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Digital twin modeling and controlling of optical power evolution enabling autonomous-driving optical networks:a Bayesian approach

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Advanced Photonics 2024年第2期6卷 69-80页

作者： Xiaomin Liu Yihao Zhang Yuli Chen Yichen Liu Meng Cai Qizhi Qiu Mengfan Fu Lilin Yi Weisheng Hu Qunbi Zhuge Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and NetworksDepartment of Electronic EngineeringShanghaiChina

Optical networks are evolving toward ultrawide bandwidth and autonomous operation.In this scenario,it is crucial to accurately model and control optical power evolutions(OPEs)through optical amplifiers(OAs),as they directly affect the signal-to-noise ratio and fiber nonlinearities.However,a fundamental contradiction arises between the complex physical phenomena in optical transmission and the required precision in network control.Traditional theoretical methods underperform due to ideal assumptions,while data-driven approaches entail exorbitant costs associated with acquiring massive amounts of data to achieve the desired level of accuracy.In this work,we propose a Bayesian inference framework(BIF)to construct the digital twin of OAs and control OPE in a data-efficient manner.Only the informative data are collected to balance the exploration and exploitation of the data space,thus enabling efficient autonomous-driving optical networks(ADONs).Simulations and experiments demonstrate that the BIF can reduce the data size for modeling erbium-doped fiber amplifiers by 80%and Raman amplifiers by 60%.Within 30 iterations,the optimal controlling performance can be achieved to realize target signal/gain profiles in links with different types of OAs.The results show that the BIF paves the way to accurately model and control OPE for future ADONs.

关键词： optical fiber communications digital twin Bayesian inference optical amplifiers autonomous-driving optical networks

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Silicon photonic integrated interrogator for fiber-optic distributed acoustic sensing

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Photonics Research 2024年第3期12卷 465-473页

作者： ZHICHENG JIN JIAGENG CHEN YANMING CHANG QINGWEN LIU ZUYUAN HE State Key Laboratory of Advanced Optical Communication System and networks Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghai 200240China

Distributed acoustic sensing(DAS)technology has been a promising tool in various applications.Currently,the large size and relatively high cost of DAS equipment composed of discrete devices restrict its further popularization to some degree,and the photonic integration technology offers a potential solution.In this paper,we demonstrate an integrated interrogator for DAS on the silicon-on-insulator(SOI)platform.The design of the chip revolves around a Mach–Zehnder modulator(MZM)transmitter and a dual-quadrature and dualpolarization coherent receiver.The integrated interrogator supports multiple DAS schemes,including the time-gated digital optical frequency domain reflectometry(TGD-OFDR),which is adopted for system performance evaluation.59■strain resolution in 12.1km sensing fiber with 1.14 m spatial resolution(SR)is realized.Besides,along 49.0 km sensing fiber,81■strain resolution with 3.78 m SR is achieved.The results show that the integrated interrogator has comparable performance to the discrete DAS system.To the best of our knowledge,this is the first dedicated on-chip DAS interrogator,which validates the effectiveness of the blend of photonics integration and DAS technology.

关键词： fiber polarization modulator

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Analog spatiotemporal feature extraction for cognitive radio-frequency sensing with integrated photonics

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Light(Science & Applications) 2024年第3期13卷 478-487页

作者： Shaofu Xu Binshuo Liu Sicheng Yi Jing Wang Weiwen Zou State Key Laboratory of Advanced Optical Communication Systems and networks Intelligent Microwave Lightwave Integration Innovation Center(imLic)Department of Electronic EngineeringShanghai Jiao Tong UniversityShanghaiChina

Analog feature extraction(AFE)is an appealing strategy for low-latency and efficient cognitive sensing systems since key features are much sparser than the Nyquist-sampled data.However,applying AFE to broadband radio-frequency(RF)scenarios is challenging due to the bandwidth and programmability bottlenecks of analog electronic circuitry.Here,we introduce a photonics-based scheme that extracts spatiotemporal features from broadband RF signals in the analog domain.The feature extractor structure inspired by convolutional neural networks is implemented on integrated photonic circuits to process RF signals from multiple antennas,extracting valid features from both temporal and spatial dimensions.Because of the tunability of the photonic devices,the photonic spatiotemporal feature extractor is trainable,which enhances the validity of the extracted features.Moreover,a digital-analog-hybrid transfer learning method is proposed for the effective and low-cost training of the photonic feature extractor.To validate our scheme,we demonstrate a radar target recognition task with a 4-GHz instantaneous bandwidth.Experimental results indicate that the photonic analog feature extractor tackles broadband RF signals and reduces the sampling rate of analog-to-digital converters to 1/4 of the Nyquist sampling while maintaining a high target recognition accuracy of 97.5%.Our scheme offers a promising path for exploiting the AFE strategy in the realm of cognitive RF sensing,with the potential to contribute to the efficient signal processing involved in applications such as autonomous driving,robotics,and smart factories.

关键词： scheme driving smart

FMSA:a meta-learning framework-based fast model stealing attack technique against intelligent network intrusion detection systems

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Cybersecurity 2024年第1期7卷 110-121页

作者： Kaisheng Fan Weizhe Zhang Guangrui Liu Hui He School of Cyberspace Science Harbin Institute of TechnologyHarbin 150001China department of New networks Peng Cheng LaboratoryShenzhen 518055GuangdongChina

Intrusion detection systems are increasingly using machine learning.While machine learning has shown excellent performance in identifying malicious traffic,it may increase the risk of privacy leakage.This paper focuses on imple-menting a model stealing attack on intrusion detection systems.Existing model stealing attacks are hard to imple-ment in practical network environments,as they either need private data of the victim dataset or frequent access to the victim model.In this paper,we propose a novel solution called Fast Model Stealing Attack(FMSA)to address the problem in the field of model stealing attacks.We also highlight the risks of using ML-NIDS in network security.First,meta-learning frameworks are introduced into the model stealing algorithm to clone the victim model in a black-box state.Then,the number of accesses to the target model is used as an optimization term,resulting in minimal queries to achieve model stealing.Finally,adversarial training is used to simulate the data distribution of the target model and achieve the recovery of privacy data.Through experiments on multiple public datasets,compared to existing state-of-the-art algorithms,FMSA reduces the number of accesses to the target model and improves the accuracy of the clone model on the test dataset to 88.9%and the similarity with the target model to 90.1%.We can demonstrate the successful execution of model stealing attacks on the ML-NIDS system even with protective measures in place to limit the number of anomalous queries.

关键词： AI security Model stealing attack Network intrusion detection Meta learning

Integrated optical frequency transfer and optical physical layer key distribution with enhanced link reciprocity detection

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Science China(Information Sciences) 2024年第7期67卷 335-336页

作者： Kunfeng XIE Liang HU Jianping CHEN Guiling WU State Key Laboratory of Advanced Optical Communication System and networks Department of Electronic EngineeringShanghai Jiao Tong University

Optical fiber transmission has been widely used in communications and metrology, wherein optical frequency transfer (OFT) and optical physical layer secure key distribution (OKD) have become research hotspots [1, 2]. Moreover,OFT can perform a comparison between state-of-the-art optical atomic clocks and implement coherent optical phase transfer over long distances to support geodesy [1, 3].

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NFA:A neural factorization autoencoder based online telephony fraud detection

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Digital Communications and networks 2024年第1期10卷 158-167页

作者： Abdul Wahid Mounira Msahli Albert Bifet Gerard Memmi department of Computer Sciences and networks(INFRES) LTCITelecom ParisInstitut Polytechnique de ParisPalaiseau91120France Artificial Inteligence Institute The University of WaikatoPrivate Bag 31053240HamiltonNew Zealand

The proliferation of internet communication channels has increased telecom fraud,causing billions of euros in losses for customers and the industry each year.Fraudsters constantly find new ways to engage in illegal activity on the network.To reduce these losses,a new fraud detection approach is *** fraud detection involves identifying a small number of fraudulent calls from a vast amount of call traffic.Developing an effective strategy to combat fraud has become challenging.Although much effort has been made to detect fraud,most existing methods are designed for batch processing,not real-time detection.To solve this problem,we propose an online fraud detection model using a Neural Factorization Autoencoder(NFA),which analyzes customer calling patterns to detect fraudulent calls.The model employs Neural Factorization Machines(NFM)and an Autoencoder(AE)to model calling patterns and a memory module to adapt to changing customer behaviour.We evaluate our approach on a large dataset of real-world call detail records and compare it with several state-of-the-art methods.Our results show that our approach outperforms the baselines,with an AUC of 91.06%,a TPR of 91.89%,an FPR of 14.76%,and an F1-score of 95.45%.These results demonstrate the effectiveness of our approach in detecting fraud in real-time and suggest that it can be a valuable tool for preventing fraud in telecommunications networks.

关键词： Telecom industry Streaming anomaly detection Fraud analysis Factorization machine Real-time system Security

Joint position optimization,user association,and resource allocation for load balancing in UAV-assisted wireless networks

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Digital Communications and networks 2024年第1期10卷 25-37页

作者： Daosen Zhai Huan Li Xiao Tang Ruonan Zhang Haotong Cao School of Electronics and Information Northwestern Polytechnical UniversityXian710072China State Key Laboratory of Integrated Services networks Xidian UniversityXian710071China department of Computing Hong Kong Polytechnic UniversityHong Kong SARChina

Unbalanced traffic distribution in cellular networks results in congestion and degrades spectrum efficiency.To tackle this problem,we propose an Unmanned Aerial Vehicle(UAV)-assisted wireless network in which the UAV acts as an aerial relay to divert some traffic from the overloaded cell to its adjacent underloaded cell.To fully exploit its potential,we jointly optimize the UAV position,user association,spectrum allocation,and power allocation to maximize the sum-log-rate of all users in two adjacent cells.To tackle the complicated joint optimization problem,we first design a genetic-based algorithm to optimize the UAV position.Then,we simplify the problem by theoretical analysis and devise a low-complexity algorithm according to the branch-and-bound method,so as to obtain the optimal user association and spectrum allocation schemes.We further propose an iterative power allocation algorithm based on the sequential convex approximation theory.The simulation results indicate that the proposed UAV-assisted wireless network is superior to the terrestrial network in both utility and throughput,and the proposed algorithms can substantially improve the network performance in comparison with the other schemes.

关键词： Load balance Unmanned aerial vehicle Userassociation Resource management

Whispering-gallery-mode barcode-based broadband sub-femtometer-resolution spectroscopy with an electro-optic frequency comb

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Advanced Photonics 2024年第1期6卷 115-125页

作者： Bingxin Xu Yangyang Wan Xinyu Fan Zuyuan He Shanghai Jiao Tong University State Key Laboratory of Advanced Optical Communication Systems and NetworksDepartment of Electronic EngineeringShanghaiChina Max-Planck Institute of Quantum Optics Hans-Kopfermann-Straûe 185748 GarchingGermany

Spectroscopy is the basic tool for studying molecular physics and realizing biochemical sensing.However,it is challenging to realize sub-femtometer resolution spectroscopy over broad bandwidth.Broadband and high-resolution spectroscopy with calibrated optical frequency is demonstrated by bridging the fields of speckle pattern and electro-optic frequency comb.A wavemeter based on a whispering-gallery-mode barcode is proposed to link the frequencies of a probe continuous-wave laser and an ultrastable laser.The ultrafine electro-optic comb lines are generated from the probe laser to record spectrum of sample with sub-femtometer resolution.Measurement bandwidth is a thousandfold broader than comb bandwidth,by sequentially tuning the probe laser while its wavelength is determined.This approach fully exploits the advantages of two fields to realize 0.8-fm resolution with a fiber laser and 80-nm bandwidth with an external cavity diode laser.The spectroscopic measurements of an ultrahigh Q-factor cavity and gas molecular absorption are experimentally demonstrated.The compact system,predominantly constituted by few-gigahertz electronics and telecommunication components,shows enormous potential for practical spectroscopic applications.

关键词： frequency comb reconstructive wavemeter spectroscopy

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An intelligent active probing and trace-back scheme for IoT anomaly detection

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Digital Communications and networks 2024年第1期10卷 168-181页

作者： Luying Wang Lingyi Chen Neal N.Xiong Anfeng Liu Tian Wang Mianxiong Dong School of Computer Science and Engineering Central South UniversityChangsha 410083China department of Computer Science and Mathematics Sul Ross State UniversityAlpineTX 79830USA Artificial Intelligence and Future networks Beijing Normal University&UICZhuhai 519087China department of Information and Electronic Engineering Muroran Institute of TechnologyMuroran 050-8585Japan

Due to their simple hardware,sensor nodes in IoT are vulnerable to attack,leading to data routing blockages or malicious tampering,which significantly disrupts secure data collection.An Intelligent Active Probing and Trace-back Scheme for IoT Anomaly Detection(APTAD)is proposed to collect integrated IoT data by recruiting Mobile Edge Users(MEUs).(a)An intelligent unsupervised learning approach is used to identify anomalous data from the collected data by MEUs and help to identify anomalous nodes.(b)Recruit MEUs to trace back and propose a series of trust calculation methods to determine the trust of nodes.(c)The last,the number of active detection packets and detection paths are designed,so as to accurately identify the trust of nodes in IoT at the minimum cost of the network.A large number of experimental results show that the recruiting cost and average anomaly detection time are reduced by 6.5 times and 34.33%respectively,while the accuracy of trust identification is improved by 20%.

关键词： Anomaly detection Internet of things Integrating data collection Mobile edge users Intelligent