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Comparative Analysis of K-Nearest Neighbor and Decision Tree in Detecting distributed Denial of Service 8

Comparative Analysis of K-Nearest Neighbor and Decision Tree...

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8th international conference on Information and Communication Technology, ICoICT 2020

作者： Ramadhan, Ilham Sukarno, Parman Nugroho, Muhammad Arief Telkom University School of Computing Bandung Indonesia

ISBN: (纸本)9781728161426

distributed Denial of Service (DDoS) attacks are attacks made by several attackers by flooding the victim's device with a packet. the ease of making DDoS attacks has led to an increase of these attacks in network traffic. In contrast, the method of non-machine learning Intrusion Detection System (IDS) is now seen very inaccurate. there is a need then for an IDS method with machine learning (ML) that is more accurate in detecting attacks. Several previous studies have known that the K-Nearest Neighbor (KNN) and Decision Tree (DT) algorithms are two algorithms with high accuracy in detecting DDoS attacks. However, research comparing the two algorithms is not found so far. In this study, a comparative analysis was carried out between the two algorithms. the result of this study showed that DT had a higher accuracy with an accuracy value of 99.91% than KNN which only had an accuracy value of 98.94% in detecting DDoS attacks. © 2020 IEEE.

关键词： Denial-of-service attack

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Intelligent distributed Method to Secure Stored Data in Cloud computing

Intelligent Distributed Method to Secure Stored Data in Clou...

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IEEE UP Section conference on Electrical Computer and Electronics (UPCON)

作者： Siddharth Pokhriyal Rashmi Sharma Krishna Engineering College Ghaziabad U.P India

ISBN: (纸本)9781665409636

computing using cloud services is a new and different paradigm that is manufacturing nice changes to enterprise hardware and code style additionally opening a massive path to the web-based computing services for meeting the varied wants because the cloud is gaining huge advancement cloud knowledge security and privacy of information is turning into an essential issue. If we have a tendency to take into account all the safety problems associated with the cloud then one amongst the main issues is that cloud operators have an opportunity to succeed in the cloud user’s sensitive knowledge. this concern dramatically decreases the usability of cloud computing in several areas, like monetary organizations. this paper concentrates on the difficulty that cloud operators cannot reach the sensitive info of cloud users and thus proposes a new cryptography method by which cloud administrators cannot gain access to the entire information. the proposed approach divides the data into three parts and thus the data is stored in three different distributed clouds due to which cloud operators cannot reach the complete data, they can only gain access to the partial data which is of no use. In this research we will present a solution to the above- stated problem and will try to develop a model which focuses on secure cloud data model with acceptable computation time.

关键词： Cloud computing Processor scheduling Computational modeling distributed databases Turning Data models Scheduling

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Description for Smart Grid: Towards the Ontological Approach 8

Description for Smart Grid: Towards the Ontological Approach

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8th international conference on Information Technology and Multimedia, ICIMU 2020

作者： Yee Chong, Alicia Tang Mahmoud, Moamin A Lim, Fung-Cheng Kasim, Hairoladenan Institute of Informatics and Computing in Energy Universiti Tenaga Nasional Malaysia College of Computing and Informatics Universiti Tenaga Nasional Malaysia

ISBN: (纸本)9781728173108

Smart grids facilitate the use of distributed and renewable resources on the supply side and providing consumers with a range of tailored services on the consumption side. the introduction of energy smart grid in Malaysia allows other stakeholders to reap the potential benefits it offers. However, for the stakeholders to share knowledge and collaborate, a common language that can act as intermediary that allows any application to communicate with each other is needed. Ontologies have been successful in integrating the knowledge required for solving complex problems such as energy management problems. Ontology has become a promising future direction towards devising a context-aware middleware platform for the smart grid. Hence, we propose to develop ontology for energy smart grid to support information integration for existing smart grid applications and to produce an efficient, semantically aware and operationally accurate software environment for managing flexibility in Malaysian energy distribution networks. © 2020 IEEE.

关键词： Ontology

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Topic Modelling Twitterati Sentiments using Latent Dirichlet Allocation during Demonetization

Topic Modelling Twitterati Sentiments using Latent Dirichlet...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： GM Harshvardhan Mahendra Kumar Gourisaria Aanchal Sahu Siddharth Swarup Rautaray Manjusha Pandey School of Computer Engineering KIIT Deemed to be University Bhubaneswar India

ISBN: (纸本)9789380544434;9781728195469

Twitter has surfaced as one of the major social media platforms for sharing political views on pressing issues for the common man. In this paper, we attempt to apply a topic modelling technique, namely Latent Dirichlet Allocation (LDA) on tweets to analyse and come up with pertinent topics with the most relevant words that describe the topics most aptly. the tweets contain the demonetization hashtag to help us understand sentiments of people about demonetization. this technique can be used by analysts across any industry to understand the pertinent topics revolving around any social issue to take further actions in their organizations. Leveraging this sort of statistical topic modelling can be quite useful to researchers to correctly identify primary components of huge textual corpora for any kind of further analysis. the model comes up with very meaningful categorization of topics. Further, we measure the inter-topic distances via multidimensional scaling and review words and topics through metrics such as saliency and relevance.

关键词： Measurement Industries Analytical models Social networking (online) Computational modeling Blogs Pressing

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Ensemble Sentiment Model: Bagging with Linear Discriminant Analysis (BLDA)

Ensemble Sentiment Model: Bagging with Linear Discriminant A...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Dimple Tiwari Bharti Nagpal Ambedkar Institute of Advanced Communication Technologies Research Govt. of NCT of Delhi India

Rapid development in information technology and social media platforms increased the demand for sentiment analysis and modelling. Online reviews contain information about customer choice towards a particular product or a service. Existing approaches of sentiment analysis worked on a single learner or predefined ensemble learner to classify the sentiments. this work proposed a bagging ensemble approach with Linear Discriminant Analysis (BLDA) model for analysing the restaurant reviews. the Linear Discriminant Analysis (LDA) was chosen as a base classifier for each random subset in the bagging meta-estimator model. Our solution mainly focuses on enhancement, standardisation, preprocessing, and performance of sentiment analysis with topic-modelling and ensemble learning. Topic-Modelling is used to find the context of a customer regarding a service. the BLDA model's effectiveness has tested on restaurant reviews with various measurement units as Recall, Precision, F1-Score and ROC-AUC curve. the comparative study with Gaussian Naïve Bayes (GaussianNB) and K-Nearest Neighbor (KNN) present the proposed model's best performance.

关键词： Analytical models Sentiment analysis Measurement units Social networking (online) Computational modeling Linear discriminant analysis Information technology

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Dynamic Load Balance for Hot-spot and Unbalance Region Problems in HBase 8

Dynamic Load Balance for Hot-spot and Unbalance Region Probl...

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8th IEEE international conference on Big Data (Big Data)

作者： Chen, Yibo Xiang, Xing Ling, Xiao Zhang, Xiangchi Wu, Fan Gao, Jianliang State Grid Hunan Elect Power Co Ltd Informat & Commun Branch Changsha Peoples R China Cent South Univ Sch Comp Sci & Engn Changsha Peoples R China

ISBN: (纸本)9781728162515

In distributed storage, dynamic loads usually cause a huge burden such as hot-spot and unblance region problems. In this paper, we focus on the problems of dynamic unbalance loads in distributed database HBase. Firstly, we propose a new intelligent load balance framework to support the collection and analysis of dynamic load information. then, two efficient load balance strategies are proposed to solve hot-spot problem and unbalance region problem. Finally, we evaluate the strategies on HBase using real-world big data on distributed clusters. Compared to the existing balance strategies, the proposed load balance strategies can make more effective use of the resources and reduce the response time for large dynamic loads.

关键词： distributed storage parallel and distributed computing dynamic loads intelligent load balance

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distributed Orchestration of Regression Models Over Administrative Boundaries 21

Distributed Orchestration of Regression Models Over Administ...

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2021 IEEE/ACM 8th international conference on Big Data computing, Applications and Technologies (BDCAT '21)

作者： Menuka Warushavithana Caleb Carlson Saptashwa Mitra Daniel Rammer Mazdak Arabi Jay Breidt Sangmi Lee Pallickara Shrideep Pallickara Department of Computer Science Colorado State University USA Colorado State University USA Department of Statistics Colorado State University USA

ISBN: (纸本)9781450391641

Geospatial data collections are now available in a multiplicity of domains. the accompanying data volumes, variety, and diversity of encoding formats within these collections have all continued to grow. these data offer opportunities to extract patterns, understand phenomena, and inform decision making by fitting models to the data. To ensure accuracy and effectiveness, these models need to be constructed at geospatial extents/scopes that are aligned with the nature of decision-making — administrative boundaries such as census tracts, towns, counties, states etc. this entails construction of a large number of models and orchestrating their accompanying resource requirements (CPU, RAM and I/O) within shared computing clusters. In this study, we describe our methodology to facilitate model construction at scale by substantively alleviating resource requirements while preserving accuracy. Our benchmarks demonstrate the suitability of our methodology.

关键词： workload orchestration spatiotemporal data transfer learning modeling

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A Proactive Data-Parallel Framework for Machine Learning 21

A Proactive Data-Parallel Framework for Machine Learning

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2021 IEEE/ACM 8th international conference on Big Data computing, Applications and Technologies (BDCAT '21)

作者： Guoyi Zhao Tian Zhou Lixin Gao University of Massachusetts Amherst USA University of Massachusetts Amherst USA

ISBN: (纸本)9781450391641

Data parallel frameworks become essential for training machine learning models. the classic Bulk Synchronous Parallel (BSP) model updates the model parameters through pre-defined synchronization barriers. However, when a worker computes significantly slower than other workers, waiting for the slow worker will lead to excessive waste of computing resources. In this paper, we propose a novel proactive data-parallel (PDP) framework. PDP enables the parameter server to initiate the update of the model parameter. that is, we can perform the update at any time without pre-defined update points. PDP not only initiates the update but also determines when to update. the global decision on the frequency of updates will accelerate the training. We further propose asynchronous PDP to reduce the idle time caused by synchronizing parameter updates. We theoretically prove the convergence property of asynchronous PDP. We implement a distributed PDP framework and evaluate PDP with several popular machine learning algorithms including Multilayer Perceptron, Convolutional Neural Network, K-means, and Gaussian Mixture Model. Our evaluation shows that PDP can achieve up to 20X speedup over the BSP model and scale to large clusters.

关键词： stragglers machine learning gradient descent expectation-maximization asynchronous distributed computation

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Classification of Disaster Specific Tweets - A Hybrid Approach

Classification of Disaster Specific Tweets - A Hybrid Approa...

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international conference on computing for Sustainable Global Development (INDIACom)

作者： Siddu P. Algur Venugopal S School of Mathematics and Computing Sciences Rani Channamma University Belagavi Karnataka India

ISBN: (纸本)9789380544434;9781728195469

the pervasive increase in tweeting habits of netizens has thrown open a plethora of avenues for Twitter data exploration. Users are tweeting on an innumerable number of topics with politics, entertainment, sports, and technology-related issues comprising the bulk of the tweets. Identifying tweets related to disasters - natural and manmade, among the huge repository of data posted every day on Twitter is a stimulating task. In this work, we propose a hybrid classification model to identify and distinguish the tweets attributed to disasters. An attempt is made to extract the disaster-related tweets by identifying the disaster keywords in the tweets, the tweets are then transformed into vectors using the count vectorization and Term Frequency-Inverse Document Frequency TF-IDF models. We then prepare a repository of a dataset based on unigram, bigram, and n-gram and evaluate multiple associations among the contents of a repository. Classification algorithms Naive Bayes, Logistic Regression, J48, Random Forest, and SVM are applied to evaluate their performances on our hybrid classification data model. the classification results are compared and evaluated to have an inkling about the suitability of classification with the proposed hybrid model approach.

关键词： Support vector machines Analytical models Social networking (online) Computational modeling Blogs Data models Classification algorithms

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Machine Learning-Based, networking and computing Infrastructure Resource Management 17th

Machine Learning-Based, Networking and Computing Infrastruct...

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17th IFIP WG 12.5 international conference on Artificial Intelligence Applications and Innovations (AIAI) / 6th 5G-PINE Workshop / AI-BIO Workshop / DAAI Workshop / DARE Workshop / EEAI Workshop / 10th MHDW Workshop

作者： Chochliouros, Ioannis P. Kostopoulos, Alexandros Payaro, Miquel Verikoukis, Christos di Vimercati, Sabrina De Capitani Vinogradov, Evgenii Ranjbar, Vida Vardakas, John Rahman, Md Arifur Soumplis, Polyzois Varvarigos, Emmanuel Hellen Telecommun Org OTE SA 99 Kifissias Ave Athens 15124 Greece Ctr Tecnol Telecomunicac Catalunya CTTC CERCA Barcelona Spain Univ Milan Milan Italy Katholieke Univ Leuven Leuven Belgium Iquadrat Informat SL Barcelona Spain IS Wireless Warsaw Poland Inst Commun & Comp Syst ICCS Athens Greece

ISBN: (纸本)9783030791599;9783030791575;9783030791568

5G mobile networks will be soon available to handle all types of applications and to provide service to massive numbers of users. In this complex and dynamic network ecosystem, end-to-end performance analysis and optimization will be key features in order to effectively manage the diverse requirements imposed by multiple vertical industries over the same shared infrastructure. To enable such a vision, the MARSAL project [ 1] targets the development and evaluation of a complete framework for the management and orchestration of network resources in 5G and beyond by utilizing a converged optical-wireless network infrastructure in the access and fronthaul/midhaul segments. At the network design domain, MARSAL targets the development of novel cell-free-based solutions. Namely, scalable and cost-efficient wireless access points deployment will be achieved by exploiting the distributed cell-free concept combined with wireless and wired serial fronthaul approaches. We will target the inclusion of these innovative functionalities in the O-RAN project. In parallel, in the fronthaul/midhaul segments MARSAL aims to radically increase the flexibility of optical access architectures for Beyond-5G cell site connectivity via different levels of fixed-mobile convergence. In the network and service management domain, the design philosophy of MARSAL is to provide a comprehensive framework for the management of the entire set of communication and computational network resources by exploiting novel ML-based algorithms of both edge and midhaul data centers, by incorporating the Virtual Elastic Data Centers/Infrastructures paradigm. Finally, at the network security domain, MARSAL aims to introduce mechanisms that provide privacy and security to application workload and data, targeting to allow applications and users to maintain control over their data when relying on the deployed shared infrastructures, while AI and Blockchain technologies will be developed in order to guarantee a secured

关键词： Cell-free distributed cloud Network automation Machine learning Secure multi-tenancy

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