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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Charoenwong, Nutcha Kosolsombat, Somkiat Ratanavilisagul, Chiabwoot College of Interdisciplinary Studies Thammasat University Data Science and Innovation Pathum Thani Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Thailand

ISBN: (纸本)9798350352214

Internet of Things (IoT) devices are small, low-power devices used for detecting and processing data remotely through the internet. These devices have increasingly been integrated into our daily lives, both in the digital and physical world. However, this interconnectedness poses risks of being targeted by malware due to limited resources and connectivity. Machine learning (ML) techniques can be used to identify and prevent malware attacks on IoT devices. In this research report, we investigate the use of ML techniques for classifying malware in IoT devices. Our research includes the implementation and evaluation of decision tree, k-nearest neighbors (KNN), and XGBoost algorithms. Our evaluation metrics include accuracy, precision, recall, and the average of precision and recall. Our method is tested using a dataset of real-world malware attacks on IoT devices, and the XGBoost algorithm achieves the highest accuracy of 98.44%, demonstrating its effectiveness in detecting and classifying malware in IoT devices. © 2024 IEEE.

关键词： Cybersecurity

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AI-Enhanced Virtual Twin Modelling for Strengthening IoT Software Security Protocols 1

AI-Enhanced Virtual Twin Modelling for Strengthening IoT Sof...

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1st International Conference on Software, Systems and Information technology, SSITCON 2024

作者： Kumara, Bharath Barmola, Pareshwar Prasad Arora, Renuka Parikh, Swapnil M Maranan, Ramya Ramaiah University of Applied Science Department of Electronics and Communication Engineering Bengaluru India Graphic Era Deemed to Be University Department of Computer Science and Engineering Dehradun India Jagannath University Department of Computer Science and Engineering Haryana India Parul Institute of Engineering and Technology Parul University Faculty of Computer Science and Engineering Vadodara India Simats Saveetha School of Engineering Department of Research and Innovation Chennai India

ISBN: (纸本)9798350352931

To fortify security mechanisms in software systems for the Internet of Things (IoT), this article presents a framework for AI-Enhanced Virtual Twin Modelling. In order to track and examine the actions of IoT devices in real-time, the suggested method makes use of virtual twin technology that is combined with machine learning techniques. Automated response generation, continuous threat assessment, and anomaly detection are made possible by creating a digital clone of the actual IoT network using the virtual twin paradigm. By combining deep learning models like CNNs and LSTMs, it becomes easier to forecast possible security risks in network traffic data by seeing intricate spatial and temporal patterns. When tested in a virtual Internet of Things (IoT) setting, the suggested framework outperforms conventional rule-based approaches by a wide margin, reducing reaction time by 35% while achieving an accuracy of 9 6. 2 %in threat identification. © 2024 IEEE.

关键词： Risk assessment

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Machine Learning Classification for Intrusion Detection on computer Networks 9

Machine Learning Classification for Intrusion Detection on C...

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Tachaapornchai, Abhibhu Kosolsombat, Somkiat Ratanavilisagul, Chiabwoot Data Science and Innovation College of Interdisciplinary Studies Thammasat University Pathum Thani Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Thailand

ISBN: (纸本)9798350352214

To build an intelligent intrusion detection system, it is essential to have a suitable and high-quality dataset with a sufficiently large quantity to simulate real-world scenarios. The NSL-KDD dataset is an improved version derived from the previous KDD 99 dataset. In this article, an analysis of the NSL-KDD dataset was conducted to study the efficiency of classification machine learning in detecting abnormalities in network data transmission patterns. The study achieved the highest accuracy by XGBoost method using 14 features important and grid search for hyperparameter tuning and revealing many interesting insights. This research has significant implications and can be further applied in the field of cybersecurity by leveraging machine learning for network intrusion detection systems. © 2024 IEEE.

关键词： Cybersecurity

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Harnessing Deep Learning for Effective Extractive Text Summarization: A Comparative Study 2

Harnessing Deep Learning for Effective Extractive Text Summa...

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2nd International Conference of Intelligent Methods, Systems and Applications, IMSA 2024

作者： Alsekait, Deema Mohammed Almanaseer, Waref Gomaa, Ibrahim A Zeid, Magdy Abd-Elghany Baligh, Salma Alaa, Hana Hegazy, Jasmine Magdy, Julia Eldmrat, Alaa Tariq Abdelminaam, Diaa Salama Applied College Princess Nourah Bint Abdulrahman University Department of Computer Science and Information Technology Saudi Arabia Applied Science Private University Faculty of Information Technology Amman11931 Jordan Alobour high institute for computer and informatics Computer Science Department Cairo Egypt Misr International University Faculty of Computer Science Cairo Egypt Midocean University Faculty of Human Sciences United Arab Emirates Jadara University Jadara Research Center Irbid21110 Jordan Misr International University Egypt MEU Research Unit Middle East University Faculty of Computers Amman11831 Jordan

ISBN: (纸本)9798350362633

Text Summarization might seem like a trivial problem, but in reality, is essential for data acquisition and understanding by compressing large amounts of text into specific a nd to-the-point summaries. This research is conducted to help compare the proposed methods' applications and make a summary system accessible to applications such as information retrieval, legal documents summarization and much more. from this, we intend to start by acquiring the dataset, preprocessing and training, and testing the models. In this paper will explore the methods that can be applied on summarization. These methods are Machine learning models like the random forest, naive Bayes, SVM, logistic regression, Deep learning like a neural networks, Transformers-based models like T5, and performance metrics like ROUGE. By trying all these various methods and models on a news article dataset we are going to compare the results from each of them and get the best performance. Finally, the results would be a comparison between all outputs and found that Natural Language Processor. In conclusion, Text Summarization is best done using NLP, and challenges in this project would be due to the diversity of text and context. © 2024 IEEE.

关键词： Contrastive Learning

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Towards Differentiating Between Failures and Domain Shifts in Industrial Data Streams

Towards Differentiating Between Failures and Domain Shifts i...

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2024 Workshop on Embracing Human-Aware AI in Industry 5.0, HAII5.0 2024

作者： Wojak-Strzelecka, Natalia Bobek, Szymon Nalepa, Grzegorz J. Stefanowski, Jerzy Jagiellonian Human-Centered AI Lab Mark Kac Center for Complex Systems Research Institute of Applied Computer Science Faculty of Physics Astronomy and Applied Computer Science Jagiellonian University ul. prof. Stanislawa Lojasiewicza 11 Krakow30-348 Poland Institute of Computing Science Poznań University of Technology Poznań60-965 Poland

Anomaly and failure detection methods are crucial in identifying deviations from normal system operational conditions, which allows for actions to be taken in advance, usually preventing more serious damages. Long-lasting deviations indicate failures, while sudden, isolated changes in the data indicate anomalies. However, in many practical applications, changes in the data do not always represent abnormal system states. Such changes may be recognized incorrectly as failures, while being a normal evolution of the system, e.g. referring to characteristics of starting the processing of a new product, i.e. realizing a domain shift. Therefore, distinguishing between failures and such"healthy" changes in data distribution is critical to ensure the practical robustness of the system. In this paper, we propose a method that not only detects changes in the data distribution and anomalies but also allows us to distinguish between failures and normal domain shifts inherent to a given process. The proposed method consists of a modified Page-Hinkley changepoint detector for identification of the domain shift and possible failures and supervised domain-adaptation-based algorithms for fast, online anomaly detection. These two are coupled with an explainable artificial intelligence (XAI) component that aims at helping the human operator to finally differentiate between domain shifts and failures. The method is illustrated by an experiment on a data stream from the steel factory. © 2024 Copyright for this paper by its authors.

关键词： Data streams

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PDF Malware Detection Using Machine Learning 9

PDF Malware Detection Using Machine Learning

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9th IEEE International Conference on Computational Intelligence and Applications, ICCIA 2024

作者： Tantikulvichit, Naragorn Kosolsombat, Somkiat Ratanavilisagul, Chiabwoot College of Interdisciplinary Studies Thammasat University Data Science and Innovation Pathum Thani Thailand King Mongkut's University of Technology Faculty of Applied Science Department of Computer and Information Science North Bangkok Thailand

ISBN: (纸本)9798350352214

In today's world, cyber-attacks are on the rise, and PDF files are commonly used as a means of attack. One common type of attack through PDF files is the covert embedding of dangerous malware and tricking users into clicking on malicious links. The victims may have their important data stolen and suffer various forms of damage. Detecting malware embedded in PDF files can help mitigate the harm to users. This project aims to study methods for detecting malware embedded in PDF files using machine learning techniques, with the best-performing model being Gradient Boosting, achieving an accuracy of 99% in 5-fold cross-validation and 97% using 10 features. Using Feature Selection Technique Voting from 3 Models consist of Logistic Regression, Random Forest, Lasso Regression, Each model will choose matching features to use for machine learning analysis. This project approach reduce feature for machine learning to detect PDFMalware. © 2024 IEEE.

关键词： Adversarial machine learning

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Improved Reptile Search Algorithm by Salp Swarm Algorithm for Medical Image Segmentation

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Journal of Bionic Engineering 2023年第4期20卷 1766-1790页

作者： Laith Abualigah Mahmoud Habash Essam Said Hanandeh Ahmad MohdAziz Hussein Mohammad Al Shinwan Raed Abu Zitar Heming Jia Computer Science Department Prince Hussein Bin Abdullah Faculty for Information TechnologyAl Al-Bayt UniversityMafraq25113Jordan Hourani Center for Applied Scientific Research Al-Ahliyya Amman UniversityAmman19328Jordan Faculty of Information Technology Middle East UniversityAmman11831Jordan Applied Science Research Center Applied Science Private UniversityAmman11931Jordan School of Computer Sciences Universiti Sains Malaysia11800Pulau PinangMalaysia Amman Arab University AmmanJordan Department of Computer Information System Zarqa UniversityP.O.Box 13132ZarqaJordan Deanship of E-Learning and Distance Education Umm Al-Qura UniversityMakkah21955Saudi Arabia Faculty of Information Technology Applied Science Private UniversityAmman11931Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu DhabiAbu DhabiUnited Arab Emirates School of Information Engineering Sanming UniversitySanming365004China

This study proposes a novel nature-inspired meta-heuristic optimizer based on the Reptile Search Algorithm combed with Salp Swarm Algorithm for image segmentation using gray-scale multi-level thresholding,called *** proposed method introduces a better search space to find the optimal solution at each ***,we proposed RSA-SSA to avoid the searching problem in the same area and determine the optimal multi-level *** obtained solutions by the proposed method are represented using the image *** proposed RSA-SSA employed Otsu’s variance class function to get the best threshold values at each *** performance measure for the proposed method is valid by detecting fitness function,structural similarity index,peak signal-to-noise ratio,and Friedman ranking *** benchmark images of COVID-19 validate the performance of the proposed *** results showed that the proposed RSA-SSA outperformed other metaheuristics optimization algorithms published in the literature.

关键词： Bioinspired Reptile Search Algorithm Salp Swarm Algorithm Multi-level thresholding Image segmentation Meta-heuristic algorithm

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Exploring Research Opportunities to Apply Data Mining Techniques in Software Engineering Lifecycle 1

Exploring Research Opportunities to Apply Data Mining Techni...

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1st International Conference on Advances in Electrical, Electronics and Computational Intelligence, ICAEECI 2023

作者： Anitha Gracy, J. Parthasarathy, S. Bagavathilaksmi, R. Thiagarajar College of Engineering Applied Mathametics and Computational Science Madurai India VELS Institute of Science Technology and Advanced Studies Department of Computer Application/Information Technology Chennai India

ISBN: (纸本)9798350342796

This Software engineering (SE) is the core research area of the software industry, and numerous algorithms and frameworks are proposed every day. Software Engineering is the discipline that keeps up with recent developments in fields and techniques such as data mining, machine learning, natural language processing, and artificial intelligence. It is difficult to manage such a massive amount of data in a software engineering repository if data processing and big data analytics are not available. This paper provides an in-depth study of data mining techniques that to improve the efficiency of the software development lifecycle. The study emphasizes the practical applications and discoveries made by text mining, clustering, classification, and other methods. © 2023 IEEE.

关键词： Classification Clustering Data Mining SDLC Software Engineering Text Mining

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Video Summarization Approach Based on Binary Robust Invariant Scalable Keypoints and Bisecting K-Means

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computers, Materials & Continua 2024年第3期78卷 3565-3583页

作者： Sameh Zarif Eman Morad Khalid Amin Abdullah Alharbi Wail S.Elkilani Shouze Tang Department of Information Technology Faculty of Computers and InformationMenoufia UniversityMenoufia32511Egypt Department of Artificial Intelligence Faculty of Artificial IntelligenceEgyptian Russian UniversityBader11829Egypt Department of Computer Science Community CollegeKing Saud UniversityRiyadh11362Saudi Arabia College of Applied Computer Science King Saud UniversityAl-Muzahmiya19676Saudi Arabia School of Communication and Information Engineering Chongqing University of Posts and TelecommunicationsChongqing400065China

Due to the exponential growth of video data,aided by rapid advancements in multimedia *** became difficult for the user to obtain information from a large video *** process of providing an abstract of the entire video that includes the most representative frames is known as static video *** method resulted in rapid exploration,indexing,and retrieval of massive video *** propose a framework for static video summary based on a Binary Robust Invariant Scalable Keypoint(BRISK)and bisecting K-means clustering *** current method effectively recognizes relevant frames using BRISK by extracting keypoints and the descriptors from video *** video frames’BRISK features are clustered using a bisecting K-means,and the keyframe is determined by selecting the frame that is most near the cluster *** applying any clustering parameters,the appropriate clusters number is determined using the silhouette *** were carried out on a publicly available open video project(OVP)dataset that contained videos of different *** proposed method’s effectiveness is compared to existing methods using a variety of evaluation metrics,and the proposed method achieves a trade-off between computational cost and quality.

关键词： BRISK bisecting K-mean video summarization keyframe extraction shot detection

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Degree-Based Entropy Descriptors of Graphenylene Using Topological Indices

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computer Modeling in Engineering & sciences 2023年第10期137卷 939-964页

作者： M.C.Shanmukha Sokjoon Lee A.Usha K.C.Shilpa Muhammad Azeem Department of Mathematics Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Computer Engineering(Smart Security) Gachon UniversitySeongnam13120South Korea Department of Mathematics Alliance School of Applied MathematicsAlliance UniversityBangalore562106India Department of Computer Science and Engineering Bapuji Institute of Engineering&TechnologyDavanagere577004India Department of Mathematics Riphah Institute of Computing and Applied SciencesRiphah International UniversityLahore54660Pakistan

Graph theory plays a significant role in the applications of chemistry,pharmacy,communication,maps,and aeronautical *** molecules of chemical compounds are modelled as a graph to study the properties of the *** geometric structure of the compound relates to a few physical properties such as boiling point,enthalpy,π-electron energy,andmolecular *** article aims to determine the practical application of graph theory by solving one of the interdisciplinary problems describing the structures of benzenoid hydrocarbons and *** topological index is an invariant of a molecular graph associated with the chemical structure,which shows the correlation of chemical structures using many physical,chemical properties and biological *** study aims to introduce some novel degree-based entropy descriptors such as ENTSO,ENTGH,ENTHG,ENTSS,ENTNSO,ENTNReZ1,ENTNReZ2 and ENTNSS using the respective topological ***,the above-mentioned entropy measures and physico-chemical properties of benzenoid hydrocarbons are fitted using linear regression models and calculated for graphenylene structure.

关键词： Topological descriptors weighted entropy graphenylene

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