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14th International Conference on Information and Communication systems, ICICS 2023

作者： Khiat, Abdelhamid Research Center on Scientific and Technical Information Networks and Distributed Systems Division Algiers Algeria

ISBN: (纸本)9798350307870

Cloud data centers, comprising a diverse set of heterogeneous resources working collaboratively to achieve high-performance computing, face the challenge of resource dynamism, where performance fluctuates over time. This dynamism poses complexities in task scheduling, warranting further research on the resilience of existing static task scheduling algorithms when deployed in dynamic cloud environments. This study adapts three well-known task scheduling algorithms to the cloud computing context and conducts a comprehensive comparison to assess their resilience to dynamic conditions. The evaluation, employing simulation techniques, analyzes total energy consumption and total response time as key metrics. The results offer detailed insights into the effectiveness of the adapted algorithms, providing valuable guidance for optimizing task scheduling in dynamic cloud data centers. © 2023 IEEE.

关键词： Energy utilization

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HTTP Flood DOS Attack Detection on Big Data Using Data Mining 1

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13th International Conference on Information systems and Advanced Technologies, ICISAT 2023

作者： Haddadi, Mohamed Khiat, Abdelhamid Abidi, Yasmina Derradji, Yaakoub Department of Commercial Sciences Faculty of Economic Commercial and Management Sciences M’hamed BOUGARA University of Boumerdes Independence Avenue Boumerdes35000 Algeria Networks and Distributed Systems Division Research Center on Scientic and Technical Information Ben Aknoun Algeria Department of Computer Science Faculty of Sciences University of Ferhat Abbas Setif1 Setif Algeria

ISBN: (数字)9783031605918

ISBN: (纸本)9783031605901

Nowadays, increasing use of Internet connection, security becomes a huge challenge for individuals as well as governments and organizations. Therefore, in the last decade, the world is moving towards green computing in the purpose either to store energy or to decrease operational costs. So, this new technology uses web servers to provide web applications to end user. Generally, these web servers become unavailable because of HTTP flood DoS attack. This paper applied data mining techniques such as WEKA and TANAGRA tools for data preprocessing in the aim to get a consistent data in one hand, and for classifying the traffic in normal behavior or anomaly using like J48, Random tree, SMO, Naïve bayes, IBK, and combined classifier in the other hand. For this aim, a well-known big data set is used which called http_csic_2010_full dataset. Results show that the two data mining tools are well close with all the classifiers, just MLP in WEKA, and CVM in TANAGRA they didn’t perform well in terms of detection accuracy. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： HTTP

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Optimizing Cloud Energy Consumption Using Static Task Scheduling Algorithms: A Comparative Study

Optimizing Cloud Energy Consumption Using Static Task Schedu...

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International Conference on Information and Communication systems (ICICS)

作者： Abdelhamid Khiat Networks and Distributed Systems Division Research Center on Scientific and Technical Information Algiers Algeria

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Interrc: An inter-resources collaboration heuristic for scheduling independent tasks on heterogeneous distributedenvironments 25

Interrc: An inter-resources collaboration heuristic for sche...

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25th International Conference on Soft Computing, MENDEL 2019

作者： Khiat, Abdelhamid Tari, Abdelkamel Networks and Distributed Systems Division Research Center on Scientific and Technical Information Algeria Faculty of Exact Sciences University of Bejaia Algeria

The independent task scheduling problem in distributed computing environments with makespan optimization as an objective is an NP-Hard problem. Consequently, an important number of approaches looking to approximate the optimal makespan in reasonable time have been proposed in the literature. In this paper, a new independent task scheduling heuristic called InterRC is presented. The proposed InterRC solution is an evolutionary approach, which starts with an initial solution, then executes a set of iterations, for the purpose of improving the initial solution and close the optimal makespan as soon as possible. Experiments show that InterRC obtains a better makespan compared to the other efficient algorithms. © 2019, Brno University of Technology. All rights reserved.

关键词： Optimization

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Intrusion Detection systems using Data Mining Techniques: A comparative study 11

Intrusion Detection Systems using Data Mining Techniques: A ...

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11th International Conference on Information systems and Advanced Technologies, ICISAT 2021

作者： Haddadi, Mohamed Khiat, Abdelhamid Bahnes, Nacera Université m'Hamed Bougara de Boumerdes Faculté des Sciences Économiques Commerciales et des Sciences de Gestion Département des Sciences Commerciales Avenue de l'indépendence Boumerdes35000 Algeria Research Center on Scientific and Technical Information Networks and Distributed Systems Division Algeria Computer Science Department University of Oran 1 Ahmed Ben Bella PO Box 1524 El M'naouar Oran Algeria Computer Science Department Faculty of Exact Sciences and Computer Science University of Abdelhamid Ibn Badis Mostaganem27000 Algeria

ISBN: (纸本)9781665478243

Data mining tools are widely used in computer networks. The well-known and mostly used tools to secure computers and network systems are WEKA and TANAGRA. The purpose of this study is to compare these two tools in terms of detection accuracy and computation time. This comparison was conducted using a well-known NSL-KDD dataset. Experiments show that TANAGRA achieves better results than WEKA in detection accuracy. But, TANAGRA is competitive with WEKA in terms of computation time. © 2021 IEEE.

关键词： Data mining

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Intrusion Detection systems using Data Mining Techniques: A comparative study

Intrusion Detection Systems using Data Mining Techniques: A ...

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Information systems and Advanced Technologies (ICISAT), International Conference on

作者： Mohamed Haddadi Abdelhamid Khiat Nacera Bahnes Faculté des sciences économiques commerciales et des sciences de gestion Université M’hamed Bougara de Boumerdes Avenue de l’indépendence Boumerdes Algérie Networks and Distributed Systems Division Research Center on Scientific and Technical Information Algeria Industrial Computing and Networks Laboratory (RIIR) University of Oran 1 Ahmed Ben Bella PO Box 1524 El M’naouar Oran Algeria Faculty of Exact Sciences and Computer Science University of Abdelhamid Ibn Badis Mostaganem Algeria

ISBN: (纸本)9781665478250

关键词： Training Java Codes Computer hacking Intrusion detection Computer networks Data mining

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Large margin multi-modal multi-task feature extraction for image classification

arXiv

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arXiv 2019年

作者： Luo, Yong Wen, Yonggang Tao, Dacheng Gui, Jie Xu, Chao School of Electronics Engineering and Computer Science Peking University Beijing China Division of Networks and Distributed Systems School of Computer Engineering Nanyang Technological University Singapore Centre for Quantum Computation & Intelligent Systems Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia Centre for Quantum Computation & Intelligent Systems Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Street UltimoNSW2007 Australia Hefei Institute of Intelligent Machines Chinese Academy of Sciences Hefei230031 China Center for Research on Intelligent Perception and Computing National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences Beijing100190 China

The features used in many image analysis-based applications are frequently of very high dimension. Feature extraction offers several advantages in high-dimensional cases, and many recent studies have used multi-task feature extraction approaches, which often outperform single-task feature extraction approaches. However, most of these methods are limited in that they only consider data represented by a single type of feature, even though features usually represent images from multiple modalities. We therefore propose a novel large margin multi-modal multi-task feature extraction (LM3FE) framework for handling multi-modal features for image classification. In particular, LM3FE simultaneously learns the feature extraction matrix for each modality and the modality combination coefficients. In this way, LM3FE not only handles correlated and noisy features, but also utilizes the complementarity of different modalities to further help reduce feature redundancy in each modality. The large margin principle employed also helps to extract strongly predictive features so that they are more suitable for prediction (e.g., classification). An alternating algorithm is developed for problem optimization and each sub-problem can be efficiently solved. Experiments on two challenging real-world image datasets demonstrate the effectiveness and superiority of the proposed method. Copyright © 2019, The Authors. All rights reserved.

关键词： Feature extraction

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