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distributed computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Sanjaikanth E Vadakkethil Somanathan Pillai Karthik Rajashekaran R. Archana Reddy Piyush Kumar Pareek Dipti Sontakke School of Electrical Engineering and Computer Science University of North Dakota Grand Forks USA Department of Computer Science New York University New York NY USA School of Computer Science and Engineering SR University Warangal Telangana India Department of AIML and IPR Cell Nitte Meenakshi Institute of Technology Bengaluru India Capgemini Inc USA

ISBN: (数字)9798350318609

ISBN: (纸本)9798350318616

In the era of social media, the social media and smartphone popularity has enhanced exponentially. By the electronic media, fake news has rising quick with new information which are hugely untrustworthy. The search engine like google are incapable for fraudulent of news because its limitation with restricted keywords. The Optimized Convolution Neural Network (OPCNN) is proposed for classifying fake news into actual and fraudulent news based on sentiment analysis. which accommodate varying complexity levels by adjusting its architecture in Fake News Detection (FND). The Principal Component Analysis (PCA) is used to extract features from preprocessed images which reduces the data dimension comprising numerous related variables and recalls the high change in real data. The ISOT dataset is preprocessed through four various techniques. The recall, accuracy, f1 score and precision with ISOT dataset are considered to evaluate OPCNN performance. The OPCNN realizes 99.58% recall, 99.67% accuracy, 99.61% f1score and 99.64% precision for ISOT dataset when matched to previous techniques like Random Forest (RF) and Sea Turtle Foraging Optimization-based Fake News Detection and Classification (STODL-FNDC).

关键词： Sentiment analysis Convolution Social networking (online) Neural networks Data preprocessing Computer architecture Feature extraction

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Analysis and Prediction of Outdoor Human Mobility Using Collaborative Learning

Analysis and Prediction of Outdoor Human Mobility Using Coll...

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international conference on distributed computing in Sensor Systems (DCOSS)

作者： Ioana-Valentina Marin Eduard-Claudiu Ciurezu Radu-Ioan Ciobanu Ciprian Dobre Faculty of Automatic Control and Computers National University of Science and Technology Politehnica Bucharest National Institute for Research and Development in Informatics Bucharest Romania

ISBN: (数字)9798350369441

ISBN: (纸本)9798350369458

Movement is fundamental to human existence. In today’s digital age, our ability to travel shapes our social interactions, cultural experiences, and even our health. Understanding and predicting how people move outdoors is crucial for a variety of reasons. Thus, it has easily become an area of research for tech-savvy people who aim to build digital tools that use human mobility data to study the dynamics of people. Human mobility in outdoor scenarios refers to the movement of people outside their homes, cities, or countries. Moreover, as the advancement of technology significantly shapes people’s lives, they are constantly connected on different social networks and unconsciously produce substantial amounts of data by exposing their daily activities to these networks. Thus, harnessing the large volume of data extracted through mobile apps and beyond, allows for improved tourism services, urban planning modalities or brings additional help in studying the migration of masses of people in case of natural disasters. Thus, the current paper aims to describe the concept of prediction and analysis applied in the field of human mobility, from the types of data that are used, to how we can obtain existing datasets and their sources, in the context of predicting the next location of a person or urban agglomeration in a given geographical area described by the data fetched. We finally argue the utilization of federated learning in the context of outdoor human mobility.

关键词： Shape Federated learning Social networking (online) Disasters Urban planning Information age Mobile applications

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A Novel DDoS Detection Model for SDN Using Single-Class Cluster Oversampling and Weighted Ensemble Method

A Novel DDoS Detection Model for SDN Using Single-Class Clus...

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international conference on Network Protocols

作者： Hao Zhang Shuqi Wu Jie Pan Zhi Wang Xiaolong Sun College of Computer and Data Science Fuzhou University Fuzhou China Fujian Key Laboratory of Network Computing and Intelligent Information Processing Fuzhou University Fuzhou China Engineering Research Center of Big Data Intelligence Chinese Ministry of Education Fuzhou China

ISBN: (数字)9798350351712

ISBN: (纸本)9798350351729

The centralized control plane characteristic of Software Defined networking (SDN) makes it a prime target for distributed Denial of Service (DDoS) attacks. A significant issue in network traffic data is the severe imbalance between background traffic and various types of DDoS attack traffic. To address this challenge, we propose a novel DDoS detection model based on single-class cluster oversampling and weighted ensemble method. Initially, we design a multi-strategy feature selection using variance inflation factor and recursive feature elimination. Next, we construct a single-class cluster model based on the spatial distribution of samples, and create minority class samples independently within each cluster. Finally, detection results are determined using the weight vector of each base classifier in ensemble learning. The proposed DDoS detection model's performance is validated through simulations of various DDoS attack scenarios in an SDN environment. Experimental results indicate that the proposed strategy exhibits superior performance, showing improvements in precision, recall, and F1 score compared to state-of-the-art techniques.

关键词： Interpolation Protocols Telecommunication traffic Denial-of-service attack Feature extraction Vectors Data models Ensemble learning Computer crime Software defined networking

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A distributed Storage System for System Logs Based on Hybrid Compression Scheme

A Distributed Storage System for System Logs Based on Hybrid...

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IEEE international conference on Big Data and Cloud computing (BdCloud)

作者： Baoming Chang Fengxi Zhou Zhaoyang Wang Yu Wen Boyang Zhang Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China

Modern enterprises are facing a massive threat from Advanced Persistent Threats (APTs), which have risen to be one of the most dangerous challenges in recent years. Since system logs capture the complex causality dependencies between system entities, they have become the primary data source for countering APTs. However, as modern computer systems get more complicated, system logs can pile up in large quantities. Besides, APTs are sophisticated and persistent cyber attacks that can remain hidden in the target for a long time and constantly steal private data. System logs need to be collected and stored for a long duration to enable a complete analysis of APTs. Such a vast amount of log data is challenging for enterprises to store and manage. There are two mainstream solutions for reducing storage overhead. Data compression methods provide an intuitive idea. However, they are designed for general text and lack optimization for system logs. Another solution considers log reduction, which removes redundant system events recorded in system logs by predefined rules. Unfortunately, they are tailored for specific kinds of redundant information, resulting in limited applicability. Realizing that these two solutions adopt two distinct perspectives to reduce storage overhead, they are complementary. Data compression methods shrink the size of log data from their binary form. Log reduction starts from the semantic information of system logs and removes redundant information to reduce storage overhead. Combining both methods maximizes storage efficiency. In this paper, we propose a distributed storage system based on a hybrid compression scheme. To address the above deficiencies, we first identify and merge redundant system events by analyzing and tracing the information flow rather than based on rules. Then, we apply log parsing to preprocess log entries for further storage efficiency. Besides, we design a distributed architecture to optimize compression and eliminate repeated

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Comprehensive Energy Efficient Approach to Workload Processing in distributed computing Environment

Comprehensive Energy Efficient Approach to Workload Processi...

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IEEE international Black Sea conference on Communications and networking (IEEE BlackSeaCom)

作者： Globa, Larysa Gvozdetska, Nataliia Natl Tech Univ Ukraine Igor Sikorsky Kyiv Polytech Inst Kiev Ukraine

ISBN: (纸本)9781728171272

Modern pace of Information and Communication Technologies (ICT) progress requires rapid development of completely new approaches to data processing. One of the most important criteria that should be taken into account in every computing system nowadays is its energy efficiency. Strict requirements to service availability and timeliness must be taken into account as well. In this paper new comprehensive energy efficient approach to workload processing in distributed computing environment is proposed. The goal of this approach is to get as close as possible to the energy-proportional computing model. Proposed approach combines the advantages of horizontal scaling and energy efficient scheduling taking into account individual power consumption characteristics of computing nodes and dynamicity of workload in modern computing systems. The efficiency of the proposed approach is proven using Matlab modeling.

关键词： energy-proportional computing energy consumption availability SLA dynamic workload distributed computing

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distributed ADMM With Synergetic Communication and Computation

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IEEE TRANSACTIONS ON COMMUNICATIONS 2021年第1期69卷 501-517页

作者： Tian, Zhuojun Zhang, Zhaoyang Wang, Jue Chen, Xiaoming Wang, Wei Dai, Huaiyu Zhejiang Univ Coll Informat Sci & Elect Engn Hangzhou 310027 Peoples R China Zhejiang Univ Zhejiang Prov Key Lab Informat Proc Commun & Netw Hangzhou 310027 Peoples R China Zhejiang Univ Int Joint Innovat Ctr Haining 314400 Peoples R China NC State Univ Dept Elect & Comp Engn Raleigh NC 27695 USA

In this article, we propose a novel distributed alternating direction method of multipliers (ADMM) algorithm with synergetic communication and computation, called SCCD-ADMM, to reduce the total communication and computation cost of the system. Explicitly, in the proposed algorithm, each node interacts with only part of its neighboring nodes, the number of which is progressively determined according to a heuristic searching procedure, which takes into account both the predicted convergence rate and the communication and computation costs at each iteration, resulting in a trade-off between communication and computation. Then the node chooses its neighboring nodes according to an importance sampling distribution derived theoretically to minimize the variance with the latest information it locally stores. Finally, the node updates its local information with a new update rule which adapts to the number of communication nodes. We prove the convergence of the proposed algorithm and provide an upper bound of the convergence variance brought by randomness. Extensive simulations validate the excellent performances of the proposed algorithm in terms of convergence rate and variance, the overall communication and computation cost, the impact of network topology as well as the time for evaluation, in comparison with the traditional counterparts.

关键词： Alternating direction method of multipliers (ADMM) synergetic communication and computation distributed algorithms

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Building next-generation healthcare systems using distributed machine learning 21

Building next-generation healthcare systems using distribute...

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22nd international conference on distributed computing and networking (ICDCN)

作者： van der Schaar, Mihaela Univ Cambridge Machine Learning Artificial Intelligence & Med Cambridge England Univ Calif Los Angeles Los Angeles CA 90095 USA

ISBN: (纸本)9781450389334

Medicine stands apart from other areas where AI can be applied. While we have seen advances in other fields with lots of data, it is not the volume of data that makes medicine so hard, it is the challenges arising from extracting actionable information from the complexity of the data. It is these challenges that make medicine the most exciting area for anyone who is really interested in the frontiers of machine learning – giving us real-world problems where the solutions are ones that are societally important and which potentially impact on us all. Think Covid 19! In this talk I will show how AI and machine learning are transforming medicine and more generally healthcare and how medicine is driving new advances in machine learning, including new methodologies in automated machine learning, interpretable and explainable machine learning, dynamic forecasting, causal inference and distributed machine learning. I will also discuss our experiences in implementing such AI solutions nationally, in the UK, in order to fight the current Covid 19 pandemic as well as how they can be adapted for international use.

关键词： Healthcare machine learning AI

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6th IEEE international Workshop on Wireless Communications and networking in Extreme Environments (WCNEE) Message from the Workshop Chairs

Proceedings - 18th Annual International Conference on Distri...

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Proceedings - 18th Annual international conference on distributed computing in Sensor Systems, DCOSS 2022 2022年 XX页

作者： Balasubramaniam, Sasitharan Demirors, Emrecan Guan, Zhangyu Sklivanitis, George University of Nebraska-Lincoln United States Northeastern University United States University at Buffalo United States Florida Atlantic University United States

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Computation of Personalized PageRank Using Cayley-Hamilton Theorem

Computation of Personalized PageRank Using Cayley-Hamilton T...

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IEEE international conference on Consumer Electronics-Taiwan (ICCE-TW)

作者： Su-Ling Lee Chien-Cheng Tseng Depart. of Computer Science and Information Engineering Chang-Jung Christian University Tainan Taiwan Depart. of Computer and Communication Engineering National Kaohsiung University of Science and Technology Kaohsiung Taiwan

ISBN: (数字)9798350386844

ISBN: (纸本)9798350386851

Personalized PageRank centrality (PPRC) is an important tool to rank the significance of nodes in a complex network, so it is desirable to develop an efficient calculation algorithm for computing PPRC. In this article, the Cayley-Hamilton theorem is presented to compute PPRC iteratively. Three steps involved are described below: First, it is shown that the PPRC vector can be determined by using matrix inversion. Second, the Cayley-Hamilton theorem is used to compute matrix inversion by a polynomial of graph shift matrix. Third, an iterative method is applied to implement the polynomial of shift matrix in a distributed manner. The main feature of the proposed method is that the PPRC vector can be obtained without error after AM iterations, where N is the total number of nodes of complex network. Finally, the computation of PPRC of a social network is demonstrated to illustrate the effectiveness of the proposed method, and the comparison with conventional iterative method is also made.

关键词： Social networking (online) Complex networks Vectors Polynomials Iterative methods Consumer electronics

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Advancing Trust in the Internet: Blockchain-driven Decentralized VPNs Leveraging Smart Contracts and PoS as Trust Factor Calculators

Advancing Trust in the Internet: Blockchain-driven Decentral...

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Intelligent Systems for Cybersecurity (ISCS), international conference on

作者： Aniruddha Ghosh Sudhanshu Srivastava P Supraja Department of Networking and Communications SRM Institute of Science and Technology Kattankulathur Chennai Tamil Nadu India

ISBN: (数字)9798350375237

ISBN: (纸本)9798350375244

The imperative need for a secure and privacy-focused virtual private network (VPN) solution arises amidst growing concerns about centralized VPN architectures. The limitations of existing VPN models are examined, emphasizing issues related to single points of control, susceptibility to attacks, and potential privacy breaches. To counter these challenges, a decentralized VPN system built upon the blockchain client that supports smart contracts and proof of stake (PoS) is proposed. The blockchain-based architecture is designed to enhance security, transparency, and accountability in VPN operations. By leveraging distributed ledger technology, the proposed system eliminates the risks related to centralized control, offering a more resilient and tamper-resistant infrastructure. Smart contracts and PoS further contribute to the autonomous and trustworthy nature of the decentralized VPN. This contribution aims to advance the current discourse on VPNs by presenting a solution that addresses the shortcomings of traditional models.

关键词： Proof of stake Smart contracts Quality of service User experience Virtual private networks Peer-to-peer computing Cryptocurrency

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