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arXiv 2023年

作者： Zhao, Jinzheng Xu, Yong Qian, Xinyuan Berghi, Davide Wu, Peipei Cui, Meng Sun, Jianyuan Jackson, Philip J.B. Wang, Wenwu Centre for Vision Speech and Signal Processing University of Surrey GuildfordGU2 7XH United Kingdom Tencent AI Lab BellevueWA98004 United States Department of Computer Science and Technology University of Science and Technology Beijing Beijing100083 China Department of Computer Science University of Sheffield SheffieldS1 4DP United Kingdom

Audio-visual speaker tracking has drawn increasing attention over the past few years due to its academic values and wide application. Audio and visual modalities can provide complementary information for localization and tracking. With audio and visual information, the Bayesian-based filter can solve the problem of data association, audio-visual fusion and track management. In this paper, we conduct a comprehensive overview of audio-visual speaker tracking. To our knowledge, this is the first extensive survey over the past five years. We introduce the family of Bayesian filters and summarize the methods for obtaining audio-visual measurements. In addition, the existing trackers and their performance on AV16.3 dataset are summarized. In the past few years, deep learning techniques have thrived, which also boosts the development of audio-visual speaker tracking. The influence of deep learning techniques in terms of measurement extraction and state estimation is also discussed. At last, we discuss the connections between audio-visual speaker tracking and other areas such as speech separation and distributed speaker tracking. © 2023, CC BY.

关键词： Face recognition

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Learning Fair and Efficient Multiple Access Schemes with Decomposed MADDPG

Learning Fair and Efficient Multiple Access Schemes with Dec...

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Future Communications and Networks (FCN), International Conference on

作者： Zhaoyang Liu Haoxin Lin Xijun Wang Jie Gong Xiang Chen School of Electronics and Information Technology Sun Yat-sen University Guangzhou China Guangxi Key Laboratory of Wireless Wideband Communication and Signal Processing Guilin China State Key Lab of Novel Software Technology Nanjing University Nanjing China Guangdong Key Laboratory of Information Security Technology School of Computer Science and Engineering Sun Yat-sen University Guangzhou China

ISBN: (数字)9798350396034

ISBN: (纸本)9798350396041

With the growing demand for wireless communication networks, achieving efficient and equitable channel access schemes has become paramount. In this paper, we delve into the realm of distributed channel access in homogeneous wireless networks, focusing on both fairness and efficiency. We introduce a media access control protocol, named Fair and Efficient Multiple Access (FEMA), by leveraging decomposed Multi-Agent Deep Deterministic Policy Gradient (MADDPG). FEMA decouples the dual objectives into self-oriented efficiency reward and team-oriented fairness reward. Correspondingly, local critic networks and global critic networks target self-oriented reward and team-oriented reward, respectively. Extensive simulations demonstrate FEMA’s superiority over conventional MAC protocols and other multiple access techniques based on deep reinforcement learning, showcasing its ability to simultaneously enhance fairness and efficiency.

关键词： Protocols Wireless networks Focusing Media Access Protocol Dynamic scheduling Deep reinforcement learning Resource management

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HL-DPoS: An Enhanced Anti-Long-Range Attack DPoS Algorithm

arXiv

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arXiv 2023年

作者： Li, Yang Xia, Chunhe Li, Chunyan Zhao, Yuan Chen, Chen Wang, Tianbo School of Computer Science and Engineering Beihang University Beijing China Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China

The consensus algorithm is crucial in blockchain for ensuring the validity and security of transactions across the decentralized network. However, achieving consensus among nodes and packaging blocks in blockchain networks is a complex task that requires efficient and secure consensus algorithms. The DPoS consensus algorithm has emerged as a popular choice due to its fast transaction processing and high throughput. Despite these advantages, the algorithm still suffers from weaknesses such as centralization and vulnerability to long-range attacks, which can compromise the integrity of the blockchain network. To combat these problems, we developed an Enhanced Anti-Long-Range Attack DPoS algorithm (HL-DPoS). First, we split nodes into pieces to reduce centralization issues while giving witness nodes the power to report and benefit from malicious node's reports, maintaining high efficiency and high security. Second, we propose a validation method in HL-DPoS that compares consensuses transactions with the longest chain to detect long-range attacks. Algorithm analysis and simulation experiment results demonstrate that our HL-DPoS consensus algorithm improves security while achieving better consensus performance. Copyright © 2023, The Authors. All rights reserved.

关键词： Blockchain

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AIDE: Attack Inference Based on Heterogeneous Dependency Graphs with MITRE ATT&CK

AIDE: Attack Inference Based on Heterogeneous Dependency Gra...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Chunhe Xia Nan Feng Xinyi Pan Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Software & Microelectronics Peking University Beijing China School of Cyber Science and Technology Beihang University Beijing China School of Computer Science and Engineering Guangxi Normal University Guilin China

ISBN: (数字)9798331506209

ISBN: (纸本)9798331506216

The MITRE ATT&CK Framework provides a rich and actionable repository of adversarial tactics, techniques, and procedures (TTPs). By leveraging the associations among these techniques, we can infer unobserved techniques based on those have been observed. Current machine learning inference methods based on small-scale data face the following problems: "logical dependencies missing" and "complex associations missing." The former means that current association methods rely solely on the statistical dependencies between attack techniques while ignoring the logical dependencies involved in executing those techniques. The latter indicates that current inference methods simplify the complex associations between technologies based on statistical assumptions, failing to adequately capture the long-range and intricate real-world relationships between techniques. To address these problems, we propose an Attack Inference model based on heterogeneous DEpendency graphs (AIDE). First, we extract three types of logical dependencies: CVE, Pattern, and Conditional, which allows us to construct a heterogeneous dependency graph among the techniques, thus addressing the "logical dependencies missing" problem. Second, we employ Relation Graph Convolution Neural Networks (RGCN) to aggregate heterogeneous dependencies into representation vectors of the techniques. Finally, we utilize the sequence inference model Decoder, which excels at capturing implicit contextual associations, to infer unknown attacks, thus addressing the "complex associations missing" problem. Experiments conducted on real datasets demonstrate that our model significantly outperforms state-of-the-art baseline models in both hits@k and map@k metrics.

关键词： Adaptation models Aggregates Refining Machine learning Vectors Threat assessment Real-time systems Decoding Security Context modeling

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FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protection Method for Federated Android Malware Classifier

FedDADP: A Privacy-Risk-Adaptive Differential Privacy Protec...

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International Joint Conference on Neural Networks (IJCNN)

作者： Changnan Jiang Chunhe Xia Mengyao Liu Rui Fang Pengfei Li Huacheng Li Tianbo Wang Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China SNationa Innovation Center of Intelligent and Connected Vehicles Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing China

ISBN: (数字)9798350359312

ISBN: (纸本)9798350359329

The federated Android malware classifier has attracted much attention owing to its advantages of privacy protection and multi-party joint modeling. However, the research indicates that the gradient transmitted within the federated classifier still encodes the user's sensitive information, exposing it to indirect privacy inference threats from curious servers. Differential privacy is a recognized and effective way to address this privacy breach threat by adding noise to the user's model parameters to limit the attacker's inference of sensitive information. However, the protection effect of existing differential privacy methods is at the cost of significantly reducing the model's classification accuracy, and it cannot be reasonably balanced. To address this challenge, we propose a privacy protection method, FedDADP. FedDADP performs adaptive, lightweight privacy configuration in its training time dimension and model space dimension according to the privacy risk distribution law in the federated Android malware classifier to protect users' privacy while maintaining the model's utility. Numerous experiments on the Androzoo dataset and multiple baseline classifiers show that FedDADP protects users' sensitive information better (7% more effectiveness against adversaries' inference) than baseline differential privacy methods and achieves better model utility (classification accuracy improves by about 8%) with the same privacy budget.

关键词： Training Privacy Adaptation models Differential privacy Accuracy Privacy breach Malware

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Epileptic Seizure Detection Using a Hybrid 1D CNN-Machine Learning Approach from EEG Data

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Journal of Healthcare Engineering 2022年第1期2022卷 9579422页

作者： Hassan, Fatima Hussain, Syed Fawad Qaisar, Saeed Mian Faculty of Computer Science and Engineering G. I. K. Institute Topi Pakistan School of Computer Science University of Birmingham Birmingham United Kingdom Electrical and Computer Engineering Department Effat University Jeddah22332 Saudi Arabia Communication and Signal Processing Lab Energy and Technology Research Center Effat University Jeddah22332 Saudi Arabia

Electroencephalography (EEG) is a widely used technique for the detection of epileptic seizures. It can be recorded in a noninvasive manner to present the electrical activity of the brain. The visual inspection of nonlinear and highly complex EEG signals is both costly and time-consuming. Therefore, an effective automatic detection system is needed to assist in the long-term evaluation and treatment of patients. Traditional approaches based on machine learning require feature extraction, while deep learning approaches are time-consuming and require more layers for effective feature learning and processing of complex EEG waveforms. Deep learning-based approaches also have weak generalization ability. This paper proposes a solution based on the combination of convolution neural networks (CNN) and machine learning classifiers. It preprocesses the EEG signal using the Butterworth filter and performs feature extraction using CNN. From the extracted set of features, the approach selects only the relevant features using mutual information-based estimators to reduce the curse of dimensionality and improve classification accuracy. The selected features are then passed as input to different machine learning classifiers. The suggested solution is evaluated on the University of Bonn dataset and CHB-MIT datasets. Our model effectively predicts 2, 3, 4, and 5 classes with accuracy of 100%, 99%, 94.6%, and 94%, respectively, for the Bonn dataset and 98% for CHB-MIT datasets. © 2022 Fatima Hassan et al.

关键词： Electroencephalography

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HF-Mid: A Hybrid Framework of Network Intrusion Detection for Multi-type and Imbalanced Data

HF-Mid: A Hybrid Framework of Network Intrusion Detection fo...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Weidong Zhou Tianbo Wang Guotao Huang Xiaopeng Liang Chunhe Xia Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China Guangxi Collaborative Innovation Center of Multi-source Information Integration and Intelligent Processing Guangxi Normal University Guilin China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The data-driven deep learning methods have brought significant progress and potential to intrusion detection. However, there are two thorny problems caused by the characteristics of intrusion data: "multi-type features" and "data imbalance". The former means that forcefully and improperly transforming intrusion features from distinct metric spaces can result in semantic loss and noise. The latter indicates that the intrusion data is imbalanced in quantity and quality due to its complex spatial distribution. We propose a Hybrid Framework for Multi-type and Imbalance Data (HF-Mid) to address the above two problems. Firstly, we divide the intrusion features into equivalent and non-equivalent groups, and then embed them sequentially using Supervised Paragraph Vector-Distributed Memory (SPV-DM), which excels at modeling co-occurrence relationships, and Deep Neural Network (DNN), which is suitable for modeling non-linear relationships, thereby solving the "multitype features" problem. Secondly, we adopt a low-noise collective matrix factorization (CMF) model to fuse the two obtained features for dimensionality reduction. Finally, we employ a multiple classifier to detect intrusion. During the classifier training stage, we design a genetic algorithm-based proportional sampling method to select high-quality samples in each training batch. thus addressing the "data imbalance" problem. The experimental results demonstrate the proposed framework exhibits an overall improvement of 5.9% and 1.5% in terms of accuracy and false positive rate on average, respectively.

关键词： Training Privacy Fuses Noise Semantics Artificial neural networks Feature extraction

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FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensitive Information Leakage in Federated Learning-based Android Malware Classifier

FedDLM: A Fine-Grained Assessment Scheme for Risk of Sensiti...

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IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom)

作者： Changnan Jiang Chunhe Xia Chen Chen Huacheng Li Tianbo Wang Xiaojian Li Beijing Key Lab. of Network Technology Beihang University Beijing China Guangxi Collaborative Innovation Center of Multi-Source Information Integration and Intelligent Processing Guangxi Normal University Guilin China School of Cyber Science and Technology Beihang University Beijing China Shanghai Key Laboratory of Computer Software Evaluating and Testing Shanghai China College of Computer Science and Information Technology Guangxi Normal University Guilin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

In the traditional centralized Android malware classification framework, privacy concerns arise as it requires collecting users’ app samples containing sensitive information directly. To address this problem, new classification frameworks based on Federated Learning (FL) have emerged for privacy preservation. However, research shows that these frameworks still face risks of indirect information leakage due to adversary inference. Unfortunately, existing research lacks an effective assessment of the extent and location of this leakage risk. To bridge the gap, we propose the FedDLM, which provides a fine-grained assessment of the risk of sensitive information leakage in an FL-based Android malware classifier. FedDLM estimates attackers’ theoretical maximum inference ability from the information theory perspective to gauge the degree of leakage risk in the classifier effectively. It precisely identifies critical positions in the shared gradient where the leakage risk exists by utilizing characteristics of class activation in classifiers. Through extensive experiments on the Androzoo dataset, FedDLM demonstrates its superior effectiveness and precision compared to baseline methods in evaluating the risk of sensitive information leakage. The evaluation results provide valuable insights into information leakage problems in classifiers and targeted privacy protection methods.

关键词： Meters Privacy Federated learning Information leakage Malware Security Risk management

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RIDNet Assisted cGAN Based Channel Estimation for One-Bit ADC mmWave MIMO Systems

RIDNet Assisted cGAN Based Channel Estimation for One-Bit AD...

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IEEE Conference on Vehicular technology (VTC)

作者： Erhan Karakoca Hasan Nayir Ali Görçin Khalid Qaraqe Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electronics and Communication Engineering Istanbul Technical University İstanbul Turkey Communications and Signal Processing Research (HİSAR) Lab. TÜBİTAK BİLGEM Kocaeli Turkey Department of Electronics and Communication Engineering Yildiz Technical University İstanbul Turkey

The estimation of millimeter-wave (mmWave) massive multiple input multiple output (MIMO) channels becomes compelling when one-bit analog-to-digital converters (ADCs) are utilized. Furthermore, as the number of antenna increases, pilot overhead scales up to provide consistent channel estimation, eventually degrading spectral efficiency. This study presents a channel estimation approach that combines a conditional generative adversarial network (cGAN) with a novel blind denoising network with a sparse feature attention mechanism. Performance analysis and simulations show that using a cGAN fused with a feature attention-based denoising neural network significantly enhances the channel estimation performance while requiring less pilot transmission.

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Channel Estimation Using RIDNet Assisted OMP for Hybrid-Field THz Massive MIMO Systems

Channel Estimation Using RIDNet Assisted OMP for Hybrid-Fiel...

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IEEE International Conference on Communications (ICC)

作者： Hasan Nayir Erhan Karakoca Ali Görçin Khalid Qaraqe Department of Electrical and Computer Engineering Texas A&M University at Qatar Doha Qatar Department of Electronics and Communication Engineering Istanbul Technical University İstanbul Turkey Communications and Signal Processing Research (HİSAR) Lab. TÜBİTAK BİLGEM Kocaeli Turkey Department of Electronics and Communication Engineering Yildiz Technical University İstanbul Turkey

The terahertz (THz) band radio access with larger availab.e bandwidth is anticipated to provide higher capacities for next-generation wireless communication systems. However, higher path loss at THz frequencies significantly limits the wireless communication range. Massive multiple-input multiple-output (mMIMO) is an attractive technology to increase the Rayleigh distance by generating higher gain beams using low wavelength and highly directive antenna array aperture. In addition, both far-field and near-field components of the antenna system should be considered for modeling THz electromagnetic propagation, where the channel estimation for this environment becomes a challenging task. This paper proposes a novel channel estimation method using a real image denoising network (RIDNet) and orthogonal matching pursuit (OMP) for hybrid-field THz mMIMO channels, including far-field and near-field constituents. The simulation experiments are performed using the ray-tracing tool. The results demonstrate that the proposed RIDNet-based method consistently provides lower channel estimation errors than the conventional OMP algorithm for all signal-to-noise ratio (SNR) regions. The performance gap becomes higher at low SNR regimes. Furthermore, the results imply that the same error performance of the OMP can be achieved by the RIDNet-based method using a lower number of RF chains and pilot symbols.

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