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Research and Implementation of Log-Based Anomaly Detection Platform Based on ELK+Kafka 10

Research and Implementation of Log-Based Anomaly Detection P...

引用

10th International Conference on computer and Communications, ICCC 2024

作者： Xu, Shuai Meng, Ziyang Wang, Han China Academy of Engineering Physics Institute of Computer Application Mianyang China

Anomaly detection is vitally essential to modern large-scale distributed systems. Over the years,many log-based anomaly detection methods have been proposed. Logs record the real-time running information of processes in distributed systems and show the running status of the processes. However, when the reliability issues that happen in distributed systems, operators often inspect the logs manually with multi-rule matching and multi-keyword search. Additionally, The increasing scale and complexity of distributed systems, make the volume of logs explode, which renders the infeasibility of manual inspection. To reduce manual effort, we provide a log-based anomaly detection platform, which is based on ELK (Elasticsearch, Logstash, Kibana), Kafka and deep learning-based methods. Our proposed platform is evaluated using two different publicly-available production log datasets, and our results demonstrate that it achieves the F1-score of 0.792 and 0.829, respectively, outperforming the manually inspecting log anomalies. © 2024 IEEE.

关键词： Deep learning

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A SSCPS safety assessment method based on mission risk

A SSCPS safety assessment method based on mission risk

引用

IEEE International Conference on Software Quality, Reliability and Security Companion (QRS-C)

作者： Yafeng Shen Lianzhi Qi Shiyong Li Siyou Ma Fei Deng Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350365658

ISBN: (纸本)9798350365665

Cyber physical system (CPS) is a complex system that deeply integrates computing, network communication and control equipment. It has broad application prospects in aerospace, industrial production, transportation and other fields, and it also faces very severe security challenges. Especially for some safety-critical CPS (SSCPS), security issues will have serious consequences. How to carry out security assessment for safety-critical CPS systems has always been a hot issue in CPS research. This paper proposes a task risk-based SSCPS security assessment method. By decomposing the key tasks of the SSCPS system, identifying system risk elements based on task characteristics, and establishing the impact relationship between tasks and risk factors such as assets, vulnerabilities, and threats, construct a security assessment index system based on mission risk, and propose a security fusion method to achieve multilevel and multidimensional security assessment of SSCPS.

关键词： Transportation Software quality Time measurement Software reliability Safety Security Indexes Data mining Software measurement Standards

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Parallel Query Processing in Database on Multicore System

Parallel Query Processing in Database on Multicore System

引用

Electronic Information engineering and computer Science (EIECS), 2021 International Conference on

作者： Yaohui Zhang Zhen Liu Guiyu Lai Wenjie Gao Institute of Computer Application Chinese Academy of Engineering Physics Minayang China

ISBN: (数字)9798331531409

ISBN: (纸本)9798331531416

The growth in data volume poses a challenge to query processing. On the other hand, advancements in technology have made multicore CPUs available in many inexpensive servers. so parallel query based on multicore CPU is undoubtedly a solution for query acceleration and cost reduction. In this paper, a parallel query method is proposed and implemented, which rewrites the query execution plan and replaces the original serial scanning operator with a parallel driver operator under the premise that most of the operators do not change, so as to maximize the utilization of CPU and disk resources. Experiments show that it can achieve approximately a 2x speed increase in query processing.

关键词： Hands computers computer science Costs Multicore processing Databases Query processing Instruction sets Central Processing Unit Servers

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QVT-Based SysML v2 Model Version Difference Transformation

QVT-Based SysML v2 Model Version Difference Transformation

引用

Chinese Automation Congress (CAC)

作者： Chen Zhou Ling Li Qiang Ren Zixin Mu Bing Yu Baoran An Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350368604

ISBN: (纸本)9798350368611

In order to address the issue of model version incompatibility in supporting the implementation of Model-Based Systems engineering (MBSE) with SysML v2, this paper first elucidates a feasible engineering modelling method based on the SysML v2 specification. Subsequently, leveraging model transformation concepts, a v2 model version conversion method based on the QVT Relations Language is proposed. Various potential compatibility scenarios and implementation paths are meticulously analysed. On this foundation, a design scheme for program modules ensuring version full transitivity is presented. Finally, the proposed methods and programs are validated through case studies involving version conversion under two different scenarios. The results indicate that the proposed approach effectively resolves SysML v2 model version compatibility issues arising from changes in syntax or semantic patterns.

关键词： Analytical models Automation Semantics Syntactics

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PMT-IQA: Progressive Multi-task Learning for Blind Image Quality Assessment 20th

PMT-IQA: Progressive Multi-task Learning for Blind Image Qu...

引用

20th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2023

作者： Pan, Qingyi Guo, Ning Qingge, Letu Zhang, Jingyi Yang, Pei Department of Computer Technology and Application Qinghai University Xining China Center for Statistical Science and Department of Industrial Engineering Tsinghua University Beijing China Department of Computer Science North Carolina A and T State University Greensboro United States

ISBN: (纸本)9789819970247

Blind image quality assessment (BIQA) remains challenging due to the diverse types of distortion and variable image content, which complicates the distortion patterns crossing different scales and aggravates the difficulty of the regression problem for BIQA. However, existing BIQA methods often fail to consider multi-scale distortion patterns and image content, and there has limited research on improving the performance of quality regression models through specific learning strategies. In this paper, we propose a simple yet effective Progressive Multi-Task Image Quality Assessment (PMT-IQA) model, which contains a multi-scale feature extraction module (MS) and a progressive multi-task learning module (PMT), to help the model learn complex distortion patterns and better optimize the regression issue to align with the law of human learning process from easy to hard. To verify the effectiveness of the proposed PMT-IQA model, we conduct experiments on four widely used public datasets, and the experimental results indicate that the performance of PMT-IQA is superior to the comparison approaches, and both MS and PMT modules improve the model’s performance. The source code for this study is available at https://***/pqy000/PMT-IQA. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2024.

关键词： Image enhancement

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Utilizing Random Forests for High-Accuracy Classification in Medical Diagnostics 7

Utilizing Random Forests for High-Accuracy Classification in...

引用

7th International Conference on Contemporary Computing and Informatics, IC3I 2024

作者： Maindola, Meenakshi Al-Fatlawy, Ramy Riad Kumar, Rakesh Boob, Nandini Shirish Sreeja, S.P. Sirisha, N. Srivastava, Abhinav Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India The Islamic university College of technical engineering Department of computers Techniques engineering Najaf Iraq Gla University Department of Computer Engineering & Applications Mathura India Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamilnadu Chennai India New Horizon College of Engineering Master of Computer Application Bangalore India Mlr Institute of Technology Department of Computer Science and Engineering Telangana Hyderabad India Fs University Shikohabad Firozabad India

ISBN: (纸本)9798350350067

In recent years, medical diagnostics has increasingly relied on machine learning techniques to improve accuracy and efficiency. Among these, the Random Forest algorithm has emerged as a powerful tool for classification tasks due to its robustness and ability to handle complex datasets. This paper explores the application of Random Forests in medical diagnostics, specifically focusing on enhancing the accuracy of disease classification. By analyzing large, heterogeneous medical datasets, we demonstrate how Random Forests can outperform traditional classification algorithms, offering high precision in identifying and classifying diseases. Key features, such as decision tree ensemble methods and bootstrap aggregation, are utilized to minimize overfitting and improve model generalization. The research compares Random Forests to other machine learning models, such as Neural Networks and Support Vector Machines, and finds that Random Forests perform better in terms of dependability and classification accuracy. We further emphasise that the technique can scale to big datasets and that it can manage missing data. The findings show that Random Forests might be a useful tool for accurate and early diagnosis when incorporated into clinical procedures. The findings of this study highlight the promise of machine learning, and Random Forests in particular, to revolutionise healthcare diagnostics and enhance patient results. © 2024 IEEE.

关键词： Random forests

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An Integrated Learning Model for Online Performance Prediction Using LightGBM and Tabnet Fusion Model for Large Laser Facilities

An Integrated Learning Model for Online Performance Predicti...

引用

International Symposium on computer Science and Intelligent Controls (ISCSIC)

作者： Zizhou He Wenwen Shen Suicheng Li Jun Luo China Academy of Engineering Physics Institute of Computer Application Mianyang China

ISBN: (数字)9798350380286

ISBN: (纸本)9798350380293

Performance prediction is a crucial aspect of ensuring optimal facility operation and maintenance across diverse fields. Although deep learning algorithms have found many applications in several industries, their applicability in tabular data mining that integrates multiple parameter types has remained somewhat limited. To address this gap, this research proposes the introduction of an integrated learning model premised on practical business scenarios, featuring data cleaning, and well-grounded feature engineering methods. Specifically, the study employs the LightGBM and Tabnet fusion model to overcome inefficiencies associated with traditional integrated learning techniques like XGBoost and GBDT. Furthermore, a recursive feature elimination approach is implemented to reduce redundancy during model construction, culminating in the improved online performance prediction. The model developed in this study has demonstrated a 32.66% reduction in prediction deviation compared to traditional manual prediction methods in simulations on large laser facilities. These findings underscore the significance of this integrated learning model in enhancing predictive accuracy, which is a critical component of optimal facility management.

关键词： Laser theory Computational modeling Stacking Termination of employment Predictive models Laser modes Prediction algorithms Preamplifiers Maintenance Laser fusion

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CKD NET: Chronic Kidney Disorders Distinction Model

CKD NET: Chronic Kidney Disorders Distinction Model

引用

Intelligent Data Communication Technologies and Internet of Things (IDCIoT), International Conference on

作者： Arpana Prasad V. Asha A.P. Nirmala Akash V Chethan S G Chintan S Naik Master of Computer Application New Horizon College of Engineering Bengaluru India

Habitual Renal complaint is a condition that causes a progressive drop in renal function. It refers to a clinical reality that causes renal dysfunction, indecorous complaint opinion, and treatment can affect in unrecoverable renal complaint with veritably short chances of survival. Artificial Intelligence (AI) technologies equip healthcare providers with a rich set of algorithms to prognosticate the habitual development of conditions. previous information helps to enable visionary corrective measures. This exploration study intends to develop an effective tool for prognosticating CKD using machine learning approaches. A machine literacy model is proposed and presented to address the on-uniform distribution of cases through class balancing, point ranking and analysis, and classifier training.

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A Coverage-Oriented Fuzzing Test Method for Embedded Firmware

A Coverage-Oriented Fuzzing Test Method for Embedded Firmwar...

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International Symposium on System and Software Reliability (ISSSR)

作者： Wansheng Yang Hairu Luo Chi Hu Fengjiao He Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350362930

ISBN: (纸本)9798350362947

When it comes to firmware vulnerability detection in embedded systems, traditional static analysis approaches struggle to capture deep-level program execution logic, resulting in unsatisfactory effectiveness. Based on common fuzzer, this paper proposes a coverage-oriented dynamic fuzzing test method called dynamic instrumentation marking coverage-based fuzzer to support simulation and test for ARM and MIPS architecture-based IoT embedded device firmware. Furthermore, two experiments were designed. One proved the fuzzer can effectively discover vulnerabilities in firmware; the other one demonstrated that compared to the AFL fuzzer, the method we proposed can generate more test samples, execute most of them and trigger more crash records.

关键词： Embedded systems Instruments Static analysis Fuzzing computer crashes Safety Security

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Design of Simulation and Evaluation System for Safety Operation in High Energy Radiation Scene Based on SysML

Design of Simulation and Evaluation System for Safety Operat...

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International Conference on Electronic engineering and Informatics (EEI)

作者： Huai Wu Baoran An Weijian Huang Xuedong Zhang Sifeng Zeng Institute of Computer Application China Academy of Engineering Physics Mianyang China

ISBN: (数字)9798350353594

ISBN: (纸本)9798350353600

With the emergence and wide application of new energy sources and technologies, various high-energy radiation devices have become indispensable core facilities and play an important role. At present, there are not only a large number of high-energy radiation devices in service, but also many have been or will enter the decommissioning stage. Aiming at the uncertainty and fuzziness of the traditional safety evaluation methods in the maintenance and decommissioning of high-energy radiation equipment, this paper designs the algorithms of task allocation, path planning and safety ergonomics evaluation for high-energy radiation equipment. Based on the system modeling language, the rationality of the system control flow is verified, and a simulation evaluation system for high-energy radiation equipment is developed to realize the comprehensive evaluation of safety operation schemes.

关键词： Uncertainty Ergonomics Process control Control systems Systems Modeling Language Path planning Safety Maintenance Resource management Informatics

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