Browsing by Author "Li, Bin"

Now showing 1 - 11 of 11
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    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.
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    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.
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    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
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    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.
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    Machine learning-enabled globally guaranteed evolutionary computation
    (Nature Publishing Group, 2023-04-10) Li, Bin; Wei, Ziping; Wu, Jingjing; Yu, Shuai; Zhang, Tian; Zhu, Chunli; Zheng, Dezhi; Guo, Weisi; Zhao, Chenglin; Zhang, Jun
    Evolutionary computation, for example, particle swarm optimization, has impressive achievements in solving complex problems in science and industry; however, an important open problem in evolutionary computation is that there is no theoretical guarantee of reaching the global optimum and general reliability; this is due to the lack of a unified representation of diverse problem structures and a generic mechanism by which to avoid local optima. This unresolved challenge impairs trust in the applicability of evolutionary computation to a variety of problems. Here we report an evolutionary computation framework aided by machine learning, named EVOLER, which enables the theoretically guaranteed global optimization of a range of complex non-convex problems. This is achieved by: (1) learning a low-rank representation of a problem with limited samples, which helps to identify an attention subspace; and (2) exploring this small attention subspace via the evolutionary computation method, which helps to reliably avoid local optima. As validated on 20 challenging benchmarks, this method finds the global optimum with a probability approaching 1. We use EVOLER to tackle two important problems: power grid dispatch and the inverse design of nanophotonics devices. The method consistently reached optimal results that were challenging to achieve with previous state-of-the-art methods. EVOLER takes a leap forwards in globally guaranteed evolutionary computation, overcoming the uncertainty of data-driven black-box methods, and offering broad prospects for tackling complex real-world problems.
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    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.
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    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.
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    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.
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    Random sketch learning for deep neural networks in edge computing
    (Springer Nature, 2021-03-25) Li, Bin; Chen, Peijun; Liu, Hongfu; Guo, Weisi; Cao, Xianbin; Du, Junzhao; Zhao, Chenglin; Zhang, Jun
    Despite the great potential of deep neural networks (DNNs), they require massive weights and huge computational resources, creating a vast gap when deploying artificial intelligence at low-cost edge devices. Current lightweight DNNs, achieved by high-dimensional space pre-training and post-compression, present challenges when covering the resources deficit, making tiny artificial intelligence hard to be implemented. Here we report an architecture named random sketch learning, or Rosler, for computationally efficient tiny artificial intelligence. We build a universal compressing-while-training framework that directly learns a compact model and, most importantly, enables computationally efficient on-device learning. As validated on different models and datasets, it attains substantial memory reduction of ~50–90× (16-bits quantization), compared with fully connected DNNs. We demonstrate it on low-cost hardware, whereby the computation is accelerated by >180× and the energy consumption is reduced by ~10×. Our method paves the way for deploying tiny artificial intelligence in many scientific and industrial applications.
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    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.
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    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.