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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 3232 条记录，以下是181-190 订阅

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Detecting DDoS attacks in software Define Networking: A machine learning Based Approach

Detecting DDoS attacks in Software Define Networking: A Mach...

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2023 international conference on Next-Generation Computing, IoT and machine learning, NCIM 2023

作者： Bala, Shimul Ahsan, Sk. Md. Masudul Khulna University of Engineering & Technology Department of CSE Khulna Bangladesh

ISBN: (纸本)9798350316001

software-Defined Networking (SDN) is a type of network strategy that enables the separation of a network's control plane and data plane, thereby allowing centralized management and control of network traffic. Although SDN has many remarkable advancements, it encounters several security concerns like DDoS attack. The Distributed Denial-of-Service (DDoS) attack is a fast-growing threat that endangers the future of internet technology. In this paper, some machine learning classifiers namely support vector machine (SVM) using linear based kernel and radial basis function (RBF) based kernel, random forest (RF), decision tree (DT), logistic regression (LR), and k-nearest neighbors (KNN) are evaluated to identify and analyze DDoS assaults within an SDN environment. A comparative performance study according to the results from the experiments is described to determine the best suitable techniques for DDoS detection. The results show that some classifiers reached up to 100% accuracy and the performance is validated using ROC-AUC curve. By utilizing proper classifiers derived from machine learning and taking the benefits of SDN, the controller can be safeguarded against DDoS attacks. © 2023 IEEE.

关键词： Denial-of-service attack

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Semantic-based and learning-based Regression Test Selection focusing on Test Objectives 16

Semantic-based and Learning-based Regression Test Selection ...

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16th IEEE international conference on software Testing, Verification and Validation (ICST)

作者： Suzuki, Junji Nishi, Yasuharu Tanaka, Shoma Naruse, Kimihiko Shimoji, Minako Zhong, Zhen Univ Elect Commun Dept Informat Tokyo Tokyo Japan NTT DATA Automobiligence Res Ctr Ltd Sect 2 Yokohama Japan

ISBN: (纸本)9798350333350

machine learning-based regression test selection (MLRTS) has typically arisen in major cloud companies with full Cl/CD pipelines. Various approaches for RTS are researched except for deep semantics of test cases, i. e. test objectives. Test objectives are essential but implicit information hard to calculate. In this paper, we propose a test architecture of a test objective-based MLRTS (TOMLRTS). First, word vectors are converted front explicitly written words in all regression test cases by Word2Vec. Second, semantic test objective clusters are formed by K-means++ to express all test objectives in the regression test cases. Third, distance vectors are constituted, whose elements arc distances from each test case to the test objective clusters. Priorities of regression test cases are then calculated by MLRTS additionally according to the distance vectors. We additionally evaluate TOMLRTS compared to Facebook's MLRTS for commercial software. TOMLRTS selected test cases to detect bugs more rapidly.

关键词： Regression Test Selection Test Objective machine learning Natural Language Processing software engineering

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Fault Classification for Protection in MMC-HVDC Using machine learning Algorithms 3

Fault Classification for Protection in MMC-HVDC Using Machin...

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3rd IEEE Mysore Sub Section international conference, MysuruCon 2023

作者： Hameed Hameed, Omar Hazim Kutbay, Ugurhan Rahebi, Javad Hardalac, Firat Gazi University Electrical and Electronics Department Ankara Turkey Istanbul Topkapi University Software Engineering Department Istanbul Turkey

ISBN: (纸本)9798350340358

The problems in MMC-HVDC protection systems are categorized in this study using machine learning algorithms. The voltage and current data were utilized to determine the classification's features. With the use of the features derived from the voltage and current, machine learning (ML) and artificial machine learning (ML) have produced a defect locator that is accurate enough. Using this data, simulations of various fault types and unknown locations at different system points were run to anticipate the outcomes. Metrics including specificity, accuracy, and sensitivity were used to evaluate the efficacy of the fault classification system;the results showed 98.22%, 97.41%, and 97.23%, respectively. © 2023 IEEE.

关键词： HVDC power transmission

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machine learning Model for Measuring Cyclomatic Complexity of Source Code

Machine Learning Model for Measuring Cyclomatic Complexity o...

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2023 international conference on Intelligent Computing, Communication, Networking and Services, ICCNS 2023

作者： Odeh, Ayman Odeh, Munther Odeh, Nada Odeh, Hussein Al Ain University College of Engineering Department of Software Engineering Al Ain United Arab Emirates Oldenburg University Department of Mathematics and Science Oldenburg Germany

ISBN: (纸本)9798350339291

software developers strive to build high-performance and quality software with a very high degree of coherence among its components, simplified structure, and reduced complexity. A cyclomatic complexity measure is used to identify areas of code that are difficult to maintain or debug by determining how many independent paths, conditional and iteration statements there are through the source code. Using AI, it is possible to estimate and measure the cyclomatic complexity of source code by analyzing the structure of the code and identifying any potential areas where complexity could arise. This research aims to utilize deep learning methodologies, specifically for text classification, to measure the Cyclomatic Complexity of source code, it presents a method for measuring source code Cyclomatic Complexity using Multinomial Naive Bayes machine learning Model trained on 3270 samples collected from programs written in Python, Java, and C++. The results show an average accuracy of 96% and precision, recall, and f1-score were calculated for different levels of complexity. © 2023 IEEE.

关键词： Python

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A Meta-Summary of Challenges in Building Products with ML Components - Collecting Experiences from 4758+Practitioners 2

A Meta-Summary of Challenges in Building Products with ML Co...

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IEEE/ACM 2nd international conference on AI engineering - software engineering for AI (CAIN)

作者： Nahar, Nadia Zhang, Haoran Lewis, Grace Zhou, Shurui Kastner, Christian Carnegie Mellon Univ Pittsburgh PA 15213 USA Carnegie Mellon Software Engn Inst Pittsburgh PA 15213 USA Univ Toronto Toronto ON Canada

ISBN: (纸本)9798350301137

Incorporating machine learning (ML) components into software products raises new software-engineering challenges and exacerbates existing ones. Many researchers have invested significant effort in understanding the challenges of industry practitioners working on building products with ML components, through interviews and surveys with practitioners. With the intention to aggregate and present their collective findings, we conduct a meta-summary study: We collect 50 relevant papers that together interacted with over 4758 practitioners using guidelines for systematic literature reviews. We then collected, grouped, and organized the over 500 mentions of challenges within those papers. We highlight the most commonly reported challenges and hope this meta-summary will be a useful resource for the research community to prioritize research and education in this field.

关键词： Meta Summary ML in Production SE4ML SLR software engineering for machine learning

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TRUSTWORTHINESS EVALUATION FRAMEWORK FOR DIGITAL SHIP NAVIGATORS IN BRIDGE SIMULATOR ENVIRONMENTS 42

TRUSTWORTHINESS EVALUATION FRAMEWORK FOR DIGITAL SHIP NAVIGA...

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ASME 42nd international conference on Ocean, Offshore and Arctic engineering (OMAE)

作者： Namazi, Hosna Perera, Lokukaluge Prasad UiT Arctic Univ Norway Tromso Norway

ISBN: (纸本)9780791886878

The maritime industry is going towards implementing digital navigators, i.e., AI created by machine learning algorithms, on autonomous vessels in the future. Digital navigators can be developed by utilizing machine learning algorithms, e.g., deep learning type neural networks trained by data sets from human navigators. Even though there is significant importance in studying the trustworthiness of these digital navigators, a proper framework to evaluate it has not yet been developed. This study identifies the appropriate key performance indicators (KPIs) in the trustworthiness of digital navigators in autonomous vessels. The trustworthiness of AI-based applications, including digital navigators, can be studied from two primary levels: software and hardware levels. Each of these levels must have certain characteristics to be called trustworthy. In other words, software codes and algorithms should be Transparent, i.e., Explainable, Fair, and Accountable/Responsible. Moreover, the trustworthiness at the hardware level can be elaborated under two concepts of Resilience and Availability of the relevant systems and technologies. In addition, some concepts, such as Reliability, Privacy, Security, and Safety, should be studied for both levels since those concepts can overlap in both software and hardware levels. In this paper, the main focus is on investigating the software level's trustworthiness. After an introduction on the importance of the topic and digital navigator's development steps, the existing literature on trustworthy AI is reviewed, and the proper approaches for evaluating trustworthiness in AI-based digital navigators are identified and proposed.

关键词： Artificial Intelligence machine learning Shipping Maritime Human Navigator Digital Navigator Trustworthy AI Explainable AI Fairness Responsible AI

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A Survey on Supervised machine learning in Intrusion Detection Systems for Internet of Things 8

A Survey on Supervised Machine Learning in Intrusion Detecti...

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8th IEEE international conference on software engineering and Computer Systems, ICSECS 2023

作者： Saidin, Shakirah Binti Hisham, Syifak Binti Izhar Universiti Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Kuantan Pahang Malaysia

ISBN: (纸本)9798350310931

The Internet of Things (IoT) is expanding exponentially, increasing network traffic flow. This trend causes network security vulnerabilities and draws the attention of cybercriminals. Consequently, an intrusion detection system is designed to identify various network attacks and provide network resource protection. On the other hand, building a steadfast intrusion detection system is difficult since there are numerous flaws to address, such as the presence of supernumerary and irrelevant features in the dataset, leading to low detection accuracy and a high false alarm rate. To address these flaws, researchers are attempting to research on applying supervised machine learning techniques in intrusion detection systems for IoT. Therefore, this paper explores the prevailing machine learning techniques utilized in the intrusion detection system research area to provide better insight in this field. © 2023 IEEE.

关键词： Internet of things

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Text Labels Classification Model based on BERT Algorithm 4

Text Labels Classification Model based on BERT Algorithm

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4th international conference on Big Data and Artificial Intelligence and software engineering, ICBASE 2023

作者： Wu, Sai Huang, Zhiyin Feng, Hao Guangdong University of Technology School of Management Guangzhou China Guangdong University of Technology School of Electro-Mechanical Engineering Guangzhou China

ISBN: (纸本)9798350329490

Text labels are extracted from the content of texts and is a issue of natural language process, which contains multiple labels. The text labels classification aims to divide the multiple labels into only one correct category. Traditionally, researchers utilize machine learning algorithms to train an one classifier to solve the multiple labels, which may cause wrong classifier due to the machine learning can not process the relationship between texts. In this paper, we utilize pre-training of deep bidirectional transformers (BERT) to dispose text understanding issues. Initially, the pre-train process is established to capture the text representations and the initial classification is executed. Subsequently, the fine-tunning function is to optimize the parameters of BERT model including learning rate, training epochs and batch sizes. From our extensive experimental results, we can significantly conclude that our proposed method can achieve the text labels classification with reasonable computation costs and training process. © 2023 IEEE.

关键词： learning algorithms

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Advancing Epilepsy Disease Classification through machine learning and Deep learning Models Utilizing EEG Data. 17

Advancing Epilepsy Disease Classification through Machine Le...

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17th international conference on Open Source Systems and Technologies, ICOSST 2023

作者： Saleem, Adnan Khan, Muhammad Ali Yousaf, Hafiz Muhammad NCEPU School of Control and Computer Engineering Beijing China University of Lahore Department of Software Engineering Pakistan

ISBN: (纸本)9798350381320

Epilepsy disease is a neurological condition marked by recurring seizures that has a big effect on people's life. Effective management and therapy depend on a prompt and correct diagnosis. The traditional methods, including medical history checks and MRI-based diagnoses, are costly, time-consuming, and often create incorrect results. In order to overcome the drawbacks of conventional and expensive diagnostic techniques, this work offers a machine-learning and deep learning-based method for the classification of epilepsy disease utilizing EEG signal data. The proposed study uses the EEG signal dataset collected from the UCI machine learning repository. This study shows the potential for automating the classification of epilepsy disease by using EEG signal data, opening up a potentially new path for the early detection of disease. Proposed CNN model achieved the highest accuracy of 98.4% among all classifiers, which is quite good. © 2023 IEEE.

关键词： Deep learning

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Arabic User Requirements Classification Using machine learning 11

Arabic User Requirements Classification Using Machine Learni...

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11th international conference on Information Technology, ICIT 2023

作者： Amro, Reem Althunibat, Ahmad Hawashin, Bilal Al-Zaytoonah University of Jordan Amman Jordan

ISBN: (纸本)9798350320060

Requirement engineering is a crucial step in software engineering as it forms the foundation for all subsequent stages and significantly affects whether software development is successful or unsuccessful. The same software requirement document generally includes both functional and non-functional requirements are essential. However, expressing these requirements in natural language necessitates considerable human effort to classify them. Manual classification can be laborious, time-consuming, expensive, and prone to inaccuracies. Inaccurate classification can result in misunderstandings or ambiguities in requirements, leading to incomplete products that fail to meet customer demands. While many studies have proposed English language requirements classification techniques, there is an absence of research in Arabic requirements classification. Moreover, there aren't many Arabic datasets that are openly accessible in this sector. In this paper, we offer a method for gathering Arabic data sets for needs and utilizing machine learning techniques to automatically categorize software requirements written in Arabic into functional and non-functional requirements. This study intends to assist software developers in time savings, lower the cost and effort associated with the manual classification process, and improve the effectiveness of the requirements engineering phase. Further gives scholars in this subject a new topic to explore. © 2023 IEEE.

关键词： Requirements engineering

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