CERES :: Browsing by Author "Wei, Zhuangkun"

Browsing by Author "Wei, Zhuangkun"

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Open Access
Adversarial reconfigurable intelligent surface against physical layer key generation
(IEEE, 2023-04-12) Wei, Zhuangkun; Li, Bin; Guo, Weisi
The development of reconfigurable intelligent surfaces (RIS) has recently advanced the research of physical layer security (PLS). Beneficial impacts of RIS include but are not limited to offering a new degree-of-freedom (DoF) for key-less PLS optimization, and increasing channel randomness for physical layer secret key generation (PL-SKG). However, there is a lack of research studying how adversarial RIS can be used to attack and obtain legitimate secret keys generated by PL-SKG. In this work, we show an Eve-controlled adversarial RIS (Eve-RIS), by inserting into the legitimate channel a random and reciprocal channel, can partially reconstruct the secret keys from the legitimate PL-SKG process. To operationalize this concept, we design Eve-RIS schemes against two PL-SKG techniques used: (i) the CSI-based PL-SKG, and (ii) the two-way cross multiplication based PL-SKG. The channel probing at Eve-RIS is realized by compressed sensing designs with a small number of radio-frequency (RF) chains. Then, the optimal RIS phase is obtained by maximizing the Eve-RIS inserted deceiving channel. Our analysis and results show that even with a passive RIS, our proposed Eve-RIS can achieve a high key match rate with legitimate users, and is resistant to most of the current defensive approaches. This means the novel Eve-RIS provides a new eavesdropping threat on PL-SKG, which can spur new research areas to counter adversarial RIS attacks.
Open Access
Classification of RF transmitters in the presence of multipath effects using CNN-LSTM
(IEEE, 2024-06-09) Patil, Pradnya; Wei, Zhuangkun; Petrunin, Ivan; Guo, Weisi
Radio frequency (RF) communication systems are the backbone of many intelligent transport and aerospace operations, ensuring safety, connectivity, and efficiency. Accurate classification of RF transmitters is vital to achieve safe and reliable functioning in various operational contexts. One challenge in RF classification lies in data drifting, which is particularly prevalent due to atmospheric and multipath effects. This paper provides a convolutional neural network based long short-term memory (CNN-LSTM) framework to classify the RF emitters in drift environments. We first simulate popular-used RF transmitters and capture the RF signatures, while considering both power amplifier dynamic imperfections and the multipath effects through wireless channel models for data drifting. To mitigate data drift, we extract the scattering coefficient and approximate entropy, and incorpo-rate them with the in-phase quadrature (I/Q) signals as the input to the CNN-LSTM classifier. This adaptive approach enables the model to adjust to environmental variations, ensuring sustained accuracy. Simulation results show the accuracy performance of the proposed CNN-LSTM classifier, which achieves an overall 91.11% in the presence of different multipath effects, bolstering the resilience and precision of realistic classification systems over state of the art ensemble voting approaches.
Open Access
Control layer security: a new security paradigm for cooperative autonomous systems
(IEEE, 2023-07-21) Guo, Weisi; Wei, Zhuangkun; Gonzalez, Oscar; Perrusquía, Adolfo; Tsourdos, Antonios
Autonomous systems often cooperate to ensure safe navigation. Embedded within the centralised or distributed coordination mechanisms are a set of observations, unobservable states, and control variables. Security of data transfer between autonomous systems is crucial for safety, and both cryptography and physical layer security methods have been used to secure communication surfaces - each with its drawbacks and dependencies. Here, we show for the first time a new wireless Control Layer Security (CLS) mechanism. CLS exploits mutual physical states between cooperative autonomous systems to generate cipher keys. These mutual states are chosen to be observable to legitimate users and not sufficient to eavesdroppers, thereby enhancing the resulting secure capacity. The CLS cipher keys can encrypt data without key exchange or a common key pool, and offers very low information leakage. As such the security of digital data channels is now dependent on physical state estimation rather than wireless channel estimation. This protects the estimation process from wireless jamming and channel entropy dependency. We review for first time what kind of signal processing techniques are used for hidden state estimation and key generation, and the performance of CLS in different case studies.
Open Access
Control layer security: exploiting unobservable cooperative states of autonomous systems for secret key generation
(IEEE, 2024-02-26) Wei, Zhuangkun; Guo, Weisi
The rapid growth of autonomous systems (ASs) with data sharing means new cybersecurity methods have to be developed for them. Existing computational complexity-based cryptography does not have information-theoretical bounds and poses threats to superior computational attackers. This post-quantum cryptography issue indeed motivated the rapid advances in using common physical layer properties to generate symmetrical cipher keys (known as PLS). However, PLS remains sensitive to attackers (e.g., jamming) that destroy its prerequisite wireless channel reciprocity. When ASs are in cooperative tasks (e.g., rescuing searching, and formation flight), they will behave cooperatively in the control layer. Inspired by this, we propose a new security mechanism called control layer security (CLS), which exploits the correlated but unobservable states of cooperative ASs to generate symmetrical cipher keys. This idea is then realized in the linearized UAV cooperative control scenario. The theoretical correlation coefficients between Alice's and Bob's states are computed, based on which common feature selection and key quantization steps are designed. The results from simulation and real UAV experiments show i) an approximately 90% key agreement rate is achieved, and ii) even an Eve with the known observable states and systems fails to estimate the unobservable states and the secret keys relied upon, due to the multiple-to-one mapping from unobservable states (pitch, roll and yaw angles) to the observable states (3D trajectory). This demonstrates CLS as a promising candidate to secure the communications of ASs, especially in the adversarial radio environment with attackers that destroys the prerequisite for current PLS.
Open Access
Eavesdropping against bidirectional physical layer secret key generation in fiber communications
(IEEE, 2022-12-14) Hu, Wenxiu; Wei, Zhuangkun; Leeson, Mark; Xu, Tianhua
Physical layer secret key exploits the random but reciprocal channel features between legitimate users to encrypt their data against fiber-tapping. We propose a novel tapping-based eavesdropper scheme, leveraging its tapped signals from legitimate users to reconstruct their common features and the secret key.
Open Access
Frequency domain analysis and equalization for molecular communication
(IEEE, 2021-03-17) Huang, Yu; Ji, Fei; Wei, Zhuangkun; Wen, Miaowen; Chen, Xuan; Tang, Yuankun; Guo, Weisi
Molecular Communication (MC) is a promising micro-scale technology that enables wireless connectivity in electromagnetically challenged conditions. The signal processing approaches in MC are different from conventional wireless communications as molecular signals suffer from severe inter-symbol interference (ISI) and signal-dependent counting noise due to the stochastic diffusion process of the information molecules. One of the main challenges in MC is the high computational complexity of the existing time-domain ISI mitigation schemes that display a third-order polynomial or even exponential growth with the ISI length, which is further exasperated under the high symbol rate case. For the first time, we develop a frequency-domain equalization (FDE) with lower complexity, capable of achieving independence from the ISI effects. This innovation is grounded in our characterization of the channel frequency response of diffusion signals, facilitating the design of receiver sampling strategies. However, the perfect counting noise power is unavailable in the optimal minimum mean square error (MMSE) equalizer. We address this issue by exploiting the statistical information of the transmit signal and decision feedback for noise power estimation, designing novel MMSE equalizers with low complexity. The FDE for MC is successfully developed with its immunity to ISI effects, and its signal processing cost has only a logarithmic growth with symbol length in each block.
Open Access
Graph layer security: encrypting information via common networked physics
(MDPI, 2022-05-23) Wei, Zhuangkun; Wang, Liang; Sun, Schyler Chengyao; Li, Bin; Guo, Weisi
The proliferation of low-cost Internet of Things (IoT) devices has led to a race between wireless security and channel attacks. Traditional cryptography requires high computational power and is not suitable for low-power IoT scenarios. Whilst recently developed physical layer security (PLS) can exploit common wireless channel state information (CSI), its sensitivity to channel estimation makes them vulnerable to attacks. In this work, we exploit an alternative common physics shared between IoT transceivers: the monitored channel-irrelevant physical networked dynamics (e.g., water/oil/gas/electrical signal-flows). Leveraging this, we propose, for the first time, graph layer security (GLS), by exploiting the dependency in physical dynamics among network nodes for information encryption and decryption. A graph Fourier transform (GFT) operator is used to characterise such dependency into a graph-bandlimited subspace, which allows the generation of channel-irrelevant cipher keys by maximising the secrecy rate. We evaluate our GLS against designed active and passive attackers, using IEEE 39-Bus system. Results demonstrate that GLS is not reliant on wireless CSI, and can combat attackers that have partial networked dynamic knowledge (realistic access to full dynamic and critical nodes remains challenging). We believe this novel GLS has widespread applicability in secure health monitoring and for digital twins in adversarial radio environments.
Open Access
Hamming–Luby rateless codes for molecular erasure channels
(Elsevier, 2019-11-27) Wei, Zhuangkun; Li, Bin; Hu, Wenxiu; Guo, Weisi; Zhao, Chenglin
Nano-scale molecular communications encode digital information into discrete macro-molecules. In many nano-scale systems, due to limited molecular energy, each information symbol is encoded into a small number of molecules. As such, information may be lost in the process of diffusion–advection propagation through complex topologies and membranes. Existing Hamming-distance codes for additive counting noise are not well suited to combat the aforementioned erasure errors. Rateless Luby-Transform (LT) code and cascaded Hamming-LT (Raptor) are suitable for information-loss, however may consume substantially computational energy due to the repeated uses of random number generator and exclusive OR (XOR). In this paper, we design a novel low-complexity erasure combating encoding scheme: the rateless Hamming–Luby Transform code. The proposed rateless code combines the superior efficiency of Hamming codes with the performance guarantee advantage of Luby Transform (LT) codes, therefore can reduce the number of random number generator utilizations. We design an iterative soft decoding scheme via successive cancelation to further improve the performance. Numerical simulations show this new rateless code can provide comparable performance comparing with both standard LT and Raptor codes, while incurring a lower decoder computational complexity, which is useful for the envisaged resources constrained nano-machines
Open Access
High-dimensional metric combining for non-coherent molecular signal detection
(IEEE, 2019-12-13) Wei, Zhuangkun; Guo, Weisi; Li, Bin; Charmet, Jérôme; Zhao, Chenglin
In emerging Internet-of-Nano-Thing (IoNT), information will be embedded and conveyed in the form of molecules through complex and diffusive medias. One main challenge lies in the long-tail nature of the channel response causing inter-symbol-interference (ISI), which deteriorates the detection performance. If the channel is unknown, existing coherent schemes (e.g., the state-of-the-art maximum a posteriori, MAP) have to pursue complex channel estimation and ISI mitigation techniques, which will result in either high computational complexity, or poor estimation accuracy that will hinder the detection performance. In this paper, we develop a novel high-dimensional non-coherent detection scheme for molecular signals. We achieve this in a higher-dimensional metric space by combining different non-coherent metrics that exploit the transient features of the signals. By deducing the theoretical bit error rate (BER) for any constructed high-dimensional non-coherent metric, we prove that, higher dimensionality always achieves a lower BER in the same sample space, at the expense of higher complexity on computing the multivariate posterior densities. The realization of this high-dimensional non-coherent scheme is resorting to the Parzen window technique based probabilistic neural network (Parzen-PNN), given its ability to approximate the multivariate posterior densities by taking the previous detection results into a channel-independent Gaussian Parzen window, thereby avoiding the complex channel estimations. The complexity of the posterior computation is shared by the parallel implementation of the Parzen-PNN. Numerical simulations demonstrate that our proposed scheme can gain 10dB in SNR given a fixed BER as 10 -4 , in comparison with other state-of-the-art methods.
Open Access
Monitoring embedded flow networks using graph Fourier transform enabled sparse molecular relays
(IEEE, 2020-03-06) Wei, Zhuangkun; Pagani, Alessio; Li, Bin; Guo, Weisi
Many embedded networks are difficult to monitor, such as water distribution networks (WDNs). A key challenge is how to use minimum sparse sensors to measure contamination and transmit contamination data to a hub for system analysis. Existing approaches deploy sensors using multi-objective optimisation and transmit the data using ground penetrating waves or fixed-line access. Here, for the first time, we introduce a novel molecular communication relay system, which is able to transmit the data report to the hub via the water-flow of WDN itself, and avoids the complex ground penetrating techniques. A water flow data-driven Graph Fourier Transform (GFT) sampling method is designed to inform the invariant orthogonal locations for deploying the molecular relay sensors. Each sensor encodes information via a DNA molecule that enables the common hub to reconstruct the full contamination information. Numerical simulation validates the proposed system, providing a pathway to integrate MC into macro-scale Digital Twin platforms for infrastructure monitoring.
Open Access
A multi-eavesdropper scheme against RIS secured LoS-dominated channel
(IEEE, 2022-04-11) Wei, Zhuangkun; Guo, Weisi; Li, Bin
Reconfigurable intelligent surface (RIS) has been shown as a promising technique to increase the channel randomness for secret key generation (SKG) in low-entropy channels (e.g., static or line-of-sight (LoS)), without small-scale fading. In this letter, we show that even with the aid of RIS, collaborative eavesdroppers (Eves) can still estimate the legitimate Alice-Bob channel and erode their secret key rates (SKRs), since the RIS induced randomness is also reflected in the Eves’ observations. Conditioned on Eves’ observations, if the entropy of RIS-combined legitimate channel is zero, Eves are able to estimate it and its secret key. Leveraging this, we design a multi-Eve scheme against the RIS-secured LoS dominated scenarios, by using the multiple Eves’ observations to reconstruct the RIS-combined legitimate channel. We further deduce a closed-form secret key leakage rate under our designed multi-Eve scheme, and demonstrate the results via simulations.
Open Access
Neural network approximation of graph Fourier transform for sparse sampling of networked dynamics
(Association for Computing Machinery (ACM), 2021-09-14) Pagani, Alessio; Wei, Zhuangkun; Silva, Ricardo; Guo, Weisi
Infrastructure monitoring is critical for safe operations and sustainability. Like many networked systems, water distribution networks (WDNs) exhibit both graph topological structure and complex embedded flow dynamics. The resulting networked cascade dynamics are difficult to predict without extensive sensor data. However, ubiquitous sensor monitoring in underground situations is expensive, and a key challenge is to infer the contaminant dynamics from partial sparse monitoring data. Existing approaches use multi-objective optimization to find the minimum set of essential monitoring points but lack performance guarantees and a theoretical framework. Here, we first develop a novel Graph Fourier Transform (GFT) operator to compress networked contamination dynamics to identify the essential principal data collection points with inference performance guarantees. As such, the GFT approach provides the theoretical sampling bound. We then achieve under-sampling performance by building auto-encoder (AE) neural networks (NN) to generalize the GFT sampling process and under-sample further from the initial sampling set, allowing a very small set of data points to largely reconstruct the contamination dynamics over real and artificial WDNs. Various sources of the contamination are tested, and we obtain high accuracy reconstruction using around 5%–10% of the network nodes for known contaminant sources, and 50%–75% for unknown source cases, which although larger than that of the schemes for contaminant detection and source identifications, is smaller than the current sampling schemes for contaminant data recovery. This general approach of compression and under-sampled recovery via NN can be applied to a wide range of networked infrastructures to enable efficient data sampling for digital twins.
Open Access
Physical-layer counterattack strategies for the internet of bio-nano things with molecular communication
(IEEE, 2023-06-06) Huang, Yu; Wen, Miaowen; Lin, Lin; Li, Bin; Wei, Zhuangkun; Tang, Dong; Li, Jun; Duan, Wei; Guo, Weisi
Molecular communication (MC) is an emerging new communication paradigm where information is conveyed by chemical signals. It has been recognized as one of the most promising physical layer techniques for the future Internet of Bio-Nano Things (IoBNT), which enables revolutionary applications beyond our imagination. Compared with conventional communication systems, MC typically demands a higher security level as the IoBNT is deeply associated with the biochemical process. Against this background, this article first discusses the security and privacy issues of IoBNT with MC. Then, the physical-layer countermeasures against the threat are presented from an interdisciplinary perspective concerning data science, signal processing techniques, and the biochemical properties of MC. Correspondingly, both the keyless and key-based schemes are conceived and revisited. Finally, some open research issues and future research directions for secrecy enhancement in IoBNT with MC are put forward.
Open Access
Revealing the excitation causality between climate and political violence via a neural forward-intensity Poisson process
(arXiv, 2022-07-29) Sun, Schyler C.; Jin, Bailu; Wei, Zhuangkun; Guo, Weisi
The causal mechanism between climate and political violence is fraught with complex mechanisms. Current quantitative causal models rely on one or more assumptions: (1) the climate drivers persistently generate conflict, (2) the causal mechanisms have a linear relationship with the conflict generation parameter, and/or (3) there is sufficient data to inform the prior distribution. Yet, we know conflict drivers often excite a social transformation process which leads to violence (e.g., drought forces agricultural producers to join urban militia), but further climate effects do not necessarily contribute to further violence. Therefore, not only is this bifurcation relationship highly non-linear, there is also often a lack of data to support prior assumptions for high resolution modeling. Here, we aim to overcome the aforementioned causal modeling challenges by proposing a neural forward-intensity Poisson process (NFIPP) model. The NFIPP is designed to capture the potential non-linear causal mechanism in climate induced political violence, whilst being robust to sparse and timing-uncertain data. Our results span 20 recent years and reveal an excitation-based causal link between extreme climate events and political violence across diverse countries. Our climate-induced conflict model results are cross-validated against qualitative climate vulnerability indices. Furthermore, we label historical events that either improve or reduce our predictability gain, demonstrating the importance of domain expertise in informing interpretation.
Open Access
A review of digital twin technologies for enhanced sustainability in the construction industry
(MDPI, 2024-04-16) Zhang, Zichao; Wei, Zhuangkun; Court, Samuel; Yang, Lichao; Wang, Shuozhi; Thirunavukarasu, Arjun; Zhao, Yifan
Carbon emissions present a pressing challenge to the traditional construction industry, urging a fundamental shift towards more sustainable practices and materials. Recent advances in sensors, data fusion techniques, and artificial intelligence have enabled integrated digital technologies (e.g., digital twins) as a promising trend to achieve emission reduction and net-zero. While digital twins in the construction sector have shown rapid growth in recent years, most applications focus on the improvement of productivity, safety and management. There is a lack of critical review and discussion of state-of-the-art digital twins to improve sustainability in this sector, particularly in reducing carbon emissions. This paper reviews the existing research where digital twins have been directly used to enhance sustainability throughout the entire life cycle of a building (including design, construction, operation and maintenance, renovation, and demolition). Additionally, we introduce a conceptual framework for this industry, which involves the elements of the entire digital twin implementation process, and discuss the challenges faced during deployment, along with potential research opportunities. A proof-of-concept example is also presented to demonstrate the validity of the proposed conceptual framework and potential of digital twins for enhanced sustainability. This study aims to inspire more forward-thinking research and innovation to fully exploit digital twin technologies and transform the traditional construction industry into a more sustainable sector.
Open Access
Review of physical layer security in molecular internet of nano-things
(IEEE, 2023-06-14) Qiu, Song; Wei, Zhuangkun; Huang, Yu; Abbaszadeh, Mahmoud; Charmet, Jerome; Li, Bin; Guo, Weisi
Molecular networking has been identified as a key enabling technology for Internet-of-Nano-Things (IoNT): microscopic devices that can monitor, process information, and take action in a wide range of medical applications. As the research matures into prototypes, the cybersecurity challenges of molecular networking are now being researched on at both the cryptographic and physical layer level. Due to the limited computation capabilities of IoNT devices, physical layer security (PLS) is of particular interest. As PLS leverages on channel physics and physical signal attributes, the fact that molecular signals differ significantly from radio frequency signals and propagation means new signal processing methods and hardware is needed. Here, we review new vectors of attack and new methods of PLS, focusing on 3 areas: (1) information theoretical secrecy bounds for molecular communications, (2) key-less steering and decentralized key-based PLS methods, and (3) new methods of achieving encoding and encryption through bio-molecular compounds. The review will also include prototype demonstrations from our own lab that will inform future research and related standardization efforts.
Open Access
Robust time synchronisation for industrial internet of things by H∞ output feedback control
(IEEE, 2022-01-20) Zong, Yan; Dai, Xuewu; Wei, Zhuangkun; Zou, Mengbang; Guo, Weisi; Gao, Zhiwei
Precise timing over timestamped packet exchange communication is an enabling technology in the mission-critical industrial Internet of Things, particularly when satellite-based timing is unavailable. The main challenge is to ensure timing accuracy when the clock synchronisation system is subject to disturbances caused by the drifting frequency, time-varying delay, jitter, and timestamping uncertainty. In this work, a Robust Packet-Coupled Oscillators (R-PkCOs) protocol is proposed to reduce the effects of perturbations manifested in the drifting clock, timestamping uncertainty and delays. First, in the spanning tree clock topology, time synchronisation between an arbitrary pair of clocks is modelled as a state-space model, where clock states are coupled with each other by one-way timestamped packet exchange (referred to as packet coupling), and the impacts of both drifting frequency and delays are modelled as disturbances. A static output controller is adopted to adjust the drifting clock. The H∞ robust control design solution is proposed to guarantee that the ratio between the modulus of synchronisation precision and the magnitude of the disturbances is always less than a given value. Therefore, the proposed time synchronisation protocol is robust against the disturbances, which means that the impacts of drifting frequency and delays on the synchronisation accuracy are limited. The one-hour experimental results demonstrate that the proposed R-PkCOs protocol can realise time synchronisation with the precision of six microseconds in a 21-node IEEE 802.15.4 network. This work has widespread impacts in the process automation of automotive, mining, oil and gas industries.
Open Access
Sampling and inference of networked dynamics using Log-Koopman nonlinear graph fourier transform
(IEEE, 2020-10-21) Wei, Zhuangkun; Li, Bin; Sun, Chengyao; Guo, Weisi
Monitoring the networked dynamics via the subset of nodes is essential for a variety of scientific and operational purposes. When there is a lack of an explicit model and networked signal space, traditional observability analysis and non-convex methods are insufficient. Current data-driven Koopman linearization, although derives a linear evolution model for selected vector-valued observable of original state-space, may result in a large sampling set due to: (i) the large size of polynomial based observables (O(N2) , N number of nodes in network), and (ii) not factoring in the nonlinear dependency betweenobservables. In this work, to achieve linear scaling (O(N) ) and a small set of sampling nodes, wepropose to combine a novel Log-Koopman operator and nonlinear Graph Fourier Transform (NL-GFT) scheme. First, the Log-Koopman operator is able to reduce the size of observables by transforming multiplicative poly-observable to logarithm summation. Second, anonlinear GFT concept and sampling theory are provided to exploit the nonlinear dependence of observables for observability analysis using Koopman evolution model. The results demonstrate that the proposed Log-Koopman NL-GFT scheme can (i) linearize unknownnonlinear dynamics using O(N) observables, and (ii) achieve lower number of sampling nodes, compared with the state-of-the art polynomial Koopman based observability analysis.
Open Access
Scarce data driven deep learning of drones via generalized data distribution space
(Springer, 2023-04-06) Li, Chen; Sun, Schyler C.; Wei, Zhuangkun; Tsourdos, Antonios; Guo, Weisi
Increased drone proliferation in civilian and professional settings has created new threat vectors for airports and national infrastructures. The economic damage for a single major airport from drone incursions is estimated to be millions per day. Due to the lack of balanced representation in drone data, training accurate deep learning drone detection algorithms under scarce data is an open challenge. Existing methods largely rely on collecting diverse and comprehensive experimental drone footage data, artificially induced data augmentation, transfer and meta-learning, as well as physics-informed learning. However, these methods cannot guarantee capturing diverse drone designs and fully understanding the deep feature space of drones. Here, we show how understanding the general distribution of the drone data via a generative adversarial network (GAN), and explaining the under-learned data features using topological data analysis (TDA) can allow us to acquire under-represented data to achieve rapid and more accurate learning. We demonstrate our results on a drone image dataset, which contains both real drone images as well as simulated images from computer-aided design. When compared to random, tag-informed and expert-informed data collections (discriminator accuracy of 94.67%, 94.53% and 91.07%, respectively, after 200 epochs), our proposed GAN-TDA-informed data collection method offers a significant 4% improvement (99.42% after 200 epochs). We believe that this approach of exploiting general data distribution knowledge from neural networks can be applied to a wide range of scarce data open challenges.
Open Access
Secret key rate upper-bound for reconfigurable intelligent surface-combined system under spoofing
(IEEE, 2023-01-18) Wei, Zhuangkun; Wang, Liang; Guo, Weisi
Reconfigurable intelligent surfaces (RIS) have been shown to improve the secret key rate (SKR) for physical layer secret key generation (PL-SKG), by using the programmable phase shifts to increase reciprocal channel entropy. Most current studies consider the role of RIS on passive eavesdroppers (Eves) and overlook active attackers, especially the pilot spoofing attacks (PSA). For PSA in PL-SKG setups, this is implemented by Eve sending an amplified pilot sequence simultaneously with legitimate user Alice. With the increase of the spoofing amplifying factor, the channel probing results at Bob and Eve become similar, thereby enabling Eve to generate shared secret key with Bob. In this work, we analyze how RIS can positively or negatively affect the PL-SKG under pilot spoofing. To do so, we theoretically express the legitimate and spoofing SKRs in terms of the RIS phase shifts. Leveraging this, the closed-form theoretical upper bounds of both legitimate and spoofing SKRs are deduced, which lead to two further findings. First, the legitimate SKR upper-bound does not vary with RIS phase shift vector, but reduces drastically with the increase of the spoofing amplifying factor. This suggests the limited effect of RIS against PL-SKG spoofing, since the legitimate SKR has a hard limit, which cannot be surpassed by adjusting RIS phase and reflecting power, but can even be 0 with properly assigned spoofing amplifying factor. Second, the spoofing SKR upper-bound shows a large gap from the non-optimized SKR, which indicates a potential for RIS phase optimization.