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1st International Conference of Advanced Computing and Informatics, ICACIN 2020

作者： Errais, Mohammed Bellafkih, Mostafa Al Sarem, Mohammed Research and Computer Innovation Laboratory Hassan II University of Casablanca Casablanca Morocco Network Laboratory INPT Rabat Morocco College of Computer Science and Engineering Taibah University Medina Saudi Arabia

ISBN: (纸本)9789811560477

network virtualization is an emerging concept that aims to facilitate the integration of new technologies regardless physical layer. To do this, the new concept proposes to evolve the current Internet business model. The new business model divides the Internet access provider to several actors. This actors diversity poses several challenges, especially when allocating resources and monitoring. For this, we propose in this work a new approach based on the enhanced telecom operation management business process framework. An approach that aims at ensuring an automatic supply of resources and a real-time monitoring based on the SLA defines between different actors. © 2021, The Editor(s) (if applicable) and The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Quality of service

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Privacy-Preserving Interactive Semantic Codec Training for IoT-Based Holographic Counterparts

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IEEE Transactions on Consumer Electronics 2025年

作者： Xu, Jinpeng Chen, Liang Lin, Limei Wang, Xiaoding Huang, Yanze Xu, Li Piran, Md. Jalil Fujian Normal University College of Computer and Cyber Security Fujian Provincial Key Laboratory of Network Security and Cryptology Fujian Fuzhou350117 China Fujian University of Technology School of Computer Science and Mathematics Fujian Fuzhou350118 China Sejong University Department of Computer Science and Engineering Seoul05006 Korea Republic of

The use of IoT-based semantic codecs to process complex contextual semantic information in holographic counterparts introduces significant privacy risks, as it may expose sensitive data, thereby increasing the likelihood of privacy disclosures. The diverse and dynamic nature of holographic counterparts in IoT environments exacerbates these challenges, making it more difficult for semantic codecs to effectively safeguard data privacy. This complexity further intensifies the need for privacy-preserving computation methods, as ensuring the confidentiality and security of the data processed by these codecs becomes a critical concern. However, current privacy protection strategy for multi-party training of semantic codecs relies heavily on the central server for gradient calculation, which may lead to gradient leakage issue. To address this issue, we propose PIMSeC (Privacy-Preserving Interactive Multi-Party Semantic Codec Training for IoT-Based Holographic Counterparts), a novel encryption-based technique that facilitates secure and efficient multi-party interactive training without the dependence on the central server, which enhances both data security and privacy resilience. PIMSeC not only proposes a full interactive secure multi-party deep learning model to protect data privacy during multi-party interactive training, but also, within the above deep learning model, establishes an encrypted additive gradient noise mechanism to ensure post-training semantic codec data privacy. Our theoretical analysis and experimental results demonstrate that PIMSeC promotes semantic codecs privacy protection effectively by interactive secure multi-party training. Compared to the state-of-art methods, PIMSeC achieves a 3% to 15% improvement in terms of accuracy, precision, F1-score, and recall at lower compression rates. © 1975-2011 IEEE.

关键词： Deep learning

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An improved Schnorr-based multi-signature scheme with application to blockchain 3

An improved Schnorr-based multi-signature scheme with applic...

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3rd IEEE International Conference on Civil Aviation Safety and Information Technology, ICCASIT 2021

作者： Li, Chunlan Wu, Yulong Yu, Feng Computer Science and Engineering School of Software Guangxi Normal University Guilin China Key Laboratory of Computer Network and Information Integration Southeast University Nanjing China

ISBN: (纸本)9781665425186

A multi-signature scheme allows a group of signers to jointly generate a signature on a common message, which can be verified by anyone, making the signature more compact. Multi-signature has the advantage of fast verification, and it helps reduce bandwidth and the cost of validation and storage. Recently, the multi-signature has drawn much attention because its advantages are desirable for blockchain and cryptocurrency. However, the traditional Schnorr-based multisignature scheme suffers from an excessive number of communications. This paper proposes an improved Schnorr-based multi-signature scheme ASMS, which reduces one round of communication in a traditional scheme, supporting public key aggregation. Thus, it is suitable for e-business and e-government affairs scenarios. As an application, we implemented our solution in Fabric, an enterprise blockchain platform, using chaincode technology. © 2021 IEEE

关键词： Blockchain

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MEAT: Median-Ensemble Adversarial Training for Improving Robustness and Generalization

MEAT: Median-Ensemble Adversarial Training for Improving Rob...

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

作者： Zhaozhe Hu Jia-Li Yin Bin Chen Luojun Lin Bo-Hao Chen Ximeng Liu Fujian Province Key Laboratory of Information Security and Network Systems Fuzhou China College of Computer Science and Big Data Fuzhou University Fuzhou China Department of Computer Science and Engineering Yuan Ze University Taiwan

Self-ensemble adversarial training methods improve model robustness by ensembling models at different training epochs, such as model weight averaging (WA). However, previous research has shown that self-ensemble defense methods in adversarial training (AT) still suffer from robust overfitting, which severely affects the generalization performance. Empirically, in the late phases of training, the AT becomes more overfitting to the extent that the individuals for weight averaging also suffer from overfitting and produce anomalous weight values, which causes the self-ensemble model to continue to undergo robust overfitting due to the failure in removing the weight anomalies. To solve this problem, we aim to tackle the influence of outliers in the weight space in this work and propose an easy-to-operate and effective Median-Ensemble Adversarial Training (MEAT) method to solve the robust overfitting phenomenon existing in self-ensemble defense from the source by searching for the median of the historical model weights. Experimental results show that MEAT achieves the best robustness against the powerful AutoAttack and can effectively allievate the robust overfitting. We further demonstrate that most defense methods can improve robust generalization and robustness by combining with MEAT.

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Enhancing Unknown Encrypted Traffic Clustering with Self-Supervised Learning

Enhancing Unknown Encrypted Traffic Clustering with Self-Sup...

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IEEE Conference on Global Communications (GLOBECOM)

作者： Ke Ding Xia Zhou Xiaoyan Hu Guang Cheng Ruidong Li Hua Wu School of Cyber Science & Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Jiangsu Province Engineering Research Center of Security for Ubiquitous Network Nanjing China Ministry of Education Key Laboratory of Computer Network and Information Integration (Southeast University) China Institute of Science and Engineering Kanazawa University Kanazawa Japan

ISBN: (数字)9798350351255

ISBN: (纸本)9798350351262

Many malicious attacks are launched through encrypted traffic from unknown proprietary network protocols. Timely identification of such malicious unknown encrypted traffic is essential for the defense. However, it is challenging to acquire labels for unknown protocols in the context of encrypted traffic. Due to the lack of prior knowledge, unsupervised learning is adopted to cluster unknown encrypted traffic. The existing unsupervised encrypted traffic clustering methods do not customize feature extraction and representation of unknown encrypted traffic, resulting in imperfect clustering results. This work innovatively proposes BiFR-SSL to accurately cluster unknown encrypted traffic without prior knowledge. BiFRSSL extracts features of each unknown encrypted bidirectional network flow based on the lengths, arrival time, and directions of packets within the flow to construct a Bidirectional Flowpic Representation (BiFR). Subsequently, it exploits Self-Supervised Learning (SSL) to pre-train a feature extractor that produces feature representations from BiFRs, guaranteeing the closeness of encrypted traffic flows from the same protocol in the representation space. Finally, it clusters unknown encrypted traffic based on their feature representations generated by the pre-trained feature extractor. Our experimental studies demonstrate that BiFR-SSL can effectively cluster encrypted traffic of unknown protocols and outperforms state-of-the-art methods.

关键词： Protocols Clustering methods Clustering algorithms Self-supervised learning Telecommunication traffic Feature extraction Data augmentation Cryptography Global communication Unsupervised learning

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Proposal Feature Learning Using Proposal Relations for Weakly Supervised Object Detection

Proposal Feature Learning Using Proposal Relations for Weakl...

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IEEE International Conference on Multimedia and Expo (ICME)

作者： Zhaofei Wang Weijia Zhang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China Key Laboratory of Computer Network and Information Integration Southeast University Ministry of Education China School of Information and Physical Sciences The University of Newcastle NSW Australia

ISBN: (数字)9798350390155

ISBN: (纸本)9798350390162

Weakly Supervised Object Detection (WSOD) trains detectors using only image-level annotations. Most existing WSOD models are based on pre-computed proposals and do not fully explore the relations of proposals. In this work, we address this limitation by proposing two approaches of Proposal Feature Learning for WSOD (PFL-WSOD), which effectively capture intra-proposal relations and inter-proposal relations respectively, thus improving proposal representation. To extract intra-proposal relations, we propose to utilize Self-Attention on Single Proposal for capturing relations inside each proposal. For inter-proposal relations, we propose Salient Region Banks by capturing a unique type of inter-proposal relation called deep inclusion, which significantly improves proposal representation when used in synergy with contrastive learning. Experimental results on benchmarks demonstrate the effectiveness of our methods.

关键词： Representation learning Annotations Object detection Contrastive learning Detectors Benchmark testing Feature extraction

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Multi-instance partial-label learning with margin adjustment 24

Multi-instance partial-label learning with margin adjustment

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Proceedings of the 38th International Conference on Neural Information Processing Systems

作者： Wei Tang Yin-Fang Yang Zhaofei Wang Weijia Zhang Min-Ling Zhang School of Computer Science and Engineering Southeast University Nanjing China and Key Laboratory of Computer Network and Information Integration (Southeast University) Ministry of Education China School of Information and Physical Sciences The University of Newcastle Callaghan NSW 2308 Australia

ISBN: (纸本)9798331314385

Multi-instance partial-label learning (MIPL) is an emerging learning framework where each training sample is represented as a multi-instance bag associated with a candidate label set. Existing MIPL algorithms often overlook the margins for attention scores and predicted probabilities, leading to suboptimal generalization performance. A critical issue with these algorithms is that the highest prediction probability of the classifier may appear on a non-candidate label. In this paper, we propose an algorithm named MIPLMA, i.e., Multi-Instance Partial-Label learning with Margin Adjustment, which adjusts the margins for attention scores and predicted probabilities. We introduce a margin-aware attention mechanism to dynamically adjust the margins for attention scores and propose a margin distribution loss to constrain the margins between the predicted probabilities on candidate and non-candidate label sets. Experimental results demonstrate the superior performance of MIPLMA over existing MIPL algorithms, as well as other well-established multi-instance learning algorithms and partial-label learning algorithms.

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Beyond 5G network Failure Classification for network Digital Twin Using Graph Neural network

arXiv

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arXiv 2024年

作者： Isah, Abubakar Aliyu, Ibrahim Shim, Jaechan Ryu, Hoyong Kim, Jinsul HyperIntelligence Media Network Platform Laboratory Department of Intelligent Electronics and Computer Engineering Chonnam National University Gwangju61186 Korea Republic of Department of Computer Science Ahmadu Bello University Zaria Nigeria Korea Republic of

Fifth-generation (5G) core networks in network digital twins (NDTs) are complex systems with numerous components, generating considerable data. Analyzing these data can be challenging due to rare failure types, leading to imbalanced classes in multiclass classification. To address this problem, we propose a novel method of integrating a graph Fourier transform (GFT) into a message-passing neural network (MPNN) designed for NDTs. This approach transforms the data into a graph using the GFT to address class imbalance, whereas the MPNN extracts features and models dependencies between network components. This combined approach identifies failure types in real and simulated NDT environments, demonstrating its potential for accurate failure classification in 5G and beyond (B5G) networks. Moreover, the MPNN is adept at learning complex local structures among neighbors in an end-to-end setting. Extensive experiments have demonstrated that the proposed approach can identify failure types in three multiclass domain datasets at multiple failure points in real networks and NDT environments. The results demonstrate that the proposed GFT-MPNN can accurately classify network failures in B5G networks, especially when employed within NDTs to detect failure types. Copyright © 2024, The Authors. All rights reserved.

关键词： Message passing

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A Fault Diagnostic Method for Rolling Bearings Based on CAR-SNN Model

A Fault Diagnostic Method for Rolling Bearings Based on CAR-...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Li, Qiuting Wang, Xiuqing Du, Wenxia Hebei Normal University College of Computer and Cyber Security Shijiazhuang050024 China Hebei Provincial Key Laboratory of Network & Information Security Shijiazhuang China Hebei Provincial Engineering Research Center for Supply Chain Big Data Analytics & Data Security Shijiazhuang China Hebei Normal University College of China Combustion Engineering Shijiazhuang050024 China

ISBN: (纸本)9798350337754

In the era of big data, high-performance rolling bearing fault diagnostic methods are of great significance to ensure manufacturing smoothly and avoid mass economic losses. A rolling bearing fault diagnostic CAR-SNN model based on Convolutional Block Attention Module (CBAM), Residual network (ResNet), and Spiking Neural networks (SNNs) is proposed based on deep learning (DL). The CAR-SNN model combines CBAM, ResNet, and SNNs to extract critical features while avoiding gradient vanishing or explosion phenomena. The ability of the CAR-SNN model to process spatio-temporal information is improved by using SNNs that incorporate spatio-temporal information simultaneously. The proposed model is used for the rolling bearing fault diagnosis, which datasets are from the experimental platforms of CWRU and Jiangnan University, with the highest diagnostic accuracy 99.995% and 99.18%, respectively. The experimental results validate the effectiveness and stability of the proposed model. The results of comparison experiments show that the CAR-SNN model has better fault diagnostic accuracy and stability than that of the CAR model and other comparison methods. The proposed data-driven rolling bearing fault diagnostic method based on the CAR-SNN model is easy to be implemented, and meeting the needs of high fault diagnostic accuracy in practical applications, and provides valuable input for fault diagnosis of rolling bearings. © 2023 IEEE.

关键词： Failure analysis

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Fast Collection of Target Tags Information for Large-Scale RFID Systems 20

Fast Collection of Target Tags Information for Large-Scale R...

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20th International computer Conference on Wavelet Active Media Technology and Information Processing, ICCWAMTIP 2023

作者： Chu, Chu Fu, Ning Xu, Rui College of Computer Science Sichuan Normal University Chengdu610101 China The Visual Computing and Virtual Reality Key Laboratory of Sichuan Province Chengdu610101 China School of Network and Communication Engineering Chengdu Technological University Chengdu611730 China School of Information and Communication Engineering University of Electronic Science and Technology of China Chengdu611731 China

ISBN: (纸本)9798350318982

Radio Frequency Identification (RFID) technology is greatly supporting a variety of life and industry. Enhanced by sensors, RFID tags can monitor real-time information about attached objects and the environment, making efficient collection of tag information an important task. However, most works focus on collecting all tags but lack effective mechanisms to avoid interfering from irrelevant tags when the application only cares about a part of the tags in some cases. Besides, in more common scenarios with multiple RFID readers, existing works fail to effectively determine the surrounding tags for each reader, increasing the execution time. To conquer these defects, we propose a new solution called the efficient Vector-based target tag Information Collection (VIC) protocol to accelerate the collection rate. First, we introduce a composite vector to silence irrelevant tags and determine the local tags. During this process, the expected empty slots are skipped and the useless information transmitted by tags is reduced. Second, we use an allocation vector to collect tag information by resolving tag collision, thus improving the time efficiency. Finally, we provide theoretical analyses to optimize the parameter settings. Extensive experiments prove that VIC outperforms existing solutions by up to 32% increase in time efficiency. © 2023 IEEE.

关键词： Efficiency

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