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Analysis of Hadoop log file in an environment for dynamic detection of threats using machine learning

Measurement: Sensors 2022年 24卷

作者： Naidu, K Bapayya Ravi Prasad, B. Hassen, Samar Mansour Kaur, Chamandeep Al Ansari, Mohammed Saleh Vinod, R. Nivetha, M. Kiran Bala, B. Surampalem Jawaharlal Nehru Technological University Kakinada India CSE Dept Marri Laxman Reddy Institute of Technology and Management Hyderabad500043 India Computer Science JazanUnivertsity Jazan Saudi Arabia Jazan University Saudi Arabia Jazan Saudi Arabia College of Engineering Department of Chemical Engineering University of Bahrain Bahrain School of Contemporary Knowledge Systems Chinmaya Vishwavidyapeeth Cochin Kerala India Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Trichy India Head of the Department Department of Artificial Intelligence and Data Science K.Ramakrishnan College of Engineering Tamil Nadu Trichy India

Log files are a significant piece of records that once properly analyzed, can yield helpful information. The usage of cloud services for log analysis is almost inevitable due to the greater rate of production and quantity of hardware and sensors that produce logs. The idea is to adopt machine learning mechanisms to "understand" what such a test suite's "anticipated" behavior is. This implies the system needs to be able to learn the difference among a Hadoop log file that contains a threat that does not really, based on past patterns. Many programs that really can effectively monitor the Hadoop cluster have been developed. The bulk of these tools collect relevant information from every cluster node and send it to be processed. The majority of these diagnostic tools were post-execution techniques commonly. This paper gives an experimental evaluation of the various log analysts used in Hadoop for error monitoring and detection. MapReduce is configured using these files. The algorithms were tested on a parser set of labelled log files, and their efficiency was assessed by looking for abnormal events in the Hadoop log files and executing an experimentation using the methods. The algorithms used were Term Frequency Inverse Document Frequency, Random Forest, and Local Outlier Factor. Later, the clustering with K-Means and Principal component analysis is utilized to extract certain relevant details from the data, monitor groups of bits of data to find aberrant events. The Term Frequency Inverse Document Frequency strategy surpassed the other two techniques in predicting threat occurrences in data, according to the findings. The findings would help developers in locating abnormal occurrences without having to go through the Hadoop log file row by row individually. The framework generates events that behave in a different way than the rest of such events in the log and leads the mechanism to fail to work. © 2022 The Authors

关键词： Principal component analysis

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Automatic Textual Explanations of Concept Lattices

arXiv

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arXiv 2023年

作者： Hirth, Johannes Horn, Viktoria Stumme, Gerd Hanika, Tom Knowledge & Data Engineering Group University of Kassel Germany Partizipative IT-Gestaltung University of Kassel Germany Interdisciplinary Research Center for Information System Design University of Kassel Germany School of Library and Information Science Humboldt-Universität zu Berlin Berlin Germany Institute of Computer Science University of Hildesheim Germany

Lattices and their order diagrams are an essential tool for communicating knowledge and insights about data. This is in particular true when applying Formal Concept Analysis. Such representations, however, are difficult to comprehend by untrained users and in general in cases where lattices are large. We tackle this problem by automatically generating textual explanations for lattices using standard scales. Our method is based on the general notion of ordinal motifs in lattices for the special case of standard scales. We show the computational complexity of identifying a small number of standard scales that cover most of the lattice structure. For these, we provide textual explanation templates, which can be applied to any occurrence of a scale in any data domain. These templates are derived using principles from human-computer interaction and allow for a comprehensive textual explanation of lattices. We demonstrate our approach on the spices planner data set, which is a medium sized formal context comprised of fifty-six meals (objects) and thirty-seven spices (attributes). The resulting 531 formal concepts can be covered by means of about 100 standard *** Codes 06A15 03G10 68T30 68T27 Copyright © 2023, The Authors. All rights reserved.

关键词： Formal concept analysis

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Author Index Volume 7 (2011)

引用

Multiagent and Grid Systems 2011年第6期7卷 311-313页

作者： [ Systems Research Institute Polish Academy of Sciences Warsaw and University of Gdansk Warsaw Poland Systems Research Institute Polish Academy of Sciences Warsaw and Management Academy Warsaw Poland Software Engineering Department Faculty of Automatics Computers and Electronics University of Craiova Bvd.Decebal Craiova Romania University of Duisburg-Essen Institute for Computer Science and Business Information Systems (ICB) Practical Computer Science Data Management Systems and Knowledge Representation Essen Germany

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Elevating Digital Experiences by Enhancing Service Delivery through Revolutionary Cloud Services and Applications

Elevating Digital Experiences by Enhancing Service Delivery ...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： V Asha S Sreekanth Ginni Nijhawan K. Varada Rajkumar Monica Garg Ali Abduhussien Hameed Department of Computer Applications New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

This research introduces an approach named “Cloud-Driven Personalized Experience Enhancement” to satisfy the escalating demand for enhanced digital connectivity. Via cutting-edge cloud services and applications, the CPExE framework provides a groundbreaking solution for digital platforms. The framework is built upon three core components: Together with the Quantum-Inspired data Analytics and Evaluation (QIDAE) and Personalized Content Recommendations (PRA), Dynamic Resource Allocation and Adaptation (DRAA) completes the framework. By leveraging quantum-inspired approaches, QIDAE analyzes vast datasets with outstanding effectiveness and exactness improving processes for data handling and personalization. This strategy allows for swift detection of patterns while making certain that data-driven understandings are both on time and precise. By changing cloud resources according to user requests the DRAA module guarantees uninterrupted access to computing power and data storage. By managing resources effectively in real-time it decreases latency and improves service speeds. By using complex algorithms to assess user habits and choices the PRA system boosts the digital experience. PRA uses both content-focused filtering and collaborative screening strategies to provide users with engaging content that matches their unique preferences. By applying sophisticated analytics and resource management methods with content personalization CPExE boosts the user experience and develops cloud technology efficiency. This framework signifies an important progress toward developing dynamic and tailored online environments.

关键词： Heating systems Histograms data analysis Heuristic algorithms Scalability Memory Dynamic scheduling Real-time systems User experience Resource management

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Intelligent Gates Exploring the Massiveness of Random Access Points in NGMA

Intelligent Gates Exploring the Massiveness of Random Access...

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International Conference on Contemporary Computing and Informatics (IC3I)

作者： Sunil Prashanth Kumar P. Aswini H Pal Thethi K Smitha R. Akhilesh Reddy Ali Albawi Department of Mechanical Engineeering New Horizon College of Engineering Bangalore India Computer Science and Engineering (Data Science) Institute of Aeronautical Engineering Dundigal Hyderabad India Lovely Professional University Phagwara India Lloyd Law College Plot No. 11 Knowledge Park II Greater Noida Uttar Pradesh Department of CSE-AI&ML MLR Institute of Technology Hyderabad Telangana India Radiology Techniques Department College of Medical Technology The Islamic University Najaf Iraq

ISBN: (数字)9798350350067

ISBN: (纸本)9798350350074

With the rapid progress of Next-Generation Mobile Architecture come several obstacles in improving user experience and network performance. Active methods frequently face issues in allocating network resources correctly. Consequently this can lead to mismanagement or poor resource utilization affecting how users experience the network. As access requirements increase with the growth of architecture complexity protecting personal data is becoming more challenging. To tackle these issues the Dynamic Access Point Management (DAPM) framework shows great potential as it delivers a creative strategy for resource allocation and network optimization. This model modifies standard resource management practices to become sharper and more adaptable. Thorough examination is needed to confirm DAPM’s superiority over standard methods regarding the resolution of these problems comprehensively. This research will examine the gains in network efficiency and security outcomes from the sophisticated models of DAPM. The detailed methodology seeks to construct a network framework that is both dependable and secure and will boost NGMA’s operational effectiveness and durability.

关键词： data privacy Adaptation models Aerodynamics Dynamic scheduling Throughput User experience Stability analysis Security Resource management Standards

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ARCH-COMP20 category report: Continuous and hybrid systems with nonlinear dynamics 7th

ARCH-COMP20 category report: Continuous and hybrid systems w...

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7th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH 2020

作者： Geretti, Luca Dit Sandretto, Julien Alexandre Althoff, Matthias Benet, Luis Chapoutot, Alexandre Chen, Xin Collins, Pieter Forets, Marcelo Freire, Daniel Immler, Fabian Kochdumper, Niklas Sanders, David P. Schilling, Christian Department of Computer Science University of Verona Verona Italy ENSTA Paris Institut Polytechnique de Paris Palaiseau France Technische Universität München Munich Germany Mexico University of Dayton DaytonOH United States Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Universidad de la República Montevideo Uruguay Computer Science Department Carnegie Mellon University United States Mexico City Mexico Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States IST Austria Klosterneuburg Austria

We present the results of a friendly competition for formal verification of continuous and hybrid systems with nonlinear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2020. This year, 6 tools Ariadne, CORA, DynIbex, Flow*, Isabelle/HOL, and JuliaReach (in alphabetic order) participated. These tools are applied to solve reachability analysis problems on six benchmark problems, two of them featuring hybrid dynamics. We do not rank the tools based on the results, but show the current status and discover the potential advantages of different tools. © 2020 EasyChair. All rights reserved.

关键词： Hybrid systems

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ARCH-COMP21 Category Report: Continuous and Hybrid Systems with Nonlinear Dynamics 8th

ARCH-COMP21 Category Report: Continuous and Hybrid Systems w...

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8th International Workshop on Applied Verification of Continuous and Hybrid Systems, ARCH 2021

作者： Geretti, Luca Sandretto, Julien Alexandre Dit Althoff, Matthias Benet, Luis Chapoutot, Alexandre Collins, Pieter Duggirala, Parasara Sridhar Forets, Marcelo Kim, Edward Linares, Uziel Sanders, David P. Schilling, Christian Wetzlinger, Mark Department of Computer Science University of Verona Verona Italy ENSTA Paris Institut Polytechnique de Paris Palaiseau France Technische Universität München Munich Germany Mexico Department of Data Science and Knowledge Engineering Maastricht University Maastricht Netherlands Department of Computer Science University of North Carolina at Chapel Hill United States Universidad de la República Montevideo Uruguay Mexico City Mexico Department of Mathematics Massachusetts Institute of Technology CambridgeMA United States University of Konstanz Konstanz Germany Aalborg University Aalborg Denmark

We present the results of a friendly competition for formal verification of continuous and hybrid systems with nonlinear continuous dynamics. The friendly competition took place as part of the workshop Applied Verification for Continuous and Hybrid Systems (ARCH) in 2021. This year, 5 tools Ariadne, CORA, DynIbex, JuliaReach and Kaa (in alphabetic order) participated. These tools are applied to solve reachability analysis problems on five benchmark problems, two of them featuring hybrid dynamics. We do not rank the tools based on the results, but show the current status and discover the potential advantages of different tools. © 2021, EasyChair. All rights reserved.

关键词： Hybrid systems

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CEPV: A Tree Structure Information Extraction and Visualization Tool for Big knowledge Graph

CEPV: A Tree Structure Information Extraction and Visualizat...

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IEEE International Conference on Big knowledge (ICBK)

作者： Shaojing Sheng Peng Zhou Xindong Wu Key Laboratory of Knowledge Engineering with Big Data Heifei Unversity of Technology Ministry of Education Heifei China School of Computer Science and Information Engineering Heifei University of Technology Heifei China School of Computer Science and Technology Anhui University Heifei China Mininglamp Academy of Sciences Mininglamp Technology Beijing China Institute of Big Konowledge Science Heifei University of Technology Heifei China

A large amount of data with rich semantic and structural information has been accumulated in many real-world applications. In order to effectively describe the concepts and connections in these data sets, knowledge graph was proposed as a tool to handle it. The genealogy is a typical tree structure data and can be stored in the knowledge graph. However, due to the complexity and increasing volume of the data, how to efficiently extract and visualize the customized information from the big knowledge graph is hence a challenge and worthy of in-depth study. Motivated by this, we propose a novel user-specified information extraction and visualization tool, named CEPV (the Customized information Extracting, Processing and Visualization tool), for converting the big graph structure data into a specified tree structure display. The main steps of CEPV are as follows: firstly, according to the requirements of users, extracting the specified data from the massive, complex, heterogeneous data as fewer times as possible, which can reduce the frequency of database access and improve the overall efficiency of the algorithm. Secondly, the fault tolerance mechanism and attribute judgment rules are executed to ensure the correctness during the data processing. Finally, the processed data with a complex relationship is presented to the user in multiple visualization models. The high availability and effectiveness of our proposed tool is verified on a big knowledge graph dataset.

关键词： data visualization data mining Tools databases data processing Fault tolerance Fault tolerant systems

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LI-ideal Spaces of Lattice Implication Algebra

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Chinese Quarterly Journal of Mathematics 2007年第4期000卷

作者： CHEN Shu-wei JIANG Bao-qing YANG Xiao-wei School of Electrical Engineering Zhengzhou University ghengzhou 450001China Institute of Data & Knowledge Engineering Henan UniversityKaifeng 475001China Institute of Data & Knowledge Engineering Henan UniversityKaifeng 475001China Department of Mathe matics Southwest Jiaotong UniversityChengdu 610031China

In this paper,a topological space based on LI-ideals of a lattice implication algebra is constructed,and its topological properties,such as separability,compactness and connectedness are discussed.

关键词：晶格代数拓扑空间拓扑特性理想空间

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A Survey on Fairness in Large Language Models

arXiv

引用

arXiv 2023年

作者： Li, Yingji Du, Mengnan Song, Rui Wang, Xin Wang, Ying College of Computer Science and Technology Jilin University Changchun130012 China Department of Data Science New Jersey Institute of Technology Newark United States School of Artificial Intelligence Jilin University Changchun130012 China Key Laboratory of Symbol Computation and Knowledge Engineering Jilin University Ministry of Education Changchun130012 China

Large Language Models (LLMs) have shown powerful performance and development prospects and are widely deployed in the real world. However, LLMs can capture social biases from unprocessed training data and propagate the biases to downstream tasks. Unfair LLM systems have undesirable social impacts and potential harms. In this paper, we provide a comprehensive review of related research on fairness in LLMs. Considering the influence of parameter magnitude and training paradigm on research strategy, we divide existing fairness research into oriented to medium-sized LLMs under pre-training and fine-tuning paradigms and oriented to large-sized LLMs under prompting paradigms. First, for medium-sized LLMs, we introduce evaluation metrics and debiasing methods from the perspectives of intrinsic bias and extrinsic bias, respectively. Then, for large-sized LLMs, we introduce recent fairness research, including fairness evaluation, reasons for bias, and debiasing methods. Finally, we discuss and provide insight on the challenges and future directions for the development of fairness in LLMs. Copyright © 2023, The Authors. All rights reserved.

关键词： Computational linguistics

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