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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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Real-time suspicious detection framework for financial data streams

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International Journal of Information Technology (Singapore) 2025年 1-17页

作者： Gadimov, Elshan Birihanu, Ermiyas Department of Data Science and Engineering Faculty of Informatics Eötvös Loránd University Budapest Hungary

Money laundering hides illegal money’s origin by making it seem legal. Detecting suspicious activity quickly in financial data is key to stopping fraud and money laundering. Real-time detection is popular approach for its speed and efficiently detecting illegal activities in financial institutes system. However, handling massive and distributed data streams have challenges in achieving real-time efficiency and effectiveness. Therefore proposing and developing data stream framework is needed to handle these challenges efficiently. The main goal of this study is to propose real-time suspicious detection framework for financial institutions to effectively combat money laundering. The proposed model comprises two approaches: a distributed computing architecture based on Docker container to enhance flexibility, migration capabilities, and customization, and a suspicious detection module employing the autoencoder method. To determine whether there is any suspicious activity in the system, the proposed model uses the reconstruction error. The reconstruction error is the difference between the original input data and the data reconstructed by the proposed model. To evaluate the proposed model, we used real-world data from a financial institution and synthetic data generated from the real-world data. The study demonstrates the better performance of the proposed real-time detection framework compared to traditional methods in identifying anomalous transactions. It also explores the importance and limitations of using both real-world and generated data. Our code is publicly available: https://***/Ermiyas21/Real-Time-Suspicious-Detection-Framework-for-financial-data. © The Author(s) 2025.

关键词： Anti-money laundering (AML) Autoencoders Generative adversarial Network Real-time Variational autoencoder

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Design of a Smart Home Door Security System with Face Detection and Smart Bell using ESP32-CAM 6

Design of a Smart Home Door Security System with Face Detect...

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6th International Conference on Mobile Computing and Sustainable informatics, ICMCSI 2025

作者： Salsabila, Nurhusna Siswanto, Apri Bayuaji, Luhur Informatics Department Faculty of Engineering Universitas Islam Riau Pekanbaru Indonesia Fac. Data Science and Information Technology INTI International University Malaysia

ISBN: (纸本)9798331522667

This research introduces the design and implementation of an advanced IoT-based home security system aimed at improving both convenience and safety for homeowners. The system integrates a smart doorbell with a mobile application, enabling real-time alerts for guest arrivals. Key features include facial recognition for automated door unlocking and notification of unrecognized visitors. When an unknown face is detected, the system sends an image along with a notification to the homeowner via a Telegram bot. The architecture incorporates the ESP32-CAM microcontroller for face detection, a solenoid lock for automatic door access, and an ultrasonic sensor to detect visitor presence. Extensive testing under various environmental conditions was conducted to assess the system's performance, particularly in terms of face recognition accuracy and notification delivery. The results indicate that the system significantly enhances home security by ensuring timely notifications and automating door access for verified individuals. While the system performs reliably in well-lit environments, challenges remain in low-light conditions. Software testing confirmed the robustness of the underlying logic. This innovative IoT solution overcomes the limitations of traditional security systems, offering a more secure, user-friendly, and efficient alternative for modern home security. © 2025 IEEE.

关键词： Ultrasonic sensors

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Teachers’ Motivation to Engage with Students in a Computer science and Computational Thinking Challenge: Does Motivation Conform to a ‘One-Size-Fits-All’ Model? 17th

Teachers’ Motivation to Engage with Students in a Computer ...

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17th International Conference on informatics in Schools: Situation, Evolution, and Perspectives, ISSEP 2024

作者： Feklistova, Lidia Jevsikova, Tatjana Gaál, Bence Pluhár, Zsuzsa Institute of Computer Science University of Tartu Tartu Estonia Institute of Data Science and Digital Technologies Vilnius University Vilnius Lithuania Faculty of Informatics Eötvös Loránd University Budapest Hungary

ISBN: (纸本)9783031734731

The international Bebras challenge (BC) aims to raise the students’ interest in computer science (CS) and contribute to their problem-solving and computational thinking (CT) skills development. Through solving short conceptual tasks that are close to real-life context, students obtain the essential literacy needed to be successful in the modern world. Although the initiative has been running for about 20 years, teachers’ motivation to engage with students in this challenge remains underexplored. This study employs self-determination theory as a theoretical framework and the work tasks motivation scale for teachers as an instrument. data collected from 334 teachers across Estonia, Hungary, and Lithuania were analyzed. The results showed teachers’ motivation most significantly stemmed from intrinsic motivation and identified regulation. There were no significant differences in all types of motivation between male and female teachers or teachers with different years of teaching experience. However, teachers who were more experienced in engaging in the BC demonstrated higher levels of almost all types of motivation. Mature teachers were more externally motivated than their younger colleagues. Teachers who used the Bebras tasks in their lessons had a higher level of intrinsic motivation and identified regulation than those who did not use these tasks. Teachers teaching various subjects showed different levels of identified regulation. The study provides implications for teachers, school administration, and the BC organization. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Students

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Q-Rung Orthopair Fuzzy Sets-Enhanced FMEA for COVID-19 Risk Assessment

Informatica (Slovenia)

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Informatica (Slovenia) 2025年第1期49卷 81-92页

作者： Abdullah, Lazim Awang, Noor Azzah Qiyas, Muhammad Special Interest Group of Modelling and Data Analytics Faculty of Computer Science and Mathematics Universiti Malaysia Terengganu Kuala Nerus21030 Malaysia College of Computing Informatics and Mathematics Universiti Teknologi MARA Selangor Shah Alam40450 Malaysia Department of Mathematics Riphah International University Failsabad Campus Pakistan

Failure Modes and Effects Analysis (FMEA) is a widely used tool for risk analysis, primarily to identify risk factors affecting system quality. Due to the limitations of the traditional FMEA model, several recent models incorporating advanced fuzzy set extensions have been developed to enhance the reliability of risk assessment outcomes. However, most of these models limit expert flexibility in expressing preferences and often overlook the impact of unequal expert weights and the stability of risk ranking results. This study introduces a new FMEA model based on Q-Rung Orthopair Fuzzy Sets (Q-ROFSs), termed Q-ROFSs-FMEA. Q-ROFSs, an extension of intuitionistic fuzzy sets, introduce a new linguistic term. The Q-ROFSs-FMEA model considers the unequal weights of experts, enabling a dynamic representation of expert preferences. These weights and the linguistic evaluation of risk factors are integrated through an aggregation operator, facilitating consensus among experts. The model is applied to a case study on COVID-19 risk factors, revealing that ‘older age’ (risk priority number 0.000012) is the highest risk factor, while ‘gender’ (risk priority number -0.0037) is the lowest. It is found that the ranking of risk factors determined by the Q-ROFSs-FMEA model is obtained as FM1 FM3 FM6 FM4 FM5 FM7 FM8 FM2 . Furthermore, a comparative analysis indicates consistent ranking results across different models, demonstrating the reliability of the proposed model. The case study and comparative analysis validate the effectiveness and applicability of the Q-ROFSsbased risk assessment model. © 2025 Slovene Society Informatika. All rights reserved.

关键词： Failure modes

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Design and evaluation of Lizard-Inspired Single-Actuated robot

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ROBOMECH Journal 2025年第1期12卷 1-17页

作者： Nansai, Shunsuke Kamamichi, Norihiro Faculty of Informatics and Data Science Akita University Akita Japan Department of Robotics and Mechatronics Tokyo Denki University Adachi Japan

The purpose of this paper is to develop a new Lizard-Inspired Single-Actuated (LISA) robot prototype capable of achieving the theoretical maximum input angle. Various morphologies for one-degree-of-freedom (1-DOF) robots have been proposed in previous studies. However, these designs face challenges due to their morphology, such as the need for toe slippage during walking. LISA introduces a new multi-legged morphology that overcomes these disadvantages, allowing propulsion and turning in 1-DOF. In this paper, we develop a new LISA prototype with the design requirement of achieving the theoretical maximum input angle while maintaining the robot’s morphology. Three key design points were identified to meet this requirement. The first is to design collision-free link geometry even at the maximum input angle. The second is to develop a push-back mechanism for posture recovery. The third is to implement balancing mechanisms. To evaluate the performance of the new prototype, we formulated the kinematics of LISA. The kinematic formulation defines the robot’s coordinate system, termed “robot coordinate,” and derives key parameters such as turning angle, stride length, posture, direction of travel, and curvature. We then experimentally evaluated the new prototype through two types of tests: a wobble and mechanism evaluation, and a motion performance evaluation. In the wobble and mechanism evaluation, we verified the reproducibility of the newly designed balance mechanism, the ability to achieve the maximum input angle, and the effectiveness of the push-back mechanism. Wobble and the angles of each link during walking were quantitatively measured. The results confirmed that the new LISA prototype meets the design requirement of achieving the maximum input angle and can produce a stable gait. In the motion performance evaluation, we compared the theoretical kinematics with LISA’s actual motion. Measurements from motion capture and angle sensors were compared with the theoretica

关键词： Machine design

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Optimal Marker Genes for c-Separated Cell Types

Optimal Marker Genes for c-Separated Cell Types

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29th International Conference on Research in Computational Molecular Biology, RECOMB 2025

作者： Borozan, Bartol Borozan, Luka Ševerdija, Domagoj Matijević, Domagoj Canzar, Stefan School of Applied Mathematics and Informatics University of Osijek Osijek Croatia Faculty of Informatics and Data Science University of Regensburg Regensburg Germany

ISBN: (纸本)9783031902512

The identification of cell types in single-cell RNA-seq studies relies on the distinct expression signature of marker genes. A small set of target genes is also needed to design probes for targeted spatial transcriptomics experiments and to target proteins in single-cell spatial proteomics or for cell sorting. While traditional approaches have relied on testing one gene at a time for differential expression between a given cell type and the rest, more recent methods have highlighted the benefits of a joint selection of markers that together distinguish all pairs of cell types simultaneously. However, existing methods either impose constraints on all pairs of individual cells which becomes intractable even for medium-sized datasets, or ignore intra-cell type expression variation entirely by collapsing all cells of a given type to a single representative. Here we address these limitations and propose to find a small set of genes such that cell types are c-separated in the selected dimensions, a notion introduced previously in learning a mixture of Gaussians. To this end, we formulate a linear program that naturally takes into account expression variation within cell types without including each pair of individual cells in the model, leading to a highly stable set of marker genes that allow to accurately discriminate between cell types and that can be computed to optimality efficiently. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Linear programming

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A decision-making framework for user authentication using keystroke dynamics

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Computers and Security 2025年 155卷

作者： Medvedev, Viktor Budžys, Arnoldas Kurasova, Olga Vilnius University Faculty of Mathematics and Informatics Institute of Data Science and Digital Technologies Akademijos str. 4 Vilnius08412 Lithuania

Increasingly sophisticated cyber attacks threaten critical infrastructures, requiring more trusted user authentication mechanisms. In this work, we propose a deep learning-based user authentication framework that combines keystroke dynamics with Siamese neural networks to differentiate legitimate users from impostors. A key challenge in this area is the variability in password lengths, which leads to different feature sizes and complicates model training. Our approach uses interpolation-based data fusion strategies to standardize the number of keystroke features, ensuring consistency across different datasets and password lengths. Through experiments on the fused CMU and KeyRecs datasets, we have evaluated the effectiveness of the proposed decision-making framework with adaptive threshold strategies. The threshold strategy determines how the final decision boundary is set with respect to the user's baseline typing behavior. We empirically evaluated the framework on fused data, achieving an equal error rate as low as 0.11–0.12, indicating strong efficacy in detecting insider threats. We show how the obtained Siamese neural network with triplet loss function can be used to distinguish genuine users from impostors even under different input conditions, contributing to more robust and scalable intrusion detection systems. © 2025 Elsevier Ltd

关键词： Network intrusion

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Frequency-Domain Refinement of Vision Transformers for Robust Medical Image Segmentation Under Degradation

Frequency-Domain Refinement of Vision Transformers for Robus...

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2025 IEEE/CVF Winter Conference on Applications of Computer Vision, WACV 2025

作者： Karimijarbigloo, Sanaz Kolahi, Sina Ghorbani Azad, Reza Bagci, Ulas Merhof, Dorit Tarbiat Modares University Department of Industrial and Systems Engineering Tehran Iran Northwestern University Department of Radiology Chicago United States University of Regensburg Faculty of Informatics and Data Science Regensburg Germany Fraunhofer Institute for Digital Medicine Mevis Bremen Germany

ISBN: (纸本)9798331510831

Medical image segmentation is crucial for precise diagnosis, treatment planning, and disease monitoring in clinical settings. While convolutional neural networks (CNNs) have achieved remarkable success, they struggle with modeling long-range dependencies. Vision Transformers (ViTs) address this limitation by leveraging self-attention mechanisms to capture global contextual information. However, ViTs often fall short in local feature description, which is crucial for precise segmentation. To address this issue, we reformulate self-attention in the frequency domain to enhance both local and global feature representation. Our approach, the Enhanced Wave Vision Transformer (EW-ViT), incorporates wavelet decomposition within the self-attention block to adaptively refine feature representation in low and high-frequency components. We also introduce the Prompt-Guided High-Frequency Refiner (PGHFR) module to handle image degradation, which mainly affects high-frequency components. This module uses implicit prompts to encode degradation-specific information and adjust high-frequency representations accordingly. Additionally, we apply a contrastive learning strategy to maintain feature consistency and ensure robustness against noise, leading to state-of-the-art (SOTA) performance in medical image segmentation, especially under various conditions of degradation. Source code is available at GitHub. © 2025 IEEE.

关键词： Convolutional neural networks

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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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