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Comparative Analysis of SVM, Naïve Bayes, and Logistic Regression in Detecting IoT Botnet Attacks

International Journal of Advanced Computer science and Applications 2025年第4期16卷 338-343页

作者： Siswanto, Apri Aji, Luhur Bayu Efendi, Akmar Alfaruqi, Dhafin Azriansyah, M. Rafli Raihan, Yefrianda Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Faculty Data Science and Information Technology INTI International University Malaysia

The rapid proliferation of Internet of Things (IoT) devices has significantly increased the risk of cyberattacks, particularly botnet intrusions, which pose serious security threats to IoT networks. Machine learning-based Intrusion Detection Systems (IDS) have emerged as effective solutions for detecting such attacks. This study presents a comparative analysis of three widely used machine learning classifiers—Support Vector Machine (SVM), Naïve Bayes (NB), and Logistic Regression (LR)—to assess their performance in detecting IoT botnet attacks. The experiment uses the BoTNeTIoT-L01 dataset, applying preprocessing techniques such as data cleaning, normalization, and feature selection to enhance model accuracy. The models are trained and evaluated based on standard performance metrics, including accuracy, precision, recall, F1-score, and AUC-ROC. The results indicate that SVM outperforms the other classifiers in terms of detection accuracy and robustness, particularly in detecting malware based on PE files. These findings offer valuable insights into selecting suitable machine learning models for securing IoT environments. Future work will further explore integrating advanced feature selection techniques and deep learning models to improve detection performance. © (2025), (science and Information Organization). All rights reserved.

关键词： Logistic regression

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A NEW STEPSIZE STRATEGY FOR KORPELEVICH’S ALGORITHM SOLVING VARIATIONAL INEQUALITIES

Applied Set-Valued Analysis and Optimization

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Applied Set-Valued Analysis and Optimization 2024年第3期6卷 309-317页

作者： Trinh, Ngoc Hai Tran, Trung Duc Faculty of Mathematics and Informatics Hanoi University of Science and Technology Hanoi Viet Nam College of engineering and computer science VinUniversity Hanoi Viet Nam

In this paper, we propose two self-adaptive extragradient-like algorithms for solving pseudomonotone variational inequalities. We consider two cases: the mapping is Lipschitz continuous (with unknown modulus) and is not Lipschitz continuous. The step size in our algorithms can either increase or decrease at each step. This feature makes our algorithms more efficient. We also give some numerical examples to compare the performance of our algorithm with the existing one. ©2024 Applied Set-Valued Analysis and Optimization.

关键词： Variational techniques

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Job Resumes Recommendation using Integration of Fuzzy Discernibility Matrix Feature Selection and Convolutional Neural Network Multi-label Text Classification

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第13期49卷 49-58页

作者： Hayami, Regiolina Yusoff, Nooraini Daud, Kauthar Mohd Fatma, Yulia Department of Informatics Engineering Faculty Computer Science Universitas Muhammadiyah Riau Indonesia Faculty of Data Science and Computing Universiti Malaysia Kelantan 16100 Malaysia Center for Artificial Intelligence Technology Faculty of Information Science and Technology Universiti Kebangsaan Malaysia 43600 Malaysia

The manual analysis of job resumes poses specific challenges, including the time-intensive process and the high likelihood of human error, emphasizing the need for automation in content-based recommendations. Recent advancements in deep learning, particularly Convolutional Neural Networks (CNN) for Multi-label Text Classification (MLTC), offer a promising solution for addressing these challenges through artificial intelligence. While CNN is renowned for its robust feature extraction capabilities, it faces specific challenges such as managing high-dimensional data and poor data interpretability. To address these limitations, this study employs the Fuzzy Discernibility Matrix (FDM) feature selection technique to determine the relevance of skills for each job vacancy. FDM assigns weights to the features of each job category and ranks their relevance based on the highest scores, which are then utilized in the MLTC CNN model. The integration of FDM and MLTC CNN serves as the foundation for generating content-based recommendations derived from key features in job resumes. This study produces a content-based job recommendation system that displays the top three job categories, accompanied by explanations of the skills supporting each selected category. These recommendations also consider cosine similarity values between analyzed items and the integrated results of FDM and MLTC CNN. The application of FDM for feature weighting has been proven to enhance multi-label text classification outcomes by providing better insights during the feature selection process. With a recall of 97.26%, precision of 94.81%, and accuracy of 98.58%, the MLTC model integrating FDM feature selection and CNN demonstrates robust performance characteristics in content-based job recommendation tasks. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Convolutional neural networks

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A Fault-prone Module Prediction Combining Past and Ongoing Test Results

Computer Software

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Computer Software 2024年第4期41卷 67-73页

作者： Hirose, Ryoichi Monden, Akito Nishiura, Kinari Tsunoda, Masateru Graduate School of Environmental Life Natural Science and Technology Okayama University Japan Faculty of Information and Human Sciences Kyoto Institute of Technology Japan Dept. of Informatics Faculty of Engineering and Science Kinadai University Japan

The aim of this study is to improve the efficiency of software testing and/or review by predicting modules that are likely to contain a bug. In cross-version bug prediction, which uses bug data from a previous version to predict bugs in software currently under development, it is difficult to achieve high prediction accuracy due to differences in bug factors between versions. In this research, three methods (test method, mix method, select method) are proposed to address this challenge by incorporating ongoing test results, in addition to bug data from past versions. An evaluation experiment targeted 13 open-source software projects, comparing conventional cross-version prediction with the three proposed methods using seven evaluation metrics. The result indicates that each proposed method achieves higher accuracy in bug prediction than the conventional cross-version prediction when utilizing test results for approximately 10% of the modules in the developing software. Particularly, the select method demonstrated superior performance in comparison. © 2024 Japan Society for Software science and technology. All rights reserved.

关键词： Software testing

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Behavior of transporting pipeline sections without and with hydrogen exposure based on full-scale tests

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China Welding 2024年第3期33卷 14-24页

作者： Nóra Nagy János Lukács Institute of Materials Science and Technology Faculty of Mechanical Engineering and InformaticsUniversity of MiskolcMiskolc 3515Hungary

Pipeline transport of hydrogen is one of today’s economic and environmental *** order to find safe and reliable application of both existing gas and build new pipelines,it is essential to carry out tests on full-scale pipeline section,including the potentially more dangerous places than the main pipe,the girth *** the investigations,pipeline sections of P355NH steel with girth welds were prepared and exposed to pure hydrogen at twice the maximum allowable operating pressure for 41 ***,full-scale burst tests were carried out and specimens were cut and prepared from the typical locations of the failed pipeline sections for mechanical,and macro-and microstructural *** results obtained were evaluated and compared with data from previous full-scale tests on pipeline sections without hydrogen *** results showed differences in the behavior of pipeline sections loaded in different ways,with different characteristics of the materials and the welded joints,both in the cases without hydrogen exposure and in the cases exposed to hydrogen.

关键词： gas transporting pipeline full-scale pipeline test complex loading condition hydrogen exposure safety factor

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Big Data Analysis using Elasticsearch and Kibana: A Rating Correlation to Sustainable Sales of Electronic Goods

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第3期4卷 583-591页

作者： Henderi, Henderi Irawatia, Ranty Indra, Indra Dewi, Deshinta Arrova Kurniawan, Tri Basuki Department of Informatics Engineering University of Raharja Indonesia Informatics Technical Department Universitas Budi Luhur Indonesia Faculty of Data Science and Information Technology INTI International University Nilai Malaysia Faculty of Science and Technology Universitas Bina Darma Indonesia Faculty of Technology and Information Science Universiti Kebangsaan Malaysia Malaysia

Big data collection involves enormous amounts of raw data. To boost the sustainability of corporate value and support business intelligence and decision-making systems, in-depth data analysis is necessary. The data storage, analysis, and visualization methods, as well as the discovery of patterns and linkages, all depend on extensive data analysis. This study aims to process datasets to learn things like how ratings impact market sales transactions and how much of an impact factor connected to consumers and items have on ratings. Elasticsearch and Kibana were used for the dataset processing. This study evaluated traits related to the test parameters using a variety of test procedures. The product is scored as a representation of the product types involved in the sales transaction, and the name is assessed as a reflection of the customer. Kibana and Elasticsearch, a full-text search engine, were used in this work to do extensive data analysis on data sets. It is a visualization tool that is employed in a controlled environment to evaluate how ratings impact market exchanges for electronic goods, and it offers suggestions. The study found a substantial relationship between electronic product sales on the Amazon marketplace from 2012 to 2018. It suggested the importance of buyer constituents as users and how different product categories relate to ratings in business transactions. © 2023, Ital Publication. All rights reserved.

关键词： Decision making

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Enhancing Understanding of Public Sentiment on Twitter Using SVM and Lexicon Methods 3

Enhancing Understanding of Public Sentiment on Twitter Using...

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3rd International Conference on Creative Communication and Innovative technology, ICCIT 2024

作者： Khairani, Dewi Setiawan, Andi Masruroh, Siti Ummi Uin Syarif Hidayatullah Jakarta Informatics Engineering Faculty of Science and Technology Jakarta Indonesia

ISBN: (纸本)9798350367492

Indonesia has experienced 655 terrorism incidents between 2000 and 2024, leading to numerous casualties. This study aims to enhance understanding of public sentiment on Twitter in response to notable terrorism events, specifically the suicide bombing of the Astana Anyar Police Station in Bandung in 2022. Using a Support Vector Machine (SVM) algorithm for classification and two lexicon-based methods (VADER and InSet) for sentiment analysis, the study compared their performance and accuracy. The VADER lexicon indicated predominantly negative sentiments, while the InSet lexicon showed more positive sentiments, likely due to differences in dictionary sizes, data preparation, and feature extraction methods. The model using the VADER-labeled dataset achieved a 94% accuracy rate, compared to 93% for the InSet-labeled model. These high accuracy levels demonstrate the models proficiency in categorizing sentiment data, providing valuable insights into public reactions to significant events and underscoring the importance of selecting appropriate lexicon-based methods for sentiment analysis in social media contexts. © 2024 IEEE.

关键词： Tweets

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Machine Learning-Based Prediction of Corrosion Inhibition Efficiency of Expired Pharmaceuticals: Model Development and Application

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Journal of Bio- and Tribo-Corrosion 2025年第1期11卷 1-12页

作者： Putra, Dzaki Asari Surya Ardyansyah, Nibras Bahy Putranto, Nicholaus Verdhy Trisnapradika, Gustina Alfa Akrom, Muhamad Rustad, Supriadi Dipojono, Hermawan Kresno Maezono, Ryo Faculty of Computer Science Study Program in Informatics Engineering Universitas Dian Nuswantoro Semarang50131 Indonesia Research Center for Materials Informatics Faculty of Computer Science Universitas Dian Nuswantoro Semarang50131 Indonesia Quantum and Nano Technologies Research Group Faculty of Industrial Technology Institut Teknologi Bandung Bandung40132 Indonesia School of Information Science Japan Advanced Institute of Science and Technology Ishikawa923-1292 Japan

Corrosion poses a significant challenge in industries due to material degradation and high maintenance costs, making effective inhibitors essential. Recent studies suggest expired pharmaceuticals as alternative corrosion inhibitors, but identifying the most efficient compounds can be time-consuming. This study applies machine learning models, particularly Gradient Boosting Regressor (GBR), to predict corrosion inhibition efficiency (CIE). The developed model is integrated into a web-based application, enabling real-time, accurate CIE predictions. Results show GBR as the more precise model for predicting CIE, with low error rates. The streamlit-based web application offers a user-friendly platform, facilitating quick identification of suitable inhibitors. It is flexible, supporting analysis beyond corrosion inhibitors, and significantly enhances the efficiency of the evaluation process. © The Author(s), under exclusive licence to Springer Nature Switzerland AG 2025.

关键词： Adversarial machine learning

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Insights into research on blockchain for smart contracts: a bibliometric analysis

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Multimedia Tools and Applications 2024年 1-25页

作者： Singh, Renu Gupta, Ashlesha Mittal, Poonam Faculty of Informatics and Computing J.C. Bose University of Science and Technology YMCA Faridabad India Department of Computer Science & amp Engineering Indraprastha Institute of Information Technology IIIT New Delhi India

Over the past few years, blockchain technology has gained significant attention. This surge in popularity can be attributed to the emergence of cryptocurrencies and the development of smart contracts. Cryptocurrency is a digital currency that eliminates the problem of double spending. Cryptocurrencies like Bitcoin, Ethereum, Litecoin, Stellar, Zcash, Maker, Aave, etc. become popular and are preferred for money transfers. Smart contracts are the next popular technology on the blockchain after cryptocurrency. It can be considered a piece of code that can execute automatically when the predefined conditions are fulfilled. Researchers believe that the potential of blockchain with smart contracts is only in its initial stages and that its true potential has yet to be fully discovered. Hence, an extensive bibliometric analysis is conducted to understand blockchain trends for smart contracts and to give future directions in this field. For this analysis, various steps are followed, starting with formulating the research question, defining the scope of our research, extracting and analyzing data, answering the research question, and finally, drawing a conclusion. This research paper will be fruitful for scholars and researchers, providing an extensive statistical and network analysis of extracted smart contracts publications. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Bibliometric analysis Blockchain Crypto contract Scientometrics Scopus Smart contracts VOSViewer

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An Empirical Study of Unanswered Python-Related Questions on Stack Overflow

An Empirical Study of Unanswered Python-Related Questions on...

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2024 International Conference on Information technology Research and Innovation, ICITRI 2024

作者： Nugroho, Yusuf Sulistyo Halim, Salsabil Aisyah Abdul Islam, Syful Kurniawan, Yogiek Indra Erlina, Tati Universitas Muhammadiyah Surakarta Informatics Engineering Faculty of Communication and Informatics Surakarta Indonesia BSMRSTU Computer Science and Engineering Faculty of Engineering Gopalganj Bangladesh Universitas Jenderal Soedirman Informatics Engineering Faculty of Engineering Purwokerto Indonesia Universitas Andalas Computer Engineering Faculty of Information Technology Padang Indonesia

ISBN: (纸本)9798350376210

As a high-level programming language, Python supports user-friendly coding and system integration. It is not only used for data analytics but is also applied in software development. Although it has several benefits, users have various issues while running Python code. When programmers face issues, they often use Stack Overflow (SO) to get assistance for their problems. However, many Pythonrelated questions on SO are not responded to and remain unanswered. Thus, in this paper, we analyzed these unanswered questions to identify potential weaknesses of user and discussion characteristics. We applied a mixed-methods approach, including quantitative and qualitative analysis of user reputation, question types, and discussion topics. Our analysis shows that unanswered questions are mostly from low-reputation users (72.52%), followed by mid-reputation users (17.93%), and high-reputation users (9.5 5%). The distribution of the unanswered question types shows that low-reputation users ask more 'how' type of question (38.90%) followed by mid and high-reputation users. In contrast, the 'what' and 'why' questions are mostly posted by high-reputation users, followed by mid and low-reputation users. Our word-cloud analysis suggests a correlation between user reputation and the question topics. We acknowledge that there are limitations in our study, include potential misclassifications due to the time of posting and biases in manual labeling, which may affect the accuracy of our findings. This study provides insights into unanswered questions posted by the community, open up potential areas for future work such as improving support mechanisms and community engagement. © 2024 IEEE.

关键词： Python

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