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检索条件"机构=Science and Technology on Communication Information Security Control Laboratory"

共 1436 条记录，以下是371-380 订阅

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Study on Fluctuation Characteristics of Acoustic Signals under Density Layered Fluid Disturbance

Study on Fluctuation Characteristics of Acoustic Signals und...

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IEEE/OES China Ocean Acoustics (COA)

作者： Jintao Yong Yunfei Chen Zhenshan Wang Bing Jia Rui Wang Shiqi Mo Science and Technology on Underwater Test and Control Laboratory Dalian China College of Underwater Acoustic Engineering Harbin Engineering University Harbin China College of Information and Communication Engineering Dalian University of Technology Dalian China

ISBN: (数字)9798350340297

ISBN: (纸本)9798350340303

This study investigates the fluctuation characteristics of acoustic signals in the presence of flow field perturbations within density-stratified fluids through laboratory simulation experiments. A density stratification tank was used to replicate the ocean's stratified environment. The SUBOFF scaled model was employed to mimic the underwater vehicle's navigation process in towing mode. controlled density stratification simulation experiments were conducted to explore the acoustic signal fluctuation characteristics within the wake flow field of the submerged model. Data on the undulation properties of acoustic signals that propagated through the wake flow field of the underwater vehicle were collected. The undulation characteristics of the acoustic signal at two towing speeds and various spatial positions were analyzed. In alignment with the typical marine environment characteristics, this study designed the stratification conditions, the scale of the scaled model, and the towing speed based on the Froude number (Fri) similarity criterion and the geometric similarity law. The analysis of the experimental results indicates that the fluctuating frequency of the acoustic signal amplitude diminishes over time as the wake dissipates post-target passage. Concurrently, the intensity of the high-frequency fluctuation component of the signal also decreases with the dissipation of the wake.

关键词： Time-frequency analysis Fluctuations Fluids Navigation Perturbation methods Oceans Attenuation Acoustics Underwater vehicles

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Coastal SEA Surface Salinity Retrieval Analysis from SMAP Mission using Machine Learning

Coastal SEA Surface Salinity Retrieval Analysis from SMAP Mi...

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IEEE International Symposium on Geoscience and Remote Sensing (IGARSS)

作者： YanFang Lv YiFan Zhang JingYi Liu LanJie Zhang Key Laboratory of Information and Communication Systems Ministry of Information Industry Beijing Information Science and Technology University Beijing China Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China Key Laboratory of Microwave Remote Sensing National Space Science Center Chinese Academy of Sciences Beijing China

Sea surface salinity (SSS) plays an essential role in the study of global climate change. Especially, high accuracy coastal SSS is of great significance for marine aquaculture and other aspects. SMAP is a high resolution satellite equipped with radars and radiometers for measuring soil moisture, which can also achieve observations of SSS. Based on SMAP L2B V5.0 data, to improve the coastal SSS retrieval accuracy, this paper proposes a method for retrieving SSS in coastal regions using deep neural networks (DNN) and analyzes the impact of different variables on retrieving SSS. By comparing the retrieval results of DNN with HYCOM SSS and in situ SSS, the root mean square errors (RMSE) of DNN SSS were 0.36 psu and 0.45 psu, respectively. Compared to SMAP SSS products from physical retrieval algorithm, the RMSE of DNN SSS decreased by 1.38 psu and 1.23 psu, respectively. The results indicate that using DNN to retrieve SSS in coastal regions can achieve high accuracy.

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An Enhanced Neuron Attribution-Based Attack Via Pixel Dropping

An Enhanced Neuron Attribution-Based Attack Via Pixel Droppi...

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IEEE International Conference on Image Processing

作者： Zhi Lin Anjie Peng Hui Zeng Kaijun Wu Wenxin Yu Southwest University of Science and Technology Sichuan MY China Engineering Research Center of Digital Forensics Ministry of Education Nanjing University of Information Science & Technology Jiangsu NJ China Science and Technology on Communication Security Laboratory Sichuan CD China

Convolutional neural networks (CNNs) are vulnerable to adversarial examples (AEs). Existing feature-level attacks explore the neuron importance to distort the intrinsic object-aware features which are shareable among different CNNs, thus achieving great performance in transferability. In this work, we propose an enhanced neuron attribution-based attack via pixel dropping (ENAA) and try to increase the number of positive neurons to distort the object-aware features more fully than NAA. Specifically, when computing neuron attribution, we use a pixel dropping scheme to expand the regions where the source model pays attention to the image. Our ENAA can make the target model shift the attention regions of AEs far away from those of clean images. Experimental results validate that the proposed method outperforms the state-of-the-art feature-level attacks both in white-box and black-box settings.

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An Adaptive Power Allocation Scheme for Mini-UAV Tracking with Distributed Radar in Dense Urban Environment 10

An Adaptive Power Allocation Scheme for Mini-UAV Tracking wi...

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10th International Conference on control, Automation and information sciences, ICCAIS 2021

作者： Cao, Ziyi Yuan, Ye Yi, Wei Chen, Linxiu University of Electronic Science and Technology of China School of Information and Communication Engineering Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665440295

In this paper, a new power allocation scheme of the distributed radar system is proposed for mini-UAV tracking tasks in urban environments, considering the influence of the building occlusion on the probability of detection. Firstly, the probability of detection of each radar node is derived according to the estimated position of the target and the visibility of each radar node. Then, according to the probability of detection, the adaptive cost function concerning the transmit power and tracking accuracy is designed and used to the formulation of the optimization problem. Simulation results show that the proposed power allocation scheme can improve the target tracking accuracy in urban environments compared to the conventional method. © 2021 IEEE.

关键词： Cost functions

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Distributed Multi-Sensor Multi-View Fusion of PHD Filter for Maneuvering Targets

Distributed Multi-Sensor Multi-View Fusion of PHD Filter for...

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2021 CIE International Conference on Radar, Radar 2021

作者： Peng, Cong Chai, Lei Yi, Wei Chen, Linxiu School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing China

ISBN: (纸本)9781665498142

This paper addresses the problem of maneuvering multi-target tracking by a network of sensors having different and limited fields of view (FoV s). Each local sensor runs the Gaussian Mixture Probability Hypothetical Density (GMPHD) filter. Due to the target maneuver, based on a single motion model, severe tracking performance degradation can be observed. We propose to use multi-model (MM) method to realize the adaptation to motion characteristic so as to overcome maneuverability of targets. Then considering FoV s of sensors in the network are different and limited, the standard weighted arithmetic average (WAA) fusion is no longer applicable and leads to an underestimation of the target number. Therefore, we use state-dependent WAA (SD-WAA) fusion rule, which performs the WAA in a more robust way by calculating a set of state-related fusion weights. Numerical experiment is designed to demonstrate the efficacy of the proposed method. © 2021 IEEE.

关键词： fields of view gaussian mixture maneuvering multi-target tracking multi-model method probability hypothesis density filter state-dependent weighted arithmetic average

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MISA: Unveiling the Vulnerabilities in Split Federated Learning

MISA: Unveiling the Vulnerabilities in Split Federated Learn...

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International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wei Wan Yuxuan Ning Shengshan Hu Lulu Xue Minghui Li Leo Yu Zhang Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University Cluster and Grid Computing Lab

Federated learning (FL) and split learning (SL) are prevailing distributed paradigms in recent years. They both enable shared global model training while keeping data localized on users’ devices. The former excels in parallel execution capabilities, while the latter enjoys low dependence on edge computing resources and strong privacy protection. Split federated learning (SFL) combines the strengths of both FL and SL, making it one of the most popular distributed architectures. Furthermore, a recent study has claimed that SFL exhibits robustness against poisoning attacks, with a fivefold improvement compared to FL in terms of *** this paper, we present a novel poisoning attack known as $\color{Fuchsia} {{\text{MISA}}}$. It poisons both the top and bottom models, causing a misalignment in the global model, ultimately leading to a drastic accuracy collapse. This attack unveils the vulnerabilities in SFL, challenging the conventional belief that SFL is robust against poisoning attacks. Extensive experiments demonstrate that our proposed MISA poses a significant threat to the availability of SFL, underscoring the imperative for academia and industry to accord this matter due attention.

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Securely Fine-tuning Pre-trained Encoders Against Adversarial Examples

Securely Fine-tuning Pre-trained Encoders Against Adversaria...

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IEEE Symposium on security and Privacy

作者： Ziqi Zhou Minghui Li Wei Liu Shengshan Hu Yechao Zhang Wei Wan Lulu Xue Leo Yu Zhang Dezhong Yao Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology School of Software Engineering Huazhong University of Science and Technology Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

With the evolution of self-supervised learning, the pre-training paradigm has emerged as a predominant solution within the deep learning landscape. Model providers furnish pre-trained encoders designed to function as versatile feature extractors, enabling downstream users to harness the benefits of expansive models with minimal effort through fine-tuning. Nevertheless, recent works have exposed a vulnerability in pre-trained encoders, highlighting their susceptibility to downstream-agnostic adversarial examples (DAEs) meticulously crafted by attackers. The lingering question pertains to the feasibility of fortifying the robustness of downstream models against DAEs, particularly in scenarios where the pre-trained encoders are publicly accessible to the *** this paper, we initially delve into existing defensive mechanisms against adversarial examples within the pre-training paradigm. Our findings reveal that the failure of current defenses stems from the domain shift between pre-training data and downstream tasks, as well as the sensitivity of encoder parameters. In response to these challenges, we propose Genetic Evolution-Nurtured Adversarial Fine-tuning (Gen-AF), a two-stage adversarial fine-tuning approach aimed at enhancing the robustness of downstream models. Gen-AF employs a genetic-directed dual-track adversarial fine-tuning strategy in its first stage to effectively inherit the pre-trained encoder. This involves optimizing the pre-trained encoder and classifier separately while incorporating genetic regularization to preserve the model’s topology. In the second stage, Gen-AF assesses the robust sensitivity of each layer and creates a dictionary, based on which the top-k robust redundant layers are selected with the remaining layers held fixed. Upon this foundation, we conduct evolutionary adaptability fine-tuning to further enhance the model’s generalizability. Our extensive experiments, conducted across ten self-supervised training methods and six d

关键词： Training Adaptation models Sensitivity Accuracy Self-supervised learning Genetics Feature extraction

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Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

Why Does Little Robustness Help? A Further Step Towards Unde...

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IEEE Symposium on security and Privacy

作者： Yechao Zhang Shengshan Hu Leo Yu Zhang Junyu Shi Minghui Li Xiaogeng Liu Wei Wan Hai Jin National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Engineering Research Center on Big Data Security Hubei Key Laboratory of Distributed System Security School of Cyber Science and Engineering Huazhong University of Science and Technology School of Information and Communication Technology Griffith University School of Software Engineering Huazhong University of Science and Technology Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology

ISBN: (数字)9798350331301

ISBN: (纸本)9798350331318

Adversarial examples for deep neural networks (DNNs) are transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical *** this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient *** on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifests in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other, and emphasize the

关键词： Training Privacy Buildings Closed box Minimization Data augmentation Robustness

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Optimizing multi-input scheduling for fuzzing test using Bayesian algorithm weight optimization

Optimizing multi-input scheduling for fuzzing test using Bay...

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10th International Symposium on Test Automation & Instrumentation (ISTAI 2024)

作者： Junzhe Fang Liyong Wang Haojie Ji Teng Guo Te Hu Key Laboratory of Modern Measurement and Control Technology Ministry of Education Beijing Information Science and Technology University Beijing People's Republic of China Zaozhuang Network Security Guarantee Center Zaozhuang People's Republic of China

ISBN: (数字)9781837241910

As a major software testing method, fuzzing has the potential to enhance testing coverage and detect a great number of software vulnerabilities in complex code programs. The current fuzzy testing for multiple input parameters lacks an effective calling mechanism and treats all variant parameter equally, resulting in low efficiency. This paper presents a fuzzy testing method based on Bayesian algorithm for scheduling parameters. Firstly, it combines the characteristics of input parameters to match the appropriate variation strategy. Secondly, it utlizes the Bayesian algorithm to automatically generate weight values for the objective function index during sufficient testing. It evaluates test cases with multi-parameter inputs according to these weights and selects parameters with high utilization value for multiple tests. Finally, in order to evaluate the effectiveness of the method of this paper, an application programming interface of Amercian fuzzy lop (AFL-API) is developed based on the typical fuzzy testing tool AFL++, and compared with AFL++ as a fuzzy testing tool. The experimental results demonstate that when using the AFL-API for program testing, there was 47% increase in coverage rate, 52% increase in the number of discovered crashes, and more program vulnerabilities were identified during actual testing.

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Joint Optimization of Uplink Spectrum Efficiency and UAV Trajectory in Cell-Free UAV Networks

Joint Optimization of Uplink Spectrum Efficiency and UAV Tra...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social Computing (CPSCom) Green Computing and communications (GreenCom)

作者： Jian He Chunyu Pan Qian Chen The Key Laboratory of Information and Communication Systems Ministry of Information Industry and the Key Laboratory of Modern Measurement & Control Technology Ministry of Education Beijing Information Science and Technology University Beijing China School of Architecture and Transportation Engineering Guilin University of Electronic Technology Guilin China

The flexible mobility characteristics of unmanned aerial vehicle (UAV) lead to frequent handover and inter cell interference issues in traditional cellular based UAV assisted communication systems. Establishing cell-free UAV (CF-UAV) networks without cell boundaries will effectively alleviate this problem and has been an important research. However, the movement of the UAV results in time-varying channels. Therefore, the joint dynamic and reasonable UAV trajectory optimization and power allocation are very important for in CF-UAV networks. The expression of pilot transmission, channel estimation, uplink transmission spectrum efficiency (SE) have been derived based on the assumption of Rician channel between UAVs and access point (AP). SE optimization algorithm for CF-UAV networks combined with the Angle search and multi-UAV trajectory optimization strategy has been proposed in this paper. Simulation results show that the proposed algorithm performs better performance than comparison schemes, which SE improves by 37% compared with Angle search.

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