A Novel Physical Layer Key Generation Method Based on WGAN-GP Adversarial Autoencoder

Han, Jingyuan; Zhou, Yi; Liu, Gang; Liu, Tao; Zeng, Xin

doi:10.1109/cisce55963.2022.9851065

Cited by 5 publications

(7 citation statements)

References 13 publications

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“…Simulation results indicate that by using GAN, the authors can obtain better detection performance than using the conventional CNN network. Differently, the authors in [95] leverage GAN as an effective tool for physical layer key generation. It is well known that wireless communications are susceptible to radio attacks such as eavesdropping and tampering due to their broadcast nature.…”

Section: Channel Equalizationmentioning

confidence: 99%

“…In general, DL is superior to conventional approaches in extracting symmetric keys from reciprocal channel responses. the authors in [95] reveal that conventional DNNs are unpredictable for physical layer key generation. In addition, it is challenging to apply the extracted high-dimensional features to generate the physical layer key.…”

Section: Channel Equalizationmentioning

confidence: 99%

“…Incorporating an encoder network into the original GAN to reconstruct the spectrogram [94] Most detection methods cannot effectively detect spoofing jamming if spoofing signals are similar to authentic signals Design a GAN network that is trained on a large dataset of authentic satellite signals to accurately learn their distribution [103] Cannot effectively use to perform attacks Use GAN to construct synthetic RF signals that are similar to legitimate signals [95] Difficult to apply to the key generation in the physical layer Propose a key generation method based on GAN to extract features efficiently between legitimate nodes [104] Not effective in anomaly detection as GAI Use GAN to identify unrecognized patterns on the model outputs and associated sequenced metadata [90] Poor performance when spectrum data is not sufficient Use GAN to generate synthetic spectrum data that can help DRL to effectively learn and obtain the optimal dynamic spectrum anti-jamming access policy [92] Lack of labeled data Use GAN to learn the distribution of collected signals [86] Need attackers' information for training Use VAEs to extract valuable features of high-dimensional channel impulse responses for authentication GAN network to augment the training dataset of the classifier with adversarial samples generated from adversaries' GAN networks. Simulation results then show that by augmenting the training data with GAN the authors can effectively improve the classification accuracy under GAN-based adversarial attacks.…”

Section: Channel Equalizationmentioning

confidence: 99%

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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%

Section: Channel Equalizationmentioning

confidence: 99%

Section: Channel Equalizationmentioning

confidence: 99%

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Effect of heavy metals on duckweed growth

Nguyen¹,

Hoang²,

Pham³

et al. 2022

CTUJS

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“…Learn unique waveforms features for attacks [85] Extract/analyze complex channel character. for identity authentication [86] Robust RF Adversarial learning to identify rogue devices [87] Impersonation Attacks Using GANs [88] Channel Characteristics Based Key generation [89] C. Anomaly & Attack Detection…”

Section: Pla and Adversarial Trainingmentioning

confidence: 99%

“…9 Key Generation via WGANs with Gradient Penalty: In [89], the authors propose a key generation method based on WGANs with Gradient Penalty (WGAN-GP) adversarial autoencoder for PLS in wireless channels. The method leverages the channel reciprocity between legitimate nodes to extract symmetric keys using DNNs, outperforming traditional methods in terms of freedom and performance.…”

Section: A Phy Layer Security and Authenticationmentioning

confidence: 99%

At the Dawn of Generative AI Era: A Tutorial-cum-Survey on New Frontiers in 6G Wireless Intelligence

Celik,

Eltawil

2024

IEEE Open J. Commun. Soc.

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As we transition from the 5G epoch, a new horizon beckons with the advent of 6G, seeking a profound fusion with novel communication paradigms and emerging technological trends, bringing once-futuristic visions to life along with added technical intricacies. Although analytical models lay the foundations and offer systematic insights, we have recently witnessed a noticeable surge in research suggesting machine learning (ML) and artificial intelligence (AI) can efficiently deal with complex problems by complementing or replacing model-based approaches. The majority of data-driven wireless research leans heavily on discriminative AI (DAI) that requires vast real-world datasets. Unlike the DAI, Generative AI (GenAI) pertains to generative models (GMs) capable of discerning the underlying data distribution, patterns, and features of the input data. This makes GenAI a crucial asset in wireless domain wherein real-world data is often scarce, incomplete, costly to acquire, and hard to model or comprehend. With these appealing attributes, GenAI can replace or supplement DAI methods in various capacities.Accordingly, this combined tutorial-survey paper commences with preliminaries of 6G and wireless intelligence by outlining candidate 6G applications and services, presenting a taxonomy of state-of-theart DAI models, exemplifying prominent DAI use cases, and elucidating the multifaceted ways through which GenAI enhances DAI. Subsequently, we present a tutorial on GMs by spotlighting seminal examples such as generative adversarial networks, variational autoencoders, flow-based GMs, diffusion-based GMs, generative transformers, large language models, autoregressive GMs, to name a few. Contrary to the prevailing belief that GenAI is a nascent trend, our exhaustive review of approximately 120 technical papers demonstrates the scope of research across core wireless research areas, including 1) physical layer design; 2) network optimization, organization, and management; 3) network traffic analytics; 4) crosslayer network security; and 5) localization & positioning. Furthermore, we outline the central role of GMs in pioneering areas of 6G network research, including semantic communications, integrated sensing and communications, THz communications, extremely large antenna arrays, near-field communications, digital twins, AI-generated content services, mobile edge computing and edge AI, adversarial ML, and trustworthy AI. Lastly, we shed light on the multifarious challenges ahead, suggesting potential strategies and promising remedies. Given its depth and breadth, we are confident that this tutorial-cum-survey will serve as a pivotal reference for researchers and professionals delving into this dynamic and promising domain.

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