI research has assumed the exploited features are difficult to mimic [53,[81][82][83]110] and the emitter being identified is passive or benign. However, the works reviewed in this section indicate that this is no longer the case and that ongoing SEI research must consider and contend with strong adversaries.…”

Section: Technical Gaps-threats To Seimentioning

confidence: 99%

Considerations, Advances, and Challenges Associated with the Use of Specific Emitter Identification in the Security of Internet of Things Deployments: A Survey

Tyler,

Fadul,

Reising

2023

Information

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Initially introduced almost thirty years ago for the express purpose of providing electronic warfare systems the capabilities to detect, characterize, and identify radar emitters, Specific Emitter Identification (SEI) has recently received a lot of attention within the research community as a physical layer technique for securing Internet of Things (IoT) deployments. This attention is largely due to SEI’s demonstrated success in passively and uniquely identifying wireless emitters using traditional machine learning and the success of Deep Learning (DL) within the natural language processing and computer vision areas. SEI exploits distinct and unintentional features present within an emitter’s transmitted signals. These distinctive and unintentional features are attributed to slight manufacturing and assembly variations within and between the components, sub-systems, and systems comprising an emitter’s Radio Frequency (RF) front end. Although sufficient to facilitate SEI, these features do not hinder normal operations such as detection, channel estimation, timing, and demodulation. However, despite the plethora of SEI publications, it has remained largely a focus of academic endeavors, primarily focusing on proof-of-concept demonstration and little to no use in operational networks for various reasons. The focus of this survey is a review of SEI publications from the perspective of its use as a practical, effective, and usable IoT security mechanism; thus, we use IoT requirements and constraints (e.g., wireless standard, nature of their deployment) as a lens through which each reviewed paper is analyzed. Previous surveys have not taken such an approach and have only used IoT as motivation, a setting, or a context. In this survey, we consider operating conditions, SEI threats, SEI at scale, publicly available data sets, and SEI considerations that are dictated by the fact that it is to be employed by IoT devices or IoT infrastructure.

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“…Most SEI research assumes the identified emitters are passive and their identifying features immutable and difficult to mimic, which suggests they are reluctant and incapable of developing and implementing effective SEI countermeasures [3,4]. However, when considering DL's ability to learn emitter-specific features directly from the raw IQ signal samples and coupling it with readily available "off-the-shelf" DL architectures and the flexibility of Software-Defined Radio (SDR), one must reconsider the degree to which these assumptions hold true [5,6].…”

Section: Introductionmentioning

confidence: 99%

Assessing Adversarial Replay and Deep Learning-Driven Attacks on Specific Emitter Identification-based Security Approaches

Tyler,

Fadul,

Hilling

et al. 2023

Preprint

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Specific Emitter Identification (SEI) has been put forward as an Internet of Things (IoT) Physical Layer Security (PLS) approach for its ability to detect, characterize, and identify wireless emitters by exploiting distinct, inherent, and unintentional features in their transmitted signals. Since its introduction, a significant amount of work has been conducted; however, most assume the emitters are passive and that their identifying signal features are immutable and challenging to mimic. This suggests the emitters are reluctant and incapable of developing and implementing effective SEI countermeasures; however, Deep Learning (DL) has been shown capable of learning emitter-specific features directly from their raw in-phase and quadrature signal samples, while Software-Defined Radios (SDRs) are capable of manipulating these signal samples. Based on these capabilities, it is fair to question the ease at which an emitter can effectively mimic the SEI features of another or manipulate its own to hinder or defeat SEI. This work considers SEI mimicry using three signal features mimicking countermeasures; “off-the-self” DL; two SDRs of different sizes, weights, power, and cost (SWaP-C); handcrafted and DL-based SEI processes, and assessment of an SEI mimicry countermeasure in a ''coffee shop'' deployment. Our results show “off-the-shelf” DL algorithms, and SDR enables SEI mimicry; however, adversary success is hindered by: (i) the use of decoy emitter {preambles}, (ii) the use of a denoising autoencoder and (iii) SDR SWaP-C constraints.

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Radio Frequency Fingerprint Based Wireless Transmitter Identification Against Malicious Attacker: An Adversarial Learning Approach

Cited by 15 publications

References 7 publications

Effect of heavy metals on duckweed growth

Effect of heavy metals on duckweed growth

Considerations, Advances, and Challenges Associated with the Use of Specific Emitter Identification in the Security of Internet of Things Deployments: A Survey

Assessing Adversarial Replay and Deep Learning-Driven Attacks on Specific Emitter Identification-based Security Approaches

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