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检索条件"主题词=software algorithms"

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RSAGEL: A Hybrid Graph Neural Network Based Intrusion Detection Model for software Defined Networking

RSAGEL: A Hybrid Graph Neural Network Based Intrusion Detect...

引用

Information Science, Computer Technology and Transportation (ISCTT), International Conference on

作者： Yue Zhang Wanxiao Liu Jue Chen Yurui Ma College of Electronic and Electric Engineering Shanghai University of Engineering Science Shanghai China

ISBN: (数字)9798350388435

ISBN: (纸本)9798350388442

software Defined Networking (SDN) has emerged as a revolutionary network architecture aimed at surpassing the constraints inherent in traditional network infrastructures. As SDN adoption increases, it brings additional complexities to the domain of network security. This study focuses on the detection of anomalous behaviors and the classification of various network attack types within the SDN framework. The hybrid model Residual-GraphSAGE-LSTM(RSAGEL) introduced in this paper provides a robust approach to address intrusion detection challenges in SDN environments. The advantages of this model are utilized to effectively process graph structured data, achieving an accuracy 4.5% higher than traditional deep learning algorithms on the InSDN dataset.

关键词： Deep learning Accuracy software algorithms Intrusion detection Transportation Network security Threat assessment Real-time systems software defined networking Residual neural networks

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Key Applications and Challenges of Virtual software Simulation Technology in Data Resource Searching Schemes

Key Applications and Challenges of Virtual Software Simulati...

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Sentiment Analysis and Deep Learning (ICSADL), International Conference on

作者： Jiao Yueyue Shandong Institute of Commerce and Technology Jinan Shandong China

ISBN: (数字)9798331523923

ISBN: (纸本)9798331523930

With the continuous development of big data and complex software systems, virtual software simulation technology plays an increasingly important role in the fields of data resource search and security prevention applications. Inspired by this, this study first analyzes the basic principles of virtual software simulation technology, and proposes a new virtual simulation platform model by combining resource allocation strategy, search algorithm optimization and real-time interactivity, and conducts systematic extensibility research from the perspective of DDos detection. At the practical level, this study introduces a virtual simulation model based on the VERICUT platform to analyze the complex data search process and improve the modeling. The core methods of this research framework include the following three aspects: 1) Proposing a new collaborative management model for automated testing based on data flow; 2) Performing coverage testing of multi-dimensional data input and intermediate state parameterization at the practical level; 3) Proposing a new DDoS attack analysis and detection algorithm as an extension study. From the experimental results, it can be seen that in 50 sets of independent repeated tests, the DDos detection accuracy of the proposed algorithm is stable between 98% and 100%, with a small fluctuation rang. This shows the stability of the proposed model.

关键词： Analytical models Accuracy software algorithms Data models Stability analysis Real-time systems Security Resource management Optimization Testing

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Towards Data-Centric and Context-Aware Decision Making in software-Defined Vehicles

Towards Data-Centric and Context-Aware Decision Making in So...

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IEEE International Conference on software Architecture Workshops (ICSAW)

作者： Ella Peltonen Vishaka Basnayake Nada Elgendy Benjamin Kämä Pertti Seppänen Tero Päivärinta University of Oulu Oulu Finland

ISBN: (数字)9798331533366

ISBN: (纸本)9798331533373

As vehicular computing environments move towards software-defined paradigms and microservice architectures, more data becomes available from the in-vehicular sensors for various applications to be developed. Combining such local vehicular information with feedback from drivers and passengers, the environment, and external data sources opens new avenues for application development and enchanting vehicular capabilities, services, and systems. However, in managing more data effectively, reliably, securely, and privately, more focus is given to data analysis, machine learning, and artificial intelligence tasks and how they are integrated into vehicle-cloud architectures. This paper considers a vision for a data-centric architecture that utilizes open-source building blocks. We aim to bring together data from humans, vehicles, and environments to support novel application development and context-aware decision-making.

关键词： software architecture Architecture Soft sensors Decision making software algorithms Microservice architectures Computer architecture Machine learning Reliability Vehicles

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Optimizing Secure software Development Lifecycle with Parallel Temporal Convolutional Network for Vulnerability Detection

Optimizing Secure Software Development Lifecycle with Parall...

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Distributed Systems, Computer Networks and Cybersecurity (ICDSCNC), International Conference on

作者： S. Padmakala Mohammad Naseera Sudhakar K Hassan Mohamed Ali Banoth Ramesh Department of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Saveetha University Chennai India Department of CSE Malla Reddy Engineering College for Women (UGC-Autonomous) Hyderabad India Department of AI & DS Nitte Meenakshi Institute of Technology Bengaluru India Department of Computers Techniques Engineering College of Technical Engineering The Islamic University Najaf Iraq Department of AI&ML Gokaraju Rangaraju Institute of Engineering and Technology Hyderabad India

ISBN: (数字)9798350375442

ISBN: (纸本)9798350375459

Smart contracts are one of the software application which is developed based on blockchain, are extensively used in Internet of Things, finance management, and other applications. However, development of smart contracts increases, meanwhile vulnerability attacks elated to the software also increases which occur often. To overcome this limitation, Parallel Temporal Convolutional Network (PTCN) is proposed for the vulnerability detection in secure software development. By using parallel temporal convolutions, the detection model process multiple time steps at the same time, that helps to identify patterns effectively. Firstly, Smartbugs datasets are used for acquiring data regarding the vulnerable behaviors in a software development. After that these data are pre-processed by regularization method and fed to word embedding for converting text data to vector data. Finally, from these vector data, the proposed PTCN model detects the vulnerability efficiently. The experimental results of the proposed PTCN method attained accuracy of 93.26% which is higher than the existing detection methods such as Support Vector Machine (SVM) and Convolutional Neural Network (CNN).

关键词： Support vector machines Convolutional codes Accuracy Smart contracts software algorithms Vectors software Data models Convolutional neural networks software development management

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FPGA-based Hardware software Co-design to Accelerate Brain Tumour Segmentation

FPGA-based Hardware Software Co-design to Accelerate Brain T...

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IEEE International Symposium on Circuits and Systems (ISCAS)

作者： Vinay Rayapati Ravi Kiran Reddy Gogireddy Ajay Kumar Gandi Saketh Gajawada Gopala Krishna Reddy Sanampudi Nanditha Rao International Institute of Information Technology Bangalore India

ISBN: (数字)9798350330991

ISBN: (纸本)9798350331004

Brain tumors are a major concern, being the leading cause of cancer-related deaths. Computer-aided diagnosis significantly reduces the workload on physicians and improves cancer diagnosis and treatment. Brain tumor segmentation is a computationally intensive image-processing task. In this paper, we propose an FPGA-based Hardware-software Co-design to accelerate this task using Watershed and Otsu thresholding algorithms. The FPGA handles parallel components, while the CPU manages sequential tasks in the same System-on-Chip (SoC). Using PolarFire Icicle FPGA platform, we process 20 MRI brain scan images (128x128) from the Kaggle dataset. Implementing both algorithms in parallel on the FPGA results in a 1.97× acceleration compared to a CPU-only implementation, mainly achieved by a 1973× reduction in latency when moving the Otsu algorithm from the CPU to the FPGA. This optimization employs DSP/MATH blocks, loop unrolling, and pipelining techniques.

关键词： Image segmentation Magnetic resonance imaging software algorithms Watersheds Central Processing Unit Task analysis Field programmable gate arrays

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Improving software Defects Detection: An In-Depth Analysis of Machine Learning Methods and Static Analysis Tools for Greater Accuracy

Improving Software Defects Detection: An In-Depth Analysis o...

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Machine Learning and Autonomous Systems (ICMLAS), International Conference on

作者： Veer Bobade Chetan Puri Department of Artificial Intelligence and Machine Learning Faculty of Engineering And Technology DMIHER Wardha Maharashtra India Department of Computer Science and Engineering Faculty of Engineering And Technology DMIHER Wardha Maharashtra India

ISBN: (数字)9798331505745

ISBN: (纸本)9798331505752

Enhancing software quality and reducing testing expenses requires better software fault detection. To guarantee the dependability and usability of software, software Defect Prediction (SDP) uses machine learning approaches to anticipate and address any problems early in the development cycle. Numerous machine learning techniques, such as Support Vector Machines (SVM), Random Forest (RF), Logistic Regression, Naïve Bayes, Multilayer Perceptron (MLP), Decision Stump, J48, Lazy IBK, and ZeroR, are thoroughly examined in this work. With an emphasis on feature selection as a crucial step in increasing prediction accuracy and lower computing costs, these approaches are assessed for their capacity to detect software flaws precisely. This research study demonstrates how these models may improve fault identification and prevention procedures using historical records from sources such as the PROMISE repository. The study highlights the best practices for machine learning-based software quality assurance, lowering expenses while guaranteeing superior results.

关键词： Support vector machines Accuracy Machine learning algorithms software algorithms Machine learning software quality Multilayer perceptrons Feature extraction Prediction algorithms Random forests

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Efficient Load Balancing in software Defined Networks Using Unsupervised Meta-Reinforcement Learning

Efficient Load Balancing in Software Defined Networks Using ...

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Computational, Communication and Information Technology (ICCCIT), International Conference on

作者： Prerita Kulkarni Nitika Vats Doohan Department of Computer Science Medicaps University Indore India Department of Computer Science Sage University Indore India

ISBN: (数字)9798331512965

ISBN: (纸本)9798331512972

New technologies, such as cloud computing and big data, make traditional network management more difficult, while the network's user base continues to expand. Consequently, changes to the traditional network architecture are required. To address this issue and enhance network management compliance, a new idea called software-defined network (SDN) has been proposed. Due to limited network resources and to satisfy the criteria of quality of service, it is necessary to address numerous difficulties, one of which is load balancing (LB), which distributes data traffic among several resources to maximize their efficiency and reliability. The local information of a network is used to design LB in a typical network. Consequently, it is not very accurate. Still, software-defined network (SDN) controllers can see the network from anywhere in the world and make better LB. We provide a set of unsupervised meta- RL algorithms to build smart optimization architecture for SDN LB.

关键词： Uncertainty software algorithms Computer architecture Telecommunication traffic Load management Inference algorithms Reliability software defined networking Optimization Standards

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Are Latent Vulnerabilities Hidden Gems for software Vulnerability Prediction? An Empirical Study

Are Latent Vulnerabilities Hidden Gems for Software Vulnerab...

引用

International Mining software Repositories, MSR

作者： Triet Huynh Minh Le Xiaoning Du M. Ali Babar CREST - The Centre for Research on Engineering Software Technologies The University of Adelaide Adelaide Australia Cyber Security Cooperative Research Centre Australia Monash University Melbourne Victoria Australia

ISBN: (数字)9798400705878

ISBN: (纸本)9798350363982

Collecting relevant and high-quality data is integral to the development of effective software Vulnerability (SV) prediction models. Most of the current SV datasets rely on SV-fixing commits to extract vulnerable functions and lines. However, none of these datasets have considered latent SVs existing between the introduction and fix of the collected SVs. There is also little known about the usefulness of these latent SVs for SV prediction. To bridge these gaps, we conduct a large-scale study on the latent vulnerable functions in two commonly used SV datasets and their utilization for function-level and line-level SV predictions. Leveraging the state-of-the-art SZZ algorithm, we identify more than 100k latent vulnerable functions in the studied datasets. We find that these latent functions can increase the number of SVs by 4× on average and correct up to 5k mislabeled functions, yet they have a noise level of around 6%. Despite the noise, we show that the state-of-the-art SV prediction model can significantly benefit from such latent SVs. The improvements are up to 24.5% in the performance (F1-Score) of function-level SV predictions and up to 67% in the effectiveness of localizing vulnerable lines. Overall, our study presents the first promising step toward the use of latent SVs to improve the quality of SV datasets and enhance the performance of SV prediction *** CONCEPTS• Security and privacy → software security engineering.

关键词： Data privacy software algorithms Noise Predictive models Prediction algorithms software Data models

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Development of Intelligent Analysis and Visualization software Tools for Environmental Monitoring Data

Development of Intelligent Analysis and Visualization Softwa...

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Power, Electrical Engineering, Electronics and Control (PEEEC), International Conference on

作者： Jingyue Bao Jiajie Li Wuhan Technology and Business University Wuhan Hubei China Design Group Co. Ltd ZheJiang University of Technology Engineering Hangzhou Zhejiang China

ISBN: (数字)9798350378917

ISBN: (纸本)9798350378924

With the increasingly severe environmental pollution problem, the widespread deployment of environmental monitoring stations has generated massive and complex environmental data. Traditional data processing methods seem inadequate in the face of such a large amount of data, while the introduction of big data technology has brought revolutionary changes to the processing and analysis of environmental monitoring data. Through big data analysis, not only can the accuracy and completeness of monitoring data be significantly improved, but also the efficiency and quality of environmental monitoring can be greatly improved, providing solid data support and scientific decision-making basis for environmental management departments. This article focuses on developing an efficient and intelligent software tool for analyzing and visualizing environmental monitoring data, aiming to meet the high requirements for data processing and display in current environmental protection work. This software deeply integrates big data analysis and deep learning (DL) technology, and achieves intelligent mining and deep analysis of environmental monitoring data by constructing advanced algorithm models. The experimental results show that the software can accurately identify environmental change trends, predict potential pollution events, evaluate environmental quality levels, and present the analysis results to users in an intuitive and understandable visual form.

关键词： Visualization Pollution software algorithms Data visualization Big Data Market research Environmental monitoring Protection software tools Faces

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Comprehensive Examination of Machine Learning Methods for Predicting software Errors

Comprehensive Examination of Machine Learning Methods for Pr...

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Intelligent & Innovative Practices in Engineering & Management (IIPEM), International Conference on

作者： Lingarasu Kittusamy Tarun Dhar Diwan Parul Madan Pravin B Khatkale Ankit Vishnoi Keshav Kaushik Compunnel Inc Plainsboro Township NJ Atal Bihari Vajpayee University Bilaspur India Department of Computer Science and Engineering Graphic Era Deemed to be University Dehradun Uttarakhand India Sanjivani University Kopargaon Maharashtra India Amity School of Engineering and Technology Amity University Mohali Punjab India

ISBN: (数字)9798350390049

ISBN: (纸本)9798350390056

This paper presents a comprehensive examination of machine learning methods for predicting software errors, emphasizing the importance of algorithm selection, data quality, and class imbalance in the development process. The study analyzes various machine learning classifiers— including Support Vector Machine (SVM), C4.5, Logistic Regression (LR), Ripper, Instance-Based learner (IBk), Random Forest (RF), and Naive Bayes (NB)—and evaluates their performance under varying conditions of data imbalance, measured by the Matthews Correlation Coefficient (MCC). The findings reveal significant variability in classifier performance, highlighting the challenges associated with predictive modeling in imbalanced datasets. The research contributes to the field of software engineering by proposing a framework for integrating machine learning into software development workflows, aiming to enhance software reliability and mitigate the impact of errors in production environments.

关键词： Support vector machines Correlation coefficient Machine learning algorithms software algorithms Machine learning software quality Predictive models Prediction algorithms software reliability Classification algorithms

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