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检索条件"任意字段=2023 International Conference on Software Engineering and Machine Learning, CONF-SEML 2023"

共 3227 条记录，以下是301-310 订阅

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Cross-Site Request Forgery as an Example of machine learning for Web Vulnerability Detection 3

Cross-Site Request Forgery as an Example of Machine Learning...

引用

3rd international conference on Smart Data Intelligence, ICSMDI 2023

作者： Rao, Mekala Srinivasa Kalyani, Birudugadda Vathsalya, Baswani Dhanunjay, Karri Narayana, Alasandalapalli Lakshmi Department of Computer Science and Engineering Mylavaram India

ISBN: (纸本)9781665464871

This paper presents a strategy for discovering flaws in web applications through machine learning (ML). Web-based applications are especially troublesome to examine attributed to their variety and extensive usage of custom development methodologies. As little more than a basis, machine learning is extremely useful in website safety: It just might combine cognitive knowledge of web app terminology with automated software approaches based on verbally reported information. Mitch tool is the foremost machine learning strategy towards black-box investigation for Cross-Site Request Forgery (C.S.R.F) problems, was built using these principles. Mitch-helped us find Thirty-five recently developed cross-site request forgeries (C.S.R. Fs) in twenty wide fields, together with 3 main C.S.R. Fs in industry applications. © 2023 IEEE.

关键词： Websites

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MCDM Based Evaluation of software Defect Prediction Models

MCDM Based Evaluation of Software Defect Prediction Models

引用

international conference on Advances and Applications of Artificial Intelligence and machine learning, ICAAAIML 2022

作者： Kumar, Ajay Kaur, Kamaldeep USICT Guru Gobind Singh Indraprastha University New Delhi India

ISBN: (纸本)9789819959730

software defect prediction is an essential activity for software quality assurance. Various researchers have developed a large number of models for software defect prediction based on machine learning. Different models may have the varying capability for software defect prediction with respect to different conflicting accuracy measures. This paper aims to propose a Multi-Criteria Decision Making (MCDM) based approach for selecting the most appropriate machine learning technique for software defect prediction with respect to more than one accuracy measure. Because of the involvement of various accuracy measures, the problem of selecting an appropriate machine learning technique for software defect prediction can be modeled as a Multi-Criteria Decision Making (MCDM) problem. An experimental study was conducted using nine machine learning techniques, four performance measures and one MCDM method over three datasets to validate the proposed method. Experimental results of this study show that the proposed method can be applied as an efficient decision-making tool in software industries for choosing the most suitable machine learning technique for software defect prediction. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd 2023.

关键词： machine learning

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Dirty page prediction by machine learning methods based on temporal and spatial locality 26

Dirty page prediction by machine learning methods based on t...

引用

26th international conference on Computer Supported Cooperative Work in Design, CSCWD 2023

作者： Lu, Yahui Jiang, Yu Shenzhen University College of Computer Science and Software Engineering Shenzhen China

ISBN: (纸本)9798350331684

The memory dirty page prediction technology can effectively predict whether a memory page will be modified (dirty) at the next moment, and is widely used in virtual machine migration, container migration and other fields. In this paper, we propose a machine-learning based method for memory dirty page prediction. The method exploits the temporal and spatial locality principle of memory changes, collects dirty records of pages over a period of time, and uses supervised learning methods for training and predicting the dirty page. We also discuss the influence of data contradiction and data repetition in memory page dataset. The experiments with different memory change frequency dataset show that compared with the traditional time series methods, our machine-learning based method has better performance. © 2023 IEEE.

关键词： Forecasting

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Enhancing Cyber Security Through machine learning: A Comprehensive Analysis 4

Enhancing Cyber Security Through Machine Learning: A Compreh...

引用

4th international conference on Computation, Automation and Knowledge Management, ICCAKM 2023

作者： MacHhindra, Pagare Anuradha Vijay, Bhatkudav Nikita Mahendra, Borawake Samrudhi Rahul, Cholke Anushka Anil, Pangavhane Aditya Sunil, Pawar Rohit Sanjivani College of Engineering Savitribai Phule Pune University Department of Computer Engineering Pune India

ISBN: (纸本)9798350393248

In our increasingly digital world, cyber security has become a paramount concern, with threats evolving from malicious software to sophisticated hacking techniques. To effectively combat these challenges, the integration of machine learning techniques has garnered significant attention and relevance. This comprehensive analysis delves into the potential of leveraging machine learning to fortify cyber security, discussing the utilization of predictive analytics, anomaly detection, and threat intelligence. machine learning applications offer a multifaceted approach to identifying and mitigating cyber threats. Predictive analytics harness historical data to anticipate potential security breaches, while anomaly detection techniques scrutinize deviations from established norms, aiding in real-time threat detection. Furthermore, the integration of threat intelligence allows organizations to stay ahead of evolving threats. This analysis also addresses the challenges and ethical considerations associated with deploying machine learning in cyber security, emphasizing the importance of responsible AI practices. By offering insights into the current state of the field and future prospects, this comprehensive analysis serves as a valuable resource for cyber security professionals, researchers, and policymakers, enabling them to strengthen defenses against the ever-evolving landscape of cyber threats. It underscores the significance of continued research and implementation of machine learning in safeguarding our digital ecosystem. © 2023 IEEE.

关键词： Anomaly detection

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IoT Defect Prediction Using AI Framework: A Review 3

IoT Defect Prediction Using AI Framework: A Review

引用

3rd international conference on Ubiquitous Computing and Intelligent Information Systems, ICUIS 2023

作者： Bhuvaneshwaran, A. Deisy, C. Sri Thrishna, J. Kumaraguru College of Technology Department of Ai&ds Coimbatore India Thiagarajar College of Engineering Department of Information Technology Madurai India

ISBN: (纸本)9798350306989

The quality of a software can be defined with respect to the parameters of reliability and consistency. Both these parameters are strongly associated with software errors. software engineers ensure the quality by prioritising testing in all phases such that all they remain cost effective. When a software product is released with deep shortcoming then fixing the errors during execution will be highly expensive. This research article sorts this problem by surveying certain aspects for resolving different software faults. machine learning frameworks are extensively used in resolving all such issues. The larger input parameters are experimented using logistic regression, SVM, decision trees etc for exact insights. These results are compared, and a better framework is identified through this article. © 2023 IEEE.

关键词： Decision trees

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Application Research of machine Vision Platform Based on Deep Neural Network and software engineering

Application Research of Machine Vision Platform Based on Dee...

引用

2023 international conference on Networking, Informatics and Computing, ICNETIC 2023

作者： Yu, Xiaoting Cheng, Kun Wang, Jing Jiang, Nannan Deng, Ran Harbin HuaDe University Harbin150027 China Heilongjiang University of Science and Technology Harbin150027 China

ISBN: (纸本)9798350313314

Aiming at the problem of unstable machine vision recognition accuracy caused by various kinds and inconspicuous features of workpiece defects, this paper puts forward a workpiece defect recognition algorithm based on deep learning network model and big data automatic training, and realizes its function by software engineering. Deep neural network is to apply the established neural network mathematical model to machine vision by imitating the structure of neural network in biological individuals and its functional characteristics, that is, the ability of parallel information processing and nonlinear mapping of neural network. Using machine vision technology, the collected images are preprocessed, and a deep neural network detection and recognition model is constructed. The video event detection system based on machine vision has been widely used in various important fields, such as medical and health care, industrial production, aerospace, military and civil service industries, etc. This paper compares and analyzes a variety of mature implementation methods, and expounds a new solution based on the development direction of new hardware and the demand of large-scale popularization in actual projects, with the machine vision as the guide. The implementation process of the system is guided by software engineering, and it is implemented in a multi-modular way. It can effectively cope with many changing factors in the real environment, and has a very high practical significance. © 2023 IEEE.

关键词： Computer vision

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A Supervised machine learning Model based Spectrum Sensing using NI USRP-2922 SDR

A Supervised Machine Learning Model based Spectrum Sensing u...

引用

2023 international conference on Wireless Communications, Signal Processing and Networking, WiSPNET 2023

作者： Singh Russells P, Rock Feller Raj S, Merlin Gilbert Electronics and Communication Engineering Sree Sakthi Engineering College Coimbatore India Electronics and Communication Engineering Karunya Institute of Technology and Sciences Coimbatore India

ISBN: (纸本)9798350300451

Spectrum scarcity is a major problem in this millennial communication engineering. machine learning based spectrum sensing approaches are getting more attention among research community. The spectrum sensing techniques detects the licensed and unlicensed bands and supports spectrum management. In this work, we have proposed the spectrum detection as two level classification problem and solved using a supervised machine learning model based support vector machine(SVM) algorithm. The data samples are collected in real campus environment ranging from line of sight to non-line of sight using the Labview enabled NI USRP-2922 software defined radio platform for 815 MHz. Correlation and moving average metrics are two features used for classifcation. The effectiveness of the classfication based on the feature vectors are observed through confusion matrix, prediction estimation and detection probability. © 2023 IEEE.

关键词： Support vector machines

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software Startups Success Prediction: machine learning Methods of Comparative Analysis 1

Software Startups Success Prediction: Machine Learning Metho...

引用

1st international conference for Women in Computing, InCoWoCo 2024

作者： Gracy, J Anitha Parthasarathy, S. Hirthick, S. Nandhini, P. Applied Mathematics and Computational Science Thiagarajar College of Engineering Tamil Nadu Madurai India

ISBN: (纸本)9798331518943

In recent years, there has been a significant rise in the number of software startups globally, driven by advances in technology and increasing reliance on digital solutions. These startups are crucial in shaping the future of industries. This study examines several machine learning models - Logistic Regression, Random Forest, K-Nearest Neighbors (KNN), Support Vector Classifier (SVC), and XGBoost - to predict the success of software startups based on key factors such as team dynamics and organizational characteristics. Using 473 data records and the top 19 features, the models were tested with an 80-20 train-test split, yielding strong performance in terms of accuracy, precision, recall, and F1 scores. Logistic Regression achieved the highest accuracy at 0.97, followed closely by KNN at 0.96. The findings reveal essential factors for startup success, such as team size, company age, and industry type, providing valuable insights for entrepreneurs, investors, and startup accelerators to adopt datadriven strategies that improve their chances of success. © 2024 IEEE.

关键词： Business Forecasting Data analysis machine learning software startups success prediction

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Deep Reinforcement learning for Multiple Agents in a Decentralized Architecture: A Case Study in the Telecommunication Domain 20

Deep Reinforcement Learning for Multiple Agents in a Decentr...

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IEEE 20th international conference on software Architecture (ICSA)

作者： Zhang, Hongyi Li, Jingya Qi, Zhiqiang Aronsson, Anders Bosch, Jan Olsson, Helena Holmstrom Chalmers Univ Technol Gothenburg Sweden Ericsson Ericsson Res Gothenburg Sweden Malmo Univ Malmo Sweden

ISBN: (纸本)9781665464598

Deep reinforcement learning has made significant development in recent years, and it is currently applied not only in simulators and games but also in embedded systems. However, when implemented in a real-world context, reinforcement learning is frequently shown to be unstable and incapable of adapting to realistic situations, particularly when directing a large number of agents. In this paper, we develop a decentralized architecture for reinforcement learning to allow multiple agents to learn optimal control policies on their own devices of the same kind but in varied environments. For such multiple agents, the traditional centralized learning algorithm usually requires a costly or time-consuming effort to develop the best-regulating policy and is incapable of scaling to a large-scale system. To address this issue, we propose a decentralized reinforcement learning algorithm (DecRL) and information exchange scheme for each individual device, in which each agent shares the individual learning experience and information with other agents based on local model training. We incorporate the algorithm into each agent in the proposed collaborative architecture and validate it in the telecommunication domain under emergency conditions, in which a macro base station (BS) is broken due to a natural disaster, and three unmanned aerial vehicles carrying BSs (UAV-BSs) are deployed to provide temporary coverage for missioncritical (MC) users in the disaster area. Based on the findings, we show that the proposed decentralized reinforcement learning algorithm can successfully support multi-agent learning, while the learning speed and service quality can be further enhanced.

关键词： Reinforcement learning machine learning software engineering Emergency Communication Network Multi-Agent Decentralized Architecture

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Analysis of DDoS Attacks in software Defined Networking using machine learning

Analysis of DDoS Attacks in Software Defined Networking usin...

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2023 IEEE international conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Sharma, Anshika Chauhan, Harshvardhan Singh Kaur, Harleen Babbar, Himanshi Chitkara University Institute of Engineering and Technology Chitkara University Punjab Rajpura India

ISBN: (纸本)9781665462631

The most practical configurable network is software Defined Networking (SDN). SDN delineates the network's control layer from the data layer, while operationally integrating them. With global visibility into network status, the's logical centralized control improves network management. However, centralized management introduces new security risks. One of the most attractive victims of distributed denial of service (DDoS) attacks is the SDN control platform. Due to machine learning (ML) performance and unavoidable advantages against fingerprint vulnerabilities, this article proposes and considers a ML solution for protecting against DDoS attacks in SDN. ML techniques are evaluated in both, a theoretical format and a practical setup such that SDN controllers are disclosed to DDoS-attacks in order to reach significant deductions about ML-based cybersecurity of future networking protocols. © 2023 IEEE.

关键词： Denial-of-service attack

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