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Enhancing IoT Flow Anomaly Detection with Differential Optimal Feature Subspace

Enhancing IoT Flow Anomaly Detection with Differential Optim...

引用

IEEE international conference on Data Science in Cyberspace (DSC)

作者： Weidong Shi Mingrui Fan Jiaqi Gao Jiahao Qi Daoqi Han Yueming Lu Key Laboratory of Trustworthy Distributed Computing and Service (BUPT) Ministry of Education School of Cyberspace Security The State Key Laboratory of Information Photonics and Optical Communications School of Information and Communication Engineering Beijing University of Posts and Telecommunications Beijing China

With the continuous expansion of network scale and increasing complexity, the application of the Internet of things (IoT) is becoming more extensive. However, it is also vulnerable to various abnormal attacks, making security problems increasingly significant. Typically, traffic data dimensions are relatively large, and selecting features effectively is a key research issue. Addressing the aforementioned problems, this paper analyzes network traffic data and proposes a method(DOFS) to select the optimal feature subspace for different types of attacks, and verifies its effectiveness by comparative experiments. then the T-S feature selection algorithm is improved, and DOFS is applied to the improved T-S feature selection algorithm, and the usability of the method is verified by experiments. By obtaining feature importance scores, the best feature subspace is selected for each type of traffic category. the method is verified using the network flow dataset CIC2018 and a real-world flow dataset. Experimental evaluations on diverse datasets demonstrate that the proposed method outperforms existing feature selection algorithms under different evaluation metrics, while exhibiting efficient time over-head and strong generalization ability. the results highlight the effectiveness and practicality of the proposed approach in IoT flow anomaly detection.

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Polyfund: Polygon - Based Crowdfunding Dapp

Polyfund: Polygon - Based Crowdfunding Dapp

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international conference for Convergence of Technology (I2CT)

作者： Milind Rane Smita Mande Mustansir Bohari Asif Mursal Vinit Mundhada Omkar Mundlik Mohit Chawla Jayesh Mundphane Department of E&TC Vishwakarma Institute of Technology Pune Maharashtra India Department of DESH Vishwakarma Institute of Technology Pune Maharashtra India Department of AI&DS Vishwakarma Institute of Technology Pune Maharashtra India Department of Instru and Control Vishwakarma Institute of Technology Pune Maharashtra India Department of Mechanical Engineering Vishwakarma Institute of Technology Pune Maharashtra India

the rise of information technology has led to a surge in interest of investors and businesses in advanced futuristic technologies like Blockchain and Cryptocurrency. Blockchain is the heart of web3 technologies. One of the fields that could be revolutionized using it is crowdfunding. Crowdfunding is the process of supporting a project by obtaining modest contributions from a large number of individuals, usually online. Modern day crowdfunding websites are centralized, prone to cyberattacks, charge high amounts of transaction fees, have print rules & regulations and intellectual property risk. Blockchain has emerged as a technology which promises decentralization of a system along with a highly secured database. Blockchain is a distributed immutable ledger used to record transactions. Our project proposes a model to switch traditional crowdfunding processes on a Blockchain Network called Polygon. Apps made on Polygon use the same frontend as that used in traditional web2 websites but the backend instead of being a centralized database is an open peer-to-peer network. Our proposed work has a promising future and lots of room for development.

关键词： Databases Smart contracts Switches Intellectual property Regulation Blockchains Peer-to-peer computing

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EFM: A Negative Network Public Opinion Early Warning Model Based on Evolutionary Feature Mining

EFM: A Negative Network Public Opinion Early Warning Model B...

引用

IEEE international conference on Data Science in Cyberspace (DSC)

作者： Fanfeixue Meng Jingchen Wu Feifan Song Xu Wu Lan Hu Key Laboratory of Trustworthy Distributed Computing and Service Ministry of Education School of Cyberspace Security BUPT Beijing University of Posts and Telecommunications Beijing China School of Computer Science (National Pilot Software Engineering School) BUPT School of Economics and Finance Changchun Finance College

With the popularity of the Internet and the increase in the number of netizens, it is of great significance to effectively monitor and predict the negative network public opinion on social media platforms. Most mainstream negative network public opinion early warning models mainly focus on mining textual features, while neglecting the response and dissemination among netizens during the process of negative network public opinion fermentation. this is one of the important reasons for the accuracy bottleneck of them. this article proposes a negative network public opinion early warning model based on evolutionary feature mining. Firstly, by using entity linking techniques based on knowledge graphs, domain prior knowledge is supplemented for the text need to be analyzed for negative network public opinion. then, the text sentiment features extracted based on long short-term memory networks and the evolutionary features extracted based on graph convolutional networks are fused through an enhanced-feature fusion method. Finally, the features are used for classification and early warning. Experimental results based on the Weibo public dataset show that the EFM model has higher early warning performance for negative network public opinion compared to other baseline models. the accuracy of this model is 0.959.

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NetSheriff: Sheltering Software-Defined Networks from Rogue Switches 1

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8th international conference on Networked Systems (NETYS)

作者： Laffranchini, Paolo Miranda, Joao Machado, Nuno Rodrigues, Luis Riviere, Etienne Sadre, Ramin ULisboa INESC ID IST Lisbon Portugal UCLouvain ICTEAM Louvain La Neuve Belgium Teradata San Diego CA USA

ISBN: (数字)9783030670870

ISBN: (纸本)9783030670863;9783030670870

We present NetSheriff - a system to automatically isolate faulty switches in Software-Defined Networks. To pinpoint the devices responsible for network misbehaviors, NetSheriff performs a differential analysis between expected paths of packets (obtained from a formal model of the network forwarding specification) and the corresponding observed paths taken by flows (obtained through network monitoring). We have built a prototype of NetSheriff supporting both OpenFlow and P4 Programmable devices and evaluated it on different network topologies, simulating real traffic behavior following recent data center studies. Our results show that NetSheriff is able to accurately identify the switch(es) responsible for different types of errors.

关键词： Software defined networking

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Facial Expression Recognition Based on Deep Spatio-Temporal Attention Network 18th

Facial Expression Recognition Based on Deep Spatio-Temporal ...

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18th European-Alliance-for-Innovation (EAI) international conference on Collaborative computing - networking, Applications and Worksharing (CollaborateCom)

作者： Li, Shuqin Zheng, Xiangwei Zhang, Xia Chen, Xuanchi Li, Wei Shandong Normal Univ Sch Informat Sci & Engn Jinan Peoples R China Shandong Prov Key Lab Distributed Comp Software N Jinan Peoples R China Taian City Cent Hosp Internet Diag & Treatment Ctr Tai An Shandong Peoples R China Shandong Normal Univ Lib Shandong Normal Univ Jinan Peoples R China

ISBN: (纸本)9783031243851;9783031243868

Facial expression recognition is extremely critical in the process of human-computer interaction. Existing facial expression recognition tends to focus on a single feature of the face and does not take full advantage of the integrated spatio-temporal features of facial expression images. therefore, this paper proposes a facial expression recognition based on a deep spatio-temporal attention network (STANER) to capture the spatio-temporal features of facial expressions when they change subtly. A facial expression recognition with an attention module based on spatial global features (SGAER) is created firstly, where the addition of the attention module is able to quantify the importance of each part of the expression feature map and thus extract the spatial global appearance features at the time of subtle expression changes from a single frame expression image. then, facial expression recognition with C-LSTM based on temporal local features (TLER) is built to process image sequences of facial regions linked to expression creation and extract dynamic local temporal information about expressions. Experiments are carried out on CK+ and Oulu-CASIA datasets. the results showed that STANER can achieve better performance with the accuracy rates of 98.23% and 89.52% on the two mainstream datasets, respectively.

关键词： Facial expression recognition Spatio-temporal features Deep attention network

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Efficient Transparent Access to 5G Edge Services

Efficient Transparent Access to 5G Edge Services

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IEEE conference on Network Softwarization (NetSoft)

作者： Josef Hammer Hermann Hellwagner Institute of Information Technology Alpen-Adria-Universit&#x00E4 t Klagenfurt Austria

ISBN: (数字)9781665406949

ISBN: (纸本)9781665406956

Multi-access Edge computing (MEC) is a central piece of 5G telecommunication systems and is essential to satisfy the challenging low-latency demands of future applications. MEC provides a cloud computing platform at the edge of the radio access network that developers can utilize for their applications. In [1] we argued that edge computing should be transparent to clients and introduced a solution to that end. this paper presents how to efficiently implement such a transparent approach, leveraging Software-Defined networking. For high performance and scalability, our architecture focuses on three aspects: (i) a modular architecture that can easily be distributed onto multiple switches/controllers, (ii) multiple filter stages to avoid screening traffic not intended for the edge, and (iii) several strategies to keep the number of flows low to make the best use of the precious flow table memory in hardware switches. A performance evaluation is shown, with results from a real edge/fog testbed.

关键词： Performance evaluation Multi-access edge computing 5G mobile communication Scalability conferences Computer architecture Switches

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BYEE: Batch Fully Private Scheme for Coded distributed Matrix Multiplication

BYEE: Batch Fully Private Scheme for Coded Distributed Matri...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Guojing Li Jin Wang Xiaotian Zou Lingzhi Li Fei Gu School of Computer Science and Technology Soochow University Suzhou China Collaborative Innovation Center of Novel Software Technology and Industrialization

Recently, user privacy in distributed computing has received increasing attention. Matrix multiplication is one of the fundamental high-frequency operations in distributed machine learning (e.g., gradient descent, linear regression). this paper studies the batch Fully Private distributed Matrix Multiplication (FPMM) problem. In batch FPMM, the user aims to compute multiple desired matrix multiplications from two public libraries which are stored across all workers. Batch FPMM requires that colluding workers cannot obtain the indexes of all desired ma-trices in the two public libraries (i.e., privacy constraint). In this paper, we first propose the Bath fullY privatE schemE (BYEE) to encode multiple desired matrix multiplications together based on the construction of bilinear complexity. We then perform a solid theoretical analysis to prove the privacy and decodability of BYEE. Multi-aspect analysis and extensive simulation results illustrate that BYEE can significantly reduce the computation latency than state-of-the-art schemes that do not jointly encode multiple desired matrix multiplications, especially when the number of desired matrix multiplications and the number of colluding workers are large.

关键词： Privacy Analytical models Simulation Computational modeling Linear regression Machine learning Solids

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Questionable Fairness in Federated Learning

Questionable Fairness in Federated Learning

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conference on Data Science and Machine Learning Applications (CDMA)

作者： Raed Alharbi Truc Nguyen Nooshin Yousefzadeh Ahmed Aljohani College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia Computatioual Science Center National Renewable Energy Laboratory Golden CO USA Computer and Information Science and Engineering Department University of Florida Gainesville FL USA Computer Science and Engineering University of North Texas Denton USA

ISBN: (数字)9798331539696

ISBN: (纸本)9798331539702

Federated learning (FL) has emerged as a powerful framework for training deep learning models across numerous distributed clients, where a central server distributes and aggregates model updates without accessing clients' raw data, ensuring privacy. However, this decentralized approach introduces significant challenges regarding fairness. Existing research shows that non-i.i.d. data distributions among clients can lead to biased global models, particularly affecting decisions based on sensitive attributes. this paper aims to investigate these fairness issues rigorously by analyzing how disparities in client data distribution impact model fairness. We conduct experiments using various models and datasets to evaluate and mitigate biases. We also propose novel data partitioning strategies to reflect real-world client data distributions accurately. Our finding shows that FL is highly vulnerable to the fairness problem due to the Non-IID property. the experiment's code can be found here: https://***/raed19/Questionable-Fairness-in-Federated-Learning.

关键词： Training Deep learning Data privacy Federated learning Prevention and mitigation distributed databases Data science Data models Servers Multiaccess communication

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GSLA: A Tool for Deciphering Genotype Changes on Phenotype Level

GSLA: A Tool for Deciphering Genotype Changes on Phenotype L...

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international conference on Network and Information Systems for Computers (ICNISC)

作者： Li Ruan Pengcheng Chen Institute of Big Data and Artificial Intelligence in Medicine School of Electronics and Information Engineering Taizhou University Taizhou China

ISBN: (纸本)9781665453523

We perform the gene set linkage analysis (GSLA) tool for interpreting the functional influences of genes with expression change in omics studies. It has been illustrated in several studies that the algorithm is useful for finding new clues for physiological coordination in transcriptome profile analyses, where traditional analysis tools cannot find similar results. the web tool of GSLA supports seven model organisms: including H. sapiens, M. musculus, A. thaliana et al.

关键词： Couplings Computers Data analysis Computational modeling Physiology Organisms distributed computing

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FNotify: A Low-Latency and Scalable Publish/Subscribe System using RDMA

FNotify: A Low-Latency and Scalable Publish/Subscribe System...

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IEEE international conference on High Performance computing and Communications (HPCC)

作者： Yusen Xu Sijie Shen Mingcong Han Rong Chen Institute of Parallel and Distributed Systems SEIEE Shanghai Jiao Tong University Shanghai China

Publish/subscribe systems are widely used as messaging middleware in various scenarios, such as online business, financial trading, and social media. However, the network overhead intro-duced by the pub/sub system can severely impact application performance. Especially in the latency-sensitive applications of the data center, optimizing the latency of publishing or delivering messages of the pub/sub system is particularly important. RDMA network has gained popularity in relevant applications due to its low latency, high bandwidth and ability to bypass the CPU. Never-theless, it is still challenging to design and optimize the pub/sub system using RDMA. through an in-depth analysis of RDMA features, we design and implement FNotify, a high-performance RDMA-accelerated pub/sub system. FNotify optimizes latency and CPU load using in-memory message management and one-sided RDMA primitives for message collection. FNotify also proposes a topic-based partitioning scheme to achieve good scal-ability. We conducted a comprehensive evaluation and analysis on an 8-node RDMA cluster to verify the validity of our design by comparing the performance of FNotify with Apache Kafka (a state-of-the-art pub/sub system) in a typical pub/sub application scenario and various micro-benchmarks. Experimental results show that FNotify improves overall performance by $1.5\times$ to $3.9\times$ and reduces end-to-end latency by more than $20\times$ compared to Apache Kafka.

关键词： Data centers Publishing Social networking (online) Scalability Semantics Systems architecture Low latency communication

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