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Machine-learning-based detection for quantum hacking attacks on continuous-variable quantum-key-distribution systems

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Physical Review A 2023年第6期107卷 062422-062422页

作者： Chao Ding Shi Wang Yaonan Wang Zijie Wu Jingtao Sun Yiyu Mao College of Electrical and Information Engineering Hunan University Changsha 410082 China National Engineering Research Center of Robot Visual Perception and Control Technology Hunan University Changsha 410082 China School of Computer and Communication Engineering Changsha University of Science and Technology Changsha 410114 China

Continuous-variable quantum key distribution (CVQKD) is a mature technology that can theoretically provide an unconditional security guarantee. However, a practical CVQKD system may be vulnerable to various quantum hacking attacks due to imperfect devices and insufficient assumptions. In this paper, we propose a universal defense strategy called a machine-learning-based attack detection scheme (MADS). Leveraging the combined advantages of density-based spatial clustering of applications with noise (DBSCAN) and multiclass support vector machines (MCSVMs), MADS demonstrates remarkable effectiveness in detecting quantum hacking attacks. Specifically, we first establish a set of attack-related features to extract feature vectors. These vectors are then utilized as input data for DBSCAN to identify and remove any noise or outliers. Finally, the trained MCSVMs are employed to classify and predict the processed data. The predicted results can immediately determine whether or not to generate a final secret key. Simulation results show that the proposed MADS can efficiently detect most quantum hacking attacks and revise the overestimated secret key rates caused by a CVQKD system without any defense strategy to obtain a tighter security bound.

关键词： Quantum cryptography

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CLIENT2VEC: IMPROVING FEDERATED LEARNING BY DISTRIBUTION SHIFTS AWARE CLIENT INDEXING

arXiv

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arXiv 2024年

作者： Guo, Yongxin Wang, Lin Tang, Xiaoying Lin, Tao School of Science and Engineering The Chinese University of Hong Kong Guangdong Shenzhen518172 China The Shenzhen Institute of Artificial Intelligence and Robotics for Society China The Guangdong Provincial Key Laboratory of Future Networks of Intelligence China Research Center for Industries of the Future Westlake University China School of Engineering Westlake University China

Federated Learning (FL) is a privacy-preserving distributed machine learning paradigm. Nonetheless, the substantial distribution shifts among clients pose a considerable challenge to the performance of current FL algorithms. To mitigate this challenge, various methods have been proposed to enhance the FL training process. This paper endeavors to tackle the issue of data heterogeneity from another perspective-by improving FL algorithms prior to the actual training stage. Specifically, we introduce the Client2Vec mechanism, which generates a unique client index for each client before the commencement of FL training. Subsequently, we leverage the generated client index to enhance the subsequent FL training process. To demonstrate the effectiveness of the proposed Client2Vec method, we conduct three case studies that assess the impact of the client index on the FL training process. These case studies encompass enhanced client sampling, model aggregation, and local training. Extensive experiments conducted on diverse datasets and model architectures show the efficacy of Client2Vec across all three case studies. Our code is avaliable at https://***/LINs-lab/client2vec. © 2024, CC BY.

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Integrated Parallel Chaos driven LiDAR System with High Interference Tolerance

Integrated Parallel Chaos driven LiDAR System with High Inte...

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2023 Conference on Lasers and Electro-Optics, CLEO 2023

作者： Chen, Ruixuan Shen, Bitao Shu, Haowen Chang, Lin Xie, Weiqiang Liao, Wenchao Tao, Zihan Bowers, John Wang, Xingjun State Key Laboratory of Advanced Optical Communications System and Networks School of Electronics Peking University Beijing100871 China Department of Electrical and Computer Engineering University of California Santa BarbaraCA93106 United States Frontiers Science Center for Nano-Optoelectronics Peking University Beijing100871 China

We introduce a chaotic microcomb based high interference-tolerance parallel LiDAR. Parallel chaos with record-high noise spectra beyond 7 GHz leads to sub-cm-level ranging accuracy. The intrinsic orthogonality permits... 详细信息

ISBN: (纸本)9781957171258

关键词： Optical radar

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SumPA: Efficient Pattern-Centric Graph Mining with Pattern Abstraction 21

SumPA: Efficient Pattern-Centric Graph Mining with Pattern A...

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Proceedings of the 30th International Conference on Parallel Architectures and Compilation Techniques

作者： Chuangyi Gui Xiaofei Liao Long Zheng Pengcheng Yao Qinggang Wang Hai Jin National Engineering Research Center for Big Data Technology and System/Service Computing Technology and System Lab/Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

ISBN: (纸本)9781665442787

Graph mining aims to explore interesting structural information of a graph. Pattern-centric systems typically transform a generic-purpose graph mining problem into a series of subgraph matching problems for high performance. Existing pattern-centric mining systems reduce the substantial search space towards a single pattern by exploring a highly-optimized matching order, but inherent computational redundancies of such a matching order itself still suffer severely, leading to significant performance degradation. The key innovation of this work lies in a general redundancy criterion that characterizes computational redundancies arising in not only handing a single pattern but also matching multiple patterns simultaneously. In this paper, we present SumPA, a high-performance pattern-centric graph mining system that can sufficiently remove redundant computations for any complex graph mining problems. SumPA features three key designs: (1) a pattern abstraction technique that can simplify numerous complex patterns into a few simple abstract patterns based on pattern similarity, (2) abstraction-guided pattern matching that completely eliminates (totally and partially) redundant computations during subgraph enumeration, and (3) a suite of system optimizations to maximize storage and computation efficiency. Our evaluation on a wide variety of real-world graphs shows that SumPA outperforms the two state-of-the-art systems Peregrine and GraphPi by up to 61.89× and 8.94×, respectively. For many mining problems on large graphs, Peregrine takes hours or even days while SumPA finishes in only a few minutes.

关键词： data reuse

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AdvMap: Crafting Adversarial Maps to Counter AI Aimbot in First-Person Shooter Games

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IEEE Transactions on Games 2025年

作者： Yan, Kai Chen, Xianyi Cui, Qi Yuan, Haoqin Tan, Zhenshan Nanjing University of Information Science and Technology Engineering Research Center of Digital Forensics Ministry of Education the School of Computer Science Nanjing210044 China Beijing Jiaotong University Key Laboratory of Transport Industry of Big Data Application Technologies for Comprehensive Transport Beijing100044 China

AI-based automatic aiming cheats (a.k.a., AI aimbots) have proliferated in first-person shooter (FPS) games, which grant malicious users an unfair gameplay advantage. Since AI aimbots operate independently of game data and are developed using object detection algorithms, they are difficult to detect with traditional anti-cheating methods. To actively counter AI aimbots, we propose AdvMap, which introduces invisible adversarial perturbations into game scene elements. In optimizing these adversarial perturbations, we design a mixture-of-misleading loss function that increases the total target confidence score within each misleading bounding box. It mitigates the risk of segment missing even when AdvMap is obscured, thereby enhancing the robustness. Besides, an L1-norm constraint with a small scale is employed during each update of the adversarial perturbations, which preserves the fidelity of the game scene. Additionally, to enable effective adaptation to interact with various elements within game environments, we introduce an Image-Subspace-Based Multi-Directional (ISMD) optimization strategy. It enables the adversarial perturbations to adaptively fit into each element by leveraging the mapping relationship between the game's 3D scenes and its corresponding 2D images. Furthermore, we construct a comprehensive benchmark, which includes various FPS games with different graphics styles and perspectives. Extensive experimental results demonstrate the efficacy of our method in countering various AI aimbot tools on different state-of-the-art object detection methods. © 2021 IEEE.

关键词： Adversarial machine learning

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Visual Exploration-Enhanced Quadruped Robot with Active-Passive Composite Telescope Mechanism

Visual Exploration-Enhanced Quadruped Robot with Active-Pass...

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Intelligent robotics and Automatic Control (IRAC), International Conference on

作者： Xuchen Wang Shengzhi Wang Yiying Liang Jiajun An Puchen Zhu Weibing Li Jianshu Zhou Xin Ma Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Hong Kong China Division of Advanced Manufacturing Shenzhen International Graduate School Tsinghua University Shenzhen China Department of Agriculture and Biological Engineering Purdue University West Lafayette USA School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Department of Mechanical Engineering University of California Berkeley USA Multi-Scale Medical Robotics Center Hong Kong China

ISBN: (数字)9798350389807

ISBN: (纸本)9798350389814

Navigating hazardous environments and confined spaces during disaster search and rescue operations poses significant challenges to both human rescuers and conventional robotic systems. Recognizing the need for enhanced capabilities, we introduce a Visual Exploration-Enhanced Quadruped Robot (VEQR), a novel platform specifically designed to improve adaptability and exploration in such demanding scenarios. The VEQR combines a quadruped robot with an innovative active-passive composite telescopic mechanism—a compact, 12.8 mm diameter device equipped with a camera—that enables maneuvering through narrow and curved spaces like slits and debris-filled areas. The bending and insertion/retraction capabilities of this mechanism improve the dexterity of the robot in confined spaces and extend its reach into deeper, hard-to-access zones. Experimental results demonstrate the VEQR’s proficiency in overcoming obstacles, inspecting confined spaces, and providing clear visual feedback. Notably, the VEQR successfully traversed narrow and winding passages as small as 50 mm diameter and inspected targets positioned up to 0.6 mm deep within confined spaces, confirming its effectiveness as a robust robot for search and rescue operations in hazardous environments.

关键词： Visualization Navigation Disasters Windings Telescopes Bending Inspection Robustness Quadrupedal robots

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Colon Cancer Tissue Classification Using ML

Colon Cancer Tissue Classification Using ML

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International Conference on Information Systems and computer Networks (ISCON)

作者： Ashish Tripathi Kuldeep Kumar Anuradha Misra Brijesh Kumar Chaurasia Department of Computer Science and Engineering Amity School of Engineering & Technology Amity University Lucknow Lucknow Uttar Pradesh India Center for Data Analytics Bond Business School Bond University Gold Coast Queensland Australia Department of Computer Science and Engineering Pranveer Singh Institute of Technology Kanpur Uttar Pradesh India

In this paper, the classification of colon cancer tissues by means of machine learning approaches is evaluated. In today’s world, a revolutionary advancement has come in the classification and diagnosis of diseases in the medical and healthcare sectors. Deep learning classifiers and machine learning methods are now broadly applied to accurately diagnose a number of diseases. Cancer is one of the world’s most significant roots of death, appealing to the lives of one person out of every six. As per the national library of medicine, the third leading cause of death worldwide is colorectal cancer. Identifying an illness at a premature stage increases the chances of survival. Automated diagnosis and the classification of tissues from images can be completed much more quickly with the use of artificial intelligence. A publicly available IoT dataset CRC–VAL–HE–7K consisting of 7180 images, distributed among nine types of colorectal tissues: background, lymphocytes, adipose, mucus, colorectal adenocarcinoma epithelium, normal colon mucosa, debris, cancer-associated stroma, and, smooth muscle is used after preprocessing. Feature extraction is done by applying Differential-Box-Count on all blocks of images. The dataset is evaluated by these Machine Learning (ML) procedures: K-Nearest Neighbor, Support Vector Machine, Decision Tree, Random Forest, Extreme Gradient Boosting, and Gaussian Naive Bayes. Results show that the performance of Extreme Gradient Boosting is the best and most viable approach.

关键词： Deep learning Support vector machines Machine learning algorithms Transfer learning Feature extraction Boosting Colon

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Slow electron-phonon relaxation controls the dynamics of the superconducting resistive transition

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Physical Review B 2024年第10期110卷 104519页

作者： E. M. Baeva A. I. Kolbatova N. A. Titova S. Saha A. Boltasseva S. Bogdanov V. M. Shalaev A. V. Semenov A. Levchenko G. N. Goltsman V. S. Khrapai Moscow 119435 Russian Federation 20 Myasnitskaya Street Moscow 101000 Russian Federation Laboratory of Photonic Gas Sensors University of Science and Technology MISIS Moscow 119049 Russian Federation Birck Nanotechnology Center and Elmore Family School of Electrical and Computer Engineering Department of Electrical and Computer Engineering Holonyak Micro and Nanotechnology Laboratory Illinois Quantum Information Science and Technology Center Moscow Institute of Physics and Technology Dolgoprudny 141701 Russian Federation Department of Physics Moscow 121205 Russian Federation Osipyan

We investigate the temporal and spatial scales of resistance fluctuations (R fluctuations) at the superconducting resistive transition accessed through voltage fluctuation measurements in thin epitaxial TiN films. This material is characterized by slow electron-phonon relaxation, which puts it far beyond the applicability range of the textbook scenario of superconducting fluctuations. The measured Lorentzian spectrum of the R fluctuations identifies their correlation time, which is nearly constant across the transition region and has no relation to the conventional Ginzburg-Landau timescale. Instead, the correlation time coincides with the energy relaxation time determined by a combination of the electron-phonon relaxation and the relaxation via diffusion into reservoirs. Our data are quantitatively consistent with the model of spontaneous temperature fluctuations and highlight the lack of understanding of the resistive transition in materials with slow electron-phonon relaxation.

关键词： Critical phenomena Fluctuations & noise Phonons Superconducting fluctuations Superconducting phase transition Low-temperature superconductors Thin films Resistivity measurements

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Performance of Hybrid Satellite-Terrestrial Relay Systems Using Non-Orthogonal Multiple Access 6

Performance of Hybrid Satellite-Terrestrial Relay Systems Us...

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6th International Conference on Communication, Image and Signal Processings, CCISP 2021

作者： Chen, Xifan Zhou, Zhejian Jin, Yifeng Li, Xunan Lv, Guocheng School of Electronics Engineering and Computer Science Peking University Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China

ISBN: (纸本)9781665432795

The performance of a hybrid satellite-terrestrial relay system using non-orthogonal multiple access (HSTRS-NOMA) is studied in this paper. In the satellite communication scenario considered in this paper, a satellite transmits signals to a terrestrial user under the help of a terrestrial decode-and-forward relay. We derive the achievable average rate and the outage probability of the system under independent Shadowed-Rician satellite channels and Nakagami fading terrestrial channels. Furthermore, the asymptotic outage probability of the system in the case of high signal-to-noise ratio (SNR) is proposed. Finally, we present the simulation results to verify the theoretical results. © 2021 IEEE.

关键词： Satellite communication systems

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A Simplified Student Network with Multi-teacher Feature Fusion for Industrial Defect Detection 1

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7th Asian Conference on Pattern Recognition, ACPR 2023

作者： Pei, Mingjing Liu, Ningzhong College of Computer Science and Technology Nanjing University of Aeronautics and Astronautics Nanjing China School of Electronics and Information Engineering West Anhui University Lu’an China MIIT Key Laboratory of Pattern Analysis and Machine Intelligence Nanjing China Collaborative Innovation Center of Novel Software Technology and Industrialization Nanjing China

ISBN: (数字)9783031476341

ISBN: (纸本)9783031476334

Improved industrial defect detection is deemed critical for ensuring high-quality manufacturing processes. Despite the effectiveness of knowledge distillation in detecting defects, there are still challenges in extracting useful features and designing a better student network. In this study, a new framework based on knowledge distillation is proposed to address these issues. To avoid overfitting and balance anomaly detection between image and pixel levels, a simplified student decoding network is designed. To extract more diverse features, a multi-teacher network is used in the teacher network. Simple addition and concatenation operations for feature maps were not sufficient, the method fuses features iteratively using attention mechanisms utilized for the multi-teacher networks. The approach is evaluated on two benchmark datasets for industrial defect detection, Mvtec and BTAD, and significantly improved performance is achieved compared to the other methods. An average accuracy of 99.22% and 97.46% for image-level and pixel-level ROC-AUC, respectively, is achieved on the Mvtec dataset, and 93.47% and 96.3% on the BTAD dataset. The proposed framework shows effectiveness in detecting defects in industrial settings, as demonstrated by the results. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Distillation

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