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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441-450 订阅

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machine learning-Based Approach for Detecting DDoS Attack in SDN 2

Machine Learning-Based Approach for Detecting DDoS Attack in...

引用

2nd international engineering conference on Electrical, Energy, and Artificial Intelligence, EICEEAI 2023

作者： Alnatsheh, Athari Alsarhan, Ayoub Aljaidi, Mohammad Rafiq, Husnain Mansour, Khalid Samara, Ghassan Igried, Bashar Al Gumaei, Yousef Ali The Hashemite University Dept. of Computer Science Zarqa13116 Jordan The Hashemite University Dept. of Information Technology Zarqa13116 Jordan Zarqa University Dept. of Computer Science Zarqa Jordan Edge Hill University United Kingdom College of It Kingdom University Riffa Bahrain The Hashemite University Computer Science and Applications Zarqa Jordan Global College Heriot Watt University United Kingdom

ISBN: (纸本)9798350373363

With the great widespread networking and software-Defined Network (SDN) solutions, software-defined networks have become the target of many different attacks and security threats. software-defined networks are frequently exposed to denial-of-service attacks and distributed denial-of-service (DDoS), which may harm the controller or switch of SDN.. Consequently, the services offered by this network can be negatively *** this research, an experimental work was conducted to detect a DDoS Flooding attack. The features were extracted from a dataset to understand the behavior of the SDN and measure its performance in case of normally operating or when it is subjected to a DDoS *** performance of SDN was evaluated using several machine learning classifiers. Three classifiers are used in our experiments: Random forest (RF), Support vector machine (SVM), and Naive Bayes (NB).The results showed the superiority of the RF classifier over other classifiers with a detection accuracy of 98.89%. © 2023 IEEE.

关键词： Denial-of-service attack

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Malware Analysis Using machine learning and Deep learning 3

Malware Analysis Using Machine Learning and Deep Learning

引用

3rd international conference on Technological Advancements in Computational Sciences, ICTACS 2023

作者： Gupta, Riddhi Kumar, Puru Rani, Sonia Gupta, Aastha Kommuru, Anusha Ramkumar Dronacharya Group of Institutions Department of Computer Science &engineering Greater Noida India Galgotias University Department of Computer Science &engineering Greater Noida India

ISBN: (纸本)9798350342338

Malware is a severe danger to everyone from home users to huge corporations. As a result, it's a popular research topic. Malware fingerprints and activity patterns are analyzed both statically and dynamically to detect it. Analysis of malware on a daily basis by humans is a tedious task and still we are not ready for zero-day attack malware. Many machine learning methods are currently being utilized to find and analyze malware. The detection and classification accuracy varies based on algorithm used and the dataset taken to perform the testing. New and improved dataset will provide us with more accurate data and then new algorithm can be constructed based on it. The result will be more malware analysis data which can be used for malware detection by antivirus software. Given that Android smartphones are currently the target of the majority of attacks, the field of android malware analysis is expanding. Finding the best method to employ for a generic dataset is the research's ultimate objective, and the tested algorithm should surpass all prior tests on that dataset. © 2023 IEEE.

关键词： Android Malware Analysis Deep learning machine learning Malware Analysis Static and Dynamic Analysis

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Tensor Steepest Descent Method

Tensor Steepest Descent Method

引用

2023 international Russian Automation conference, RusAutoCon 2023

作者： Esaulov, Vasiliy A. Sinetsky, Roman M. Named after Mi Platov Dept. of Software Engineering Novocherkassk Russia

ISBN: (纸本)9798350345551

Currently, gradient optimization methods are among the most common and effective for training models in machine learning and neural networks. A well-known feature that reduces their effectiveness is the "ravine"character of the target function level lines. The paper proposes a modification of the steepest descent method associated with a change in the principle of choosing the descent step. A tensor is considered, the effect of which on the gradient can set the direction of descent, accelerating the search for the minimum of the function. The paper proposes a number of variants of the tensor structure, indicates the possibility of a step-by-step search for its elements. Using the MATLAB software package, a computational experiment was carried out, as a result of which the adequacy of the proposed method and its robustness were confirmed. © 2023 IEEE.

关键词： Global optimization

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ORFPPrediction: machine learning Based Online Recruitment Fraud Probability Prediction

ORFPPrediction: Machine Learning Based Online Recruitment Fr...

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2023 international conference on Cognitive Computing and Complex Data, ICCD 2023

作者： Zhang, Haiyan Wang, Meifeng Wang, Yuanyuan Li, Yazhou Gu, Dingyun Zhu, Yemeng Huaiyin Institute of Technology Institute of Computer and Software Engineering Jiangsu Province Internet of Things Mobile Interconnection Technology Engineering Laboratory Huai'an City Jiangsu223001 China

ISBN: (纸本)9798350306293

In response to the issue of inadequate public datasets concerning imbalances in Internet recruitment scams, this paper constructs an imbalanced experimental dataset, labels it, and proposes an ensemble learning method based on model stacking. This method enhances the prediction accuracy of the original model built with Random Forest. Initially, the method employs label encoding and TF-IDF (Term Frequency- Inverse Document Frequency) to preprocess structured and unstructured data respectively. Then, it utilizes Truncated SVD (Singular Value Decomposition) to reduce the dimensionality of the sparse matrix generated by feature extraction from TF-IDF. Subsequently, decision trees, random forests, and Light Gradient Boosting machine are utilized as base models, with the random forest employed as the meta-model to integrate the predictions of the base models. This paper presents a comparison of the experimental results from seven machine learning models based on the self-constructed experimental dataset, with model stacking implemented on the random forest that yielded the best experimental result. Through model stacking enhancement of the machine learning models, compared to the random forest model that performed best on the self-constructed experimental dataset, an accuracy of 94.85% was achieved, marking a 7.33% improvement. The experimental results hold significant value and practical implications in understanding and resolving the probability risk issue of recruitment fraud. © 2023 IEEE.

关键词： learning systems

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Multi-Label Classification of CS Papers Using Natural Language Processing Models 22

Multi-Label Classification of CS Papers Using Natural Langua...

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22nd IEEE international conference on machine learning and Applications, ICMLA 2023

作者： Zhang, Zheng Das, Amit Rahgouy, Mostafa Bao, Yida Baskiyar, Sanjeev Auburn University Dept. of Computer Science & Software Engineering Dept. of Mathematics & Statistics AuburnAL United States

ISBN: (纸本)9798350345346

Computer science (CS) is a rapidly evolving field of knowledge with a significant volume and significance of scientific papers. Consequently, meticulous management and categorization of CS papers are crucial to ensure efficient access and retrieval of relevant information. This study aims to evaluate the effectiveness of eight traditional machine learning models, as well as Recurrent Neural Network (RNN) models, such as LSTM and BiLSTM, and state-of-the-art pre-trained self-attention-based natural language processing (NLP) models, including BERT, XLNet, RoBERTa, and DistilBERT, for the task of multi-label classification of arXiv CS paper classification. The results demonstrate that pre-trained self-attention-based models consistently outperform the other models regarding classification performance. Moreover, self-attention-based models exhibited superior performance and achieved a new state-of-the-art result. To the best of our knowledge, our paper represents the first endeavor to evaluate machine learning models, specifically on computer science-related documents, as part of a multi-label classification task. © 2023 IEEE.

关键词： BERT Deep learning LSTM machine learning multi-label classification

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Automatic Subjective Answer Grading software Using machine learning 7

Automatic Subjective Answer Grading Software Using Machine L...

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7th international conference on Trends in Electronics and Informatics, ICOEI 2023

作者： Kothari, Rishabh Rangwala, Burhanuddin Patel, Kush K. J. Somaiya College of Engineering Computer Engineering Mumbai India

ISBN: (纸本)9798350397284

One of the major challenges during online examinations is the assessment of answers, particularly of the subjective type. Subjective answers test a student's ability to retain information and express it in natural language. While objective questions have a correct fixed answer, subjective questions can have multiple correct answers. These answers can convey the same information while using a completely different language and grammatical syntax. This makes it difficult to automate the process of grading subjective questions and requires a lot of manual work hours. This study intends to automate the process of grading subjective questions using machine learning (ML) and Natural Language Processing (NLP). The study has compared the subjective answer with an ideal answer that is provided by the authority that creates the question. Based on the similarity between the two answers, a score is generated which can be mapped to an appropriate grade. The authors have provided a web application made using the Django framework for people to give online examinations and be automatically graded in near real-time. No machine learning model can be 100% accurate, so there is a functionality for admins to edit the grades. © 2023 IEEE.

关键词： Grading

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Skill Recommendation for New Contributors in Open-Source software 23

Skill Recommendation for New Contributors in Open-Source Sof...

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45th IEEE/ACM international conference on software engineering (ICSE)

作者： Santos, Fabio No Arizona Univ Sch Informat Comp & Cyber Syst Flagstaff AZ 86011 USA

ISBN: (纸本)9798350322637

Selecting an appropriate task is challenging for newcomers to Open Source software (OSS) projects. Therefore, researchers and OSS projects have proposed strategies to label tasks (a.k.a. issues). Several approaches relying on machine learning techniques, historical information, and textual analysis have been submitted. However, the results vary, and these approaches are still far from mainstream adoption, possibly because of a lack of good predictors. Inspired by previous research, we advocate that the prediction models might benefit from leveraging social metrics. In this research, we investigate how to assist the new contributors in finding a task when onboarding a new project. To achieve our goal, we predict the skills needed to solve an open issue by labeling them with the categories of APIs declared in the source code (API-domain labels) that should be updated or implemented. Starting from a case study using one project and an empirical experiment, we found the API-domain labels were relevant to select an issue for a contribution. In the sequence, we investigated employing interviews and a survey of what strategies maintainers the strategies believe the communities have to adopt to assist the new contributors in finding a task. We also studied how maintainers think about new contributors' strategies to pick a task. We found maintainers, frequent contributors, and new contributors diverge about the importance of the communities and new contributors' strategies. The ongoing research works in three directions: 1) generalization of the approach, 2) Use of conversation data metrics for predictions, 3) Demonstration of the approach, and 4) Matching contributors and tasks skills. By addressing the lack of knowledge about the skills in tasks, we hope to assist new contributors in picking tasks with more confidence.

关键词： Labelling Skills Mining software Repositories Social Network Analysis Open-Source software machine learning Ontology Matching

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AN INTEGRATED APPLICATION OF BUILDING INFORMATION MODELING, COMPUTER-AIDED MANUFACTURING, machine learning, AND THE INTERNET OF THINGS: A Hybrid Stadium as a Case Study 28th

AN INTEGRATED APPLICATION OF BUILDING INFORMATION MODELING, ...

引用

28th international conference on Computer-Aided Architectural Design Research in Asia, CAADRIA 2023

作者： Qin, Bowen Wang, Tianyu Dubbeldam, Winka Korhammer, Justin Huang, Cong Wu, Dengguo Jiang, Hongming Archi-Tectonics NYC LLC United States Huadong Engineering Corporation Ltd China

ISBN: (纸本)9789887891796

Using building information modeling (BIM), design teams can maximize decision-making efficiency by using one data-sharing platform and one integrated digital model. We present a novel builtproject that integrates computer-aided manufacturing (CAM), machine learning, and internet of things (IoT) with a BIM process. The goal is to solve design and manufacturing problems and tightly connect virtual design data with the real-world construction process. Introducing CAM in the early phase reduces the cost of reverse engineering and optimizes material and construction costs. machine learning has been used for curtain wall panel standardization and to solve manufacturing problems. The IoT concept focuses on directing the data stream onto real-world objects and updating the digital model using feedback from real-world monitoring. Using the integrated application in this project, a hybrid Olympic stadium, helped to accelerate the process of design and reduced the time and cost for manufacturing and construction. © 2023 and published by the Association for Computer-Aided Architectural Design Research in Asia (CAADRIA), Hong Kong.

关键词： Internet of things

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Automating Microservices Test Failure Analysis using Kubernetes Cluster Logs 23

Automating Microservices Test Failure Analysis using Kuberne...

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27th international conference on Evaluation and Assessment in software engineering (EASE)

作者： Sarika, Pawan Kumar Badampudi, Deepika Josyula, Sai Prashanth Usman, Muhammad Ericsson AB Stockholm Sweden Blekinge Inst Technol Karlskrona Sweden

ISBN: (纸本)9798400700446

Kubernetes is a free, open-source container orchestration system for deploying and managing Docker containers that host microservices. Kubernetes cluster logs help in determining the reason for the failure. However, as systems become more complex, identifying failure reasons manually becomes more difficult and time-consuming. This study aims to identify effective and efficient classification algorithms to automatically determine the failure reason. We compare five classification algorithms, Support Vector machines, K-Nearest Neighbors, Random Forest, Gradient Boosting Classifier, and Multilayer Perceptron. Our results indicate that Random Forest produces good accuracy while requiring fewer computational resources than other algorithms.

关键词： Kubernetes cluster logs microservices machine learning

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Identifying PTSD Symptoms Using machine learning Techniques on Social Media 8

Identifying PTSD Symptoms Using Machine Learning Techniques ...

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

作者： Ibrahim, Muhamad Aiman Ismail, Nur Hafieza Kamarudin, Nur Shazwani Mohd Nafis, Nur Syafiqah Nasir, Ahmad Fakhri Ab. University Malaysia Pahang Al-Sultan Abdullah Faculty of Computing Pekan Pahang Malaysia

ISBN: (纸本)9798350310931

Post-traumatic stress disorder (PTSD) is a mental health illness brought on by watching or experiencing a horrific incident. Flashbacks, nightmares, acute anxiety, and uncontrolled thoughts about the unforgettable incident are the possible symptoms faced by PTSD sufferers. The PTSD diagnosis is usually done by a mental health specialist based on the symptoms that the person has, and the task is very time-consuming. Due to the widespread use of social media in recent years, it has opened up the opportunity to explore PTSD signs in users' postings on Twitter. The content-sharing feature available on this platform has allowed its users to share personal experiences, thoughts, and feelings that could reflect their psychological status. Thus, the goal of this work is to identify the PTSD symptom from text posting on Twitter. The crawled text posting is filtered and trained on selected machine learning and deep learning methods. The experiment results show that the support vector machine performed the best with 91% accuracy compared to others. This extracted model could be used in identifying PTSD symptoms on social media. © 2023 IEEE.

关键词： Support vector machines

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