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International Conference on Parallel and Distributed Systems (ICPADS)

作者： Yuwei Xu Yuxing Song Junyu Zeng Ran He Jingdong Xu School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China College of Computer Science Nankai University Tianjin China

With the popularization of consortium blockchain, how to supervise numerous business chains has become a problem. Recently, some researchers have proposed the concept of ‘governing chain by chain’, which means that multiple organizations deploy a consortium blockchain to supervise many business chains in the industry. However, as the number of business chains increases, a performance bottleneck occurs on the supervision chain. Sharding is a promising direction to improve scalability, but existing studies focus on public chains. If transplanted to the supervision chain, these solutions will bring two challenges. Firstly, data from multiple business chains will cause storage conflicts and unbalanced sharding. Secondly, sharding makes it difficult for inter-shard transaction validation. Aiming at the challenges, we propose $\mathcal{S}$-chain, a shard-based supervision chain scheme. The contribution of our work lies in three points. Firstly, we design a unified label-based data sharding method that can evenly map data from different business chains to all shards without conflict. Secondly, we propose a mechanism to validate general smart-contract-based inter-shard transactions. At first, we design a smart contract splitting algorithm that splits one inter-shard smart contract into multiple sub-contracts and depicts their dependencies via a Directed Acyclic Graph(DAG). Then, we design an inter-shard transaction validation method by analyzing the dependencies of all sub-contracts. Finally, we implement a prototype system of $\mathcal{S}$-chain and test its performance. The results show that our $\mathcal{S}$-chain outperforms the scheme without sharding on throughput and storage overhead.

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MedUniSeg: 2D and 3D Medical Image Segmentation via a Prompt-driven Universal Model

arXiv

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arXiv 2024年

作者： Ye, Yiwen Chen, Ziyang Zhang, Jianpeng Xie, Yutong Xia, Yong The National Engineering Laboratory for Integrated Aero-Space-Ground-Ocean Big Data Application Technology School of Computer Science and Engineering Northwestern Polytechnical University Xi’an710072 China The College of Computer Science and Technology Zhejiang University Zhejiang China The University of Adelaide Australia Research & Development Institute of Northwestern Polytechnical University in Shenzhen Shenzhen518057 China The Ningbo Institute of Northwestern Polytechnical University Ningbo315048 China

Universal segmentation models offer significant potential in addressing a wide range of tasks by effectively leveraging discrete annotations. As the scope of tasks and modalities expands, it becomes increasingly important to generate and strategically position task- and modal-specific priors within the universal model. However, existing universal models often overlook the correlations between different priors, and the optimal placement and frequency of these priors remain underexplored. In this paper, we introduce MedUniSeg, a prompt-driven universal segmentation model designed for 2D and 3D multi-task segmentation across diverse modalities and domains. MedUniSeg employs multiple modal-specific prompts alongside a universal task prompt to accurately characterize the modalities and tasks. To generate the related priors, we propose the modal map (MMap) and the fusion and selection (FUSE) modules, which transform modal and task prompts into corresponding priors. These modal and task priors are systematically introduced at the start and end of the encoding process. We evaluate MedUniSeg on a comprehensive multi-modal upstream dataset consisting of 17 sub-datasets. The results demonstrate that MedUniSeg achieves superior multi-task segmentation performance, attaining a 1.2% improvement in the mean Dice score across the 17 upstream tasks compared to nnUNet baselines, while using less than 1/10 of the parameters. For tasks that underperform during the initial multi-task joint training, we freeze MedUniSeg and introduce new modules to re-learn these tasks. This approach yields an enhanced version, MedUniSeg*, which consistently outperforms MedUniSeg across all tasks. Moreover, MedUniSeg surpasses advanced self-supervised and supervised pre-trained models on six downstream tasks, establishing itself as a high-quality, highly generalizable pre-trained segmentation model. The code and model will be available at https://***/yeerwen/UniSeg. Copyright © 2024, The Authors.

关键词： Image segmentation

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The Performance Analysis of Distributed Denial of Service Attacks in Wireless Networks

The Performance Analysis of Distributed Denial of Service At...

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International Conference on Futuristic Technologies (INCOFT)

作者： Trapty Agrawal R Raghavendra K. Ranjith Singh Rajiv Gandhi N Dharmesh Dhabliya Jitendra Kumar Katariya Maharishi School of Engineering and Technology Maharishi University of Information Technology Uttar Pradesh Indi Department of Computer Science and Information Technology Jain (Deemed to be University) Bangalore Karnataka India Department of Computer Science Karpagam Academy of Higher Education Coimbatore Department of Civil Engineering Prince Shri Venkateshwara Padmavathy Engineering College Chennai Department of Information Technology Vishwakarma Institute of Information Technology Pune Maharashtra India Department of Computer Science & Application Vivekananda Global University Jaipur India

Complete studies paper that examines the outcomes that malicious 0.33-celebration actors will have on Wi-Fi networks via dispensed denial of carrier (DDoS) attacks. This paper explores the available numerous assault models and the potential countermeasures that may be applied if you want to mitigate the damage due to such assaults. In addition, this paper focuses on the capability blessings related to deploying preventative measures to lessen the impact of such assaults. Moreover, the paper investigates existing attack trends. It affords an outline of the safety techniques to be had for limiting the harm caused by DDoS attacks in wireless networks. By comparing and analyzing the performance of diverse protection mechanisms, this paper offers an evidenceprimarily based evaluation of the effectiveness of DDoS attack safety answers. ultimately, this paper concludes with the aid of supplying recommended measures for strengthening and ensuring the safety of Wi-Fi networks against malicious actors.

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Contrastive Patch Comparison Transformer for Weakly Supervised Semantic Segmentation

Contrastive Patch Comparison Transformer for Weakly Supervis...

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IEEE International Conference on Smart Internet of Things (SmartIoT)

作者： Wentian Cai Weixian Yang Jing Lin Ying Gao School of Computer Science and Engineering South China University of Technology Guangzhou China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People's Hospital Guangdong Academy of Medical Sciences Guangzhou China

ISBN: (数字)9798350366440

ISBN: (纸本)9798350366457

Weakly supervised semantic segmentation (WSSS) focuses on utilizing image-level labels to classify all pixels into object classes or background class. Class Activation Mapping (CAM) highlights the most discriminative region of the target object, but fails to discover minor details in whole image. The gap between classification task and segmentation task causes a reduction in the accuracy. In this paper, we propose a simple-yet-efficient WSSS framework based on Transformer backbone. We introduce a patch-wise contrastive learning module to discover potential patch embedding information. Additionally, we introduced a multi-scale supervision to constrain the network to focus on target objects while learning patch-wise contrasty. Experimental results validate that our framework yields good performance on VOC2012 without using any salient maps.

关键词： Accuracy Semantic segmentation Semantics Contrastive learning Transformers Internet of Things

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Cerberus: Efficient OSPS Traffic Identification through Multi-Task Learning

Cerberus: Efficient OSPS Traffic Identification through Mult...

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

作者： Yuwei Xu Xiaotian Fang Jie Cao Rou Yu Kehui Song Guang Cheng School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China College of Computer Science Nankai University Tianjin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

The privacy protection capabilities of open source proxy software (OSPS) while browsing the Internet have sparked great interest from both industry and academia, bringing forth pressing security concerns. Currently, using artificial intelligence for traffic identification is the most promising direction. Due to the wide variety and rich configuration of OSPS, it is not feasible to train models for all tasks and deploy them on the same network device. It is a novel idea to improve efficiency by leveraging multi-task learning. However, the related studies still have three shortcomings. First, improving the performance of the main task through auxiliary tasks does not apply to equally important OSPS identification tasks. Second, the model’s ability to characterize traffic is weak, resulting in performance gaps between different tasks. Finally, the influence of task difficulty on convergence speed is ignored, which is easy to cause overfitting and underfitting. Aiming at the shortcomings, we propose Cerberus, an OSPS traffic identification scheme based on multi-task learning. The main contributions of our work can be summarized in three aspects. Firstly, a high-quality dataset is constructed through traffic collection, and three OSPS traffic identification tasks are defined on it. Secondly, an identification model is designed by optimizing the ability to characterize traffic and balancing the convergence speed of multiple tasks. Finally, Cerberus is verified through comparative experiments. Its classification performance is better than both single-task and multi-task solutions. Besides, Cerberus runs fast and consumes few resources, making it suitable for deployment on network devices.

关键词： Performance evaluation Privacy Protocols Multitasking Transformers Software Security

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Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on Retentive Network

Sequence-to-Sequence Multi-Hop Knowledge Reasoning Based on ...

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computer Information science and application Technology (CISAT), International Conference on

作者： Ruilin Su Wanshan Zhang Dunhui Yu Hubei University Wuhan China Wuhan China School of Computer Science and Information Engineering Hubei University Wuhan China Hubei Key Laboratory of Big Data Intelligent Analysis and Application Hubei University Wuhan China Engineering and Technical Research Center of Hubei Province in Software Engineering Wuhan China

ISBN: (数字)9798350375107

ISBN: (纸本)9798350375114

Multi-hop Knowledge Reasoning is a task that involves generating an answer given a query and a knowledge graph. Existing sequence-to-sequence reasoning models use the Transformer to encode and decode sequences, but these models have some flaws, such as the inability to effectively handle long-sequence reasoning and susceptibility to exposure bias. To address these issues, we provides a sequence-to-sequence reasoning model named STSR, which is based on Retentive Network. Aiming at improving training efficiency through parallel training, this model leverages the advantages in Retentive Network, reduce time overhead, and enhance reasoning efficiency through iterative reasoning. It effectively mitigates the problems of high spatial overhead and low efficiency in long-sequence reasoning faced by the Transformer. Moreover, it retains and updates historical information during the encoding and decoding process, enhancing the model’s memory and generalization capabilities. Additionally, the Scheduled Sampling method is adopted to alleviate the exposure bias introduced during reasoning, which involves using the model’s own output as the input for the next moment with a certain probability during training, instead of using the true label. We conduct the experiment on six public datasets, with the results showing that the proposed model outperforms existing baseline models in terms of precision and generation quality. This paper provides a new solution for the task of sequence-to-sequence multihop knowledge reasoning and also demonstrates the potential application of the Retentive Network in natural language processing.

关键词： Training Knowledge engineering Accuracy Spread spectrum communication Knowledge graphs Transformers Sampling methods Cognition Natural language processing Iterative decoding

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SharpEye: Identify mKCP Camouflage Traffic through Feature Optimization

SharpEye: Identify mKCP Camouflage Traffic through Feature O...

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

作者： Yuwei Xu Zizhi Zhu Yunpeng Bai Lilanyi Wu Kehui Song Guang Cheng School of Cyber Science and Engineering Southeast University Nanjing China Purple Mountain Laboratories for Network and Communication Security Nanjing China Engineering Research Center of Blockchain Application Supervision and Management Nanjing China College of Computer Science Nankai University Tianjin China

ISBN: (数字)9798350381993

ISBN: (纸本)9798350382006

As a new self-developed protocol of V2Ray, mKCP disguises users’ network access as communication of four network applications by forging application layer headers to evade traffic-based detection. The emergence of mKCP has received widespread attention. Whether mKCP can provide secure network access that protects user privacy is the focus. Traditional methods cannot identify mKCP camouflage traffic, but machine learning (ML)-based traffic identification is considered a promising direction. Unlike the previous network traffic classification, mKCP camouflage traffic identification introduces new challenges. First, existing work has neither published any dataset containing mKCP camouflage traffic nor designed specific traffic features. Second, no researchers have optimized the identification scheme for deployment on network devices. Aiming at the shortcomings, we propose SharpEye, an ML-based mKCP camouflage traffic identification scheme. The novelty of our work lies in three points. Firstly, a complete dataset containing mKCP camouflage traffic is constructed through long-term traffic collection. Secondly, by analyzing the communication patterns of mKCP traffic, a feature set mFS is designed to improve identification accuracy. Finally, a two-stage feature selection method mGBFS is proposed to improve the operation efficiency. The experimental results show that mFS can enhance the performance of classifiers in identifying mKCP camouflage traffic, and mGBFS reduces the running time and overhead while ensuring high accuracy. Therefore, SharpEye achieves accurate and efficient mKCP camouflage traffic identification.

关键词： Deep learning Privacy Protocols Telecommunication traffic Feature extraction Classification algorithms Security

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A Novel based Hybrid smart System for detecting the cyber-attacks on IOT’s

A Novel based Hybrid smart System for detecting the cyber-at...

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Artificial Intelligence and Smart Communication (AISC), International Conference on

作者： K. Juliana Gnanaselvi Surendra Kumar Shukla Keerthipati Kumar Anita Gehlot Tarun Jaiswal B. T. Geetha Department of Computer Science Rathinam College of Arts and Science Coimbatore Tamil Nadu India Department of Computer Science & Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Department of Information Technology University of Technology and Applied Science- Al Mussnna Oman Uttaranchal Institute of Technology Uttaranchal University India Department of Computer Application National Institute of Technology Raipur (C.G.) Department of ECE Saveetha School of Engineering SIMATS Tamil Nadu India

The Iot technology (IoT) is rapidly advancing sufficiently that it might have a larger effect on huge industrial infrastructure and everyday routines. However, this became the focus of fraudsters, whom made IoT into such a prey for their misbehaviour, thereby potentially opening the door for an assault on the computer systems. Have used a traditional intrusion detection device to manage the Iot network is problematic because of the enormous variety and quantity of IoT devices. A revolutionary aggregation Interdisciplinary Detection System (HIDS) that merges a C5 predictor with the a One Category Classification Algorithm is built to guard IoT devices. It project's goal is to find threats that can be inhibited with high classification certainty and low completely bogus rates, for both storing and accessing and negligible vulnerabilities. The User database, consisting includes include legal IoT data stream and other varieties of assaults, is employed to assess the specified HIDS. Studies show that when combined with SIDS and Assistance methodologies, the proposed hybrid IDS provides a higher accuracy results and a smaller percentage of false positives.

关键词： Fault diagnosis Law Databases Fault detection Stacking Ecosystems Intrusion detection

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AsyncSC: An Asynchronous Sidechain for Multi-Domain Data Exchange in Internet of Things

arXiv

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arXiv 2024年

作者： Yang, Lingxiao Dong, Xuewen Wan, Zhiguo Gao, Sheng Tong, Wei Lu, Di Shen, Yulong Du, Xiaojiang The School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi'An710071 China The Zhejiang Lab Hangzhou311121 China The School of Information Central University of Finance and Economics Beijing100081 China The School of Information Science and Engineering Zhejiang Sci-Tech University Hangzhou310018 China The School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi'An710071 China The School of Engineering and Science Stevens Institute of Technology Hoboken07030 United States

Sidechain techniques improve blockchain scalability and interoperability, providing decentralized exchange and cross-chain collaboration solutions for Internet of Things (IoT) data across various domains. However, current state-of-the-art (SOTA) schemes for IoT multi-domain data exchange are constrained by the need for synchronous networks, hindering efficient cross-chain interactions in discontinuous networks and leading to suboptimal data exchange. In this paper, we propose AsyncSC, a novel asynchronous sidechain construction. It employs a committee to provide Cross-Blockchain as a Service (C-BaaS) for data exchange in multi-domain IoT. To fulfill the need for asynchronous and efficient data exchange, we combine the ideas of aggregate signatures and verifiable delay functions to devise a novel cryptographic primitive called delayed aggregate signature (DAS), which constructs asynchronous cross-chain proofs (ACPs) that ensure the security of cross-chain interactions. To ensure the consistency of asynchronous transactions, we propose a multilevel buffered transaction pool that guarantees the transaction sequencing. We analyze and prove the security of AsyncSC, simulate an asynchronous communication environment, and conduct a comprehensive evaluation. The results show that AsyncSC outperforms SOTA schemes, improving throughput by an average of 1.21 to 3.96 times, reducing transaction latency by 59.76% to 83.61%, and maintaining comparable resource overhead. Copyright © 2024, The Authors. All rights reserved.

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Tensor Graph Convolutional Network for Dynamic Graph Representation Learning

arXiv

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arXiv 2024年

作者： Wang, Ling Yuan, Ye The School of Computer Science and Technology Chongqing University of Posts and Telecommunications Chongqing400065 China The Chongqing Key Laboratory of Big Data and Intelligent Computing Chongqing Engineering Research Center of Big Data Application for Smart Cities Chongqing Institute of Green and Intelligent Technology Chinese Academy of Sciences Chongqing400714 China The College of Computer and Information Science Southwest University Chongqing400715 China

Dynamic graphs (DG) describe dynamic interactions between entities in many practical scenarios. Most existing DG representation learning models combine graph convolutional network and sequence neural network, which model spatial-temporal dependencies through two different types of neural networks. However, this hybrid design cannot well capture the spatial-temporal continuity of a DG. In this paper, we propose a tensor graph convolutional network to learn DG representations in one convolution framework based on the tensor product with the following two-fold ideas: a) representing the information of DG by tensor form;b) adopting tensor product to design a tensor graph convolutional network modeling spatial-temporal feature simultaneously. Experiments on real-world DG datasets demonstrate that our model obtains state-of-the-art performance. Copyright © 2024, The Authors. All rights reserved.

关键词： Tensors

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