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4th IEEE International Conference on Electrical, computer, and Energy Technologies, ICECET 2024

作者： Diakhame, Mouhamadou Lamine Diallo, Cherif Mejri, Mohamed Gaston Berger University Department of Computer Science Saint Louis Senegal Laval University Department of Computer Science & Software Engineering Quebec Canada

ISBN: (纸本)9798350395914

The correlation of security alerts is crucial for effective threat detection and mitigation in modern cybersecurity landscapes. With the exponential growth in the volume of generated alerts, there arises an imperative to continually develop new, efficient methods for alert correlation. The use of Large Language Models (LLMs) presents new avenues for enhancing real-time threat detection in the field of cybersecurity. In this paper, we introduce MCM-Llama, a fine-tuned language model aimed at improving security event correlation for real-time threat detection. Building upon our prior research, we augment the current framework by integrating MCM-Llama and introducing a novel architecture to practically implement our proposition. In doing so, we replace the correlation module previously based on Named Entity Recognition (NER) and semantic similarity with a fine-tuned Large Language Model (LLM). This transition allows us to capitalize on the advanced capabilities of LLM in comprehending and correlating security events dynamically. Through this enhancement, we aim to significantly improve the accuracy and efficiency of our threat detection system while leveraging the full potential of LLM technology in cybersecurity. Our experimental results validate the efficacy of the proposed approach by significantly improving the correlation rate, thereby reinforcing the pivotal role of Large Language Models (LLMs) in advancing security event correlation and threat detection methodologies. © 2024 IEEE.

关键词： Semantics

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An Intent-Based System for software-Defined Vehicles in 5G Mobile Networks 15

An Intent-Based System for Software-Defined Vehicles in 5G M...

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15th International Conference on Information and Communication Technology Convergence, ICTC 2024

作者： Gu, Mose Ahn, Yoseop Hong, Juwon Shen, Yiwen Jeong, Jaehoon Sungkyunkwan University Department of Computer Science & Engineering Suwon Korea Republic of Ajou University Department of Software & Computer Engineering Suwon Korea Republic of

ISBN: (纸本)9798350364637

This paper introduces a comprehensive framework for intent-based management of networks, security, and applications in software-defined vehicles (SDVs) within 5G networks. To address the complexities and operational challenges of traditional network management, we propose an Intent-Based system (IBS) that simplifies the administration process by converting highlevel policies into actionable commands or configurations. The practical applicability and robustness of this approach are demonstrated through a testbed implemented using the free5GC platform. Our findings confirm the feasibility of intent-based network management and highlight its potential to enhance operational efficiency and scalability in the rapidly evolving automotive industry. © 2024 IEEE.

关键词： 5G mobile communication systems

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FCA-based θ-iceberg core decomposition in graphs

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Journal of Ambient Intelligence and Humanized Computing 2024年第2期15卷 1423-1428页

作者： Hao, Fei Xinchang, Khamphaphone Park, Doo-Soon Key Laboratory of Modern Teaching Technology Ministry of Education Xi’an China School of Computer Science Shaanxi Normal University Xi’an710119 China Department of Computer Science and Engineering Soonchunhyang University Asan31538 Korea Republic of Department of Computer Software Engineering Soonchunhyang University Asan31538 Korea Republic of

Complex networking analysis is a powerful technique for understanding both complex networks and big graphs in ubiquitous computing. Particularly, there are several novel metrics, such as k-clique and k-core are proposed in order to study the relative importance of nodes in complex networks. Among of those metrics, k-core analysis is an effective approach for simplifying graphical structure. However, the relation between k and the scale of networks is not explored in most existing literature. Toward this end, this paper formulate a new research problem, θ-Iceberg Core decomposition in graphs, which is able to incorporate a parameter θ (0 © Springer-Verlag GmbH Germany, part of Springer Nature 2017.

关键词： Formal concept analysis

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Analysis and Classification of Fake News Using Sequential Pattern Mining

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Big Data Mining and Analytics 2024年第3期7卷 942-963页

作者： M.Zohaib Nawaz M.Saqib Nawaz Philippe Fournier-Viger Yulin He College of Computer Science and Software Engineering Shenzhen UniversityShenzhen 518060 Guangdong Laboratory of Artificial Intelligence and Digital Economy(SZ) Shenzhen 518107China Department of Computer Science Faculty of Computing and Information TechnologyUnivesity of SargodhaSargodha 40100Pakistan

Disinformation,often known as fake news,is a major issue that has received a lot of attention *** researchers have proposed effective means of detecting and addressing *** machine and deep learning based methodologies for classification/detection of fake news are content-based,network(propagation)based,or multimodal methods that combine both textual and visual *** introduce here a framework,called FNACSPM,based on sequential pattern mining(SPM),for fake news analysis and *** this framework,six publicly available datasets,containing a diverse range of fake and real news,and their combination,are first transformed into a proper ***,algorithms for SPM are applied to the transformed datasets to extract frequent patterns(and rules)of words,phrases,or linguistic *** obtained patterns capture distinctive characteristics associated with fake or real news content,providing valuable insights into the underlying structures and commonalities of ***,the discovered frequent patterns are used as features for fake news *** framework is evaluated with eight classifiers,and their performance is assessed with various *** experiments were performed and obtained results show that FNACSPM outperformed other state-of-the-art approaches for fake news classification,and that it expedites the classification task with high accuracy.

关键词： disinformation fake news sequential pattern mining(SPM) frequent patterns classification

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An Exploration on Interactive Educational Games for Teaching Primary School Students of Vernacular Medium 9th

An Exploration on Interactive Educational Games for Teaching...

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9th International Conference on Research into Design, ICoRD 2023

作者： Sarma, Banamita Brahma, Debarshi Kumar Padun, Abhijit Mondal, Anowar Hussain Department of Multimedia Communication and Design CIT Kokrajhar Kokrajhar India Department of Computer Science and Engineering CIT Kokrajhar Kokrajhar India

ISBN: (纸本)9789819902927

The education system is constantly evolving. Teaching has been around since ancient times from Guru–Shishya (apprentice) method to the popularity of books after the invention of the printing press to modern schools with e-learning, and now educational games with AR and VR. Educational games can play an important role on students to make them understand various subjects in a playful manner by coming out of conventional teaching–learning methods. This study explores the implications of educational games for vernacular medium school students and evaluates their improvements. An experiment was carried out in a few vernacular medium schools of Kokrajhar, Assam, where the majority of the students are from the Bodo community who face difficulty in understanding different languages except the Bodo language. A survey conducted among the students outlines the fact that they face such difficulty due to the unavailability of appropriate course contents and study materials in vernacular medium. Hence, to address the issue, an interactive educational game has been designed and developed in three languages—Bodo, English, and Assamese to help students understand their course subjects with fun. Further, the study explores the scope of aligning the educational game with the regular course curriculum of schools to make learning enjoyable and fun. The study further explores the possibility of conducting practical classes in digital mode as well as offering attractive rewards to engage students in their studies. A survey conducted among the students on the educational game gave a strong affirmative response to implement the experiment practically. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2023.

关键词： Students

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Exploring Sleep Paralysis Phenomenon Through Machine Learning: An Analytical Study

Exploring Sleep Paralysis Phenomenon Through Machine Learnin...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Ishaq, Samra Rehman, Attique Ur Ali, Tahir Muhammad Javaid, Sabeen Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait

ISBN: (纸本)9798350349719

Sleep paralysis is when you're awake but powerless to move. Although the majority of occurrences are linked to extreme terror and some potentially clinically significant sufferings are connected with the case;little is known about it. The present state of research on the connection between sleep paralysis and overall sleep will be examined in this study. Several studies have linked an increased risk of sleep paralysis to low-quality sleep. This could occur in between sleeping and waking. The issue is tackled in the context of sleep paralysis prediction using machine learning. There are multiple attributes in the dataset (label Data). Every exam has a unique set of characteristics and expected results. We carried out an accuracy test of posture recognition by the system to evaluate its validity. Predicting sleep paralysis has been done using machine learning techniques. A comparison of sleeping positions with the findings indicated a correlation between paralysis and poor quality of sleep. Poor sleep quality is correlated with sleep paralysis. In the provided dataset, the Random Forest model predicts sleep paralysis with the highest accuracy 92.3%, K-nearest neighbors' classifier has an accuracy rate of 91.0% 91.9%, and 90.4% using Decision Tree and naïve Bayes. © 2024 IEEE.

关键词： Forecasting

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Enhancing Brain Stroke Risk Prediction with Multi-Algorithm Evaluation and Web Interface

Enhancing Brain Stroke Risk Prediction with Multi-Algorithm ...

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2024 International Conference on engineering and Computing, ICECT 2024

作者： Ahmed, Muhammad Liaquat, Umer Rehman, Attique Ur Javaid, Sabeen Ali, Tahir Muhammad Mir, Azka University of Sialkot Department of Software Engineering Sialkot Pakistan Gulf University for Science and Technology Department of Computer Science Kuwait

ISBN: (纸本)9798350349719

Brain stroke is the world's leading cause of death, impacting numerous lives annually. The chances of having a stroke have increased by 50% over one's lifetime, impacting one in four people worldwide. Machine learning may assist in the prior detection of strokes, allowing for more timely diagnosis which can save a person's life and disability. However, existing methods face challenges in integrating ML models with clinical applications to predict patient stroke status with optimal accuracy. This study conducts a comprehensive investigation into brain stroke risk prediction using numerous ML algorithms, including Random Forest, Logistic Regression, KNN, Naive Bayes, XGBoost, and LightGBM. Our main contribution is the development of a web app that predicts stroke based on user inputs with optimal accuracy. The selected dataset contains 5110 records of normal people and 249 records of people having strokes which is highly imbalanced. To ensure model robustness, the dataset undergoes balancing through SMOTE oversampling and KNN Imputation for handling missing values. Custom thresholds for classifiers optimize training, with comprehensive evaluations utilizing key statistics such as confusion matrices, F1-Score, recall, accuracy, and precision. Cross-validation is employed to assess potential overfitting and underfitting risks in machine learning models. Random Forest ranked as the most imposing, reaching a remarkable 99.85% accuracy on the test set with a custom threshold of 0.6 and a random state at 42. Additionally, a user-friendly Web App has been developed to predict stroke status based on user input, enhancing practical usability and accessibility for individuals seeking risk assessments. © 2024 IEEE.

关键词： Forecasting

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Zero Trust Networks: Evolution and Application from Concept to Practice

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computers, Materials & Continua 2025年第2期82卷 1593-1613页

作者： Yongjun Ren Zhiming Wang Pradip Kumar Sharma Fayez Alqahtani Amr Tolba Jin Wang School of Computer Science School of Cyber Science and EngineeringEngineering Research Center of Digital ForensicsMinistry of EducationNanjing University of Information Science and TechnologyNanjing210044China Department of Computing Science University of AberdeenAberdeenAB243FXUK Software Engineering Department College of Computer and Information SciencesKing Saud UniversityRiyadh12372Saudi Arabia Computer Science Department Community CollegeKing Saud UniversityRiyadh11437Saudi Arabia Sanya Institute of Hunan University of Science and Technology Sanya572024China

In the context of an increasingly severe cybersecurity landscape and the growing complexity of offensive and defen-sive techniques,Zero Trust Networks(ZTN)have emerged as a widely recognized *** Trust not only addresses the shortcomings of traditional perimeter security models but also consistently follows the fundamental principle of“never trust,always verify.”Initially proposed by John Cortez in 2010 and subsequently promoted by Google,the Zero Trust model has become a key approach to addressing the ever-growing security threats in complex network *** paper systematically compares the current mainstream cybersecurity models,thoroughly explores the advantages and limitations of the Zero Trust model,and provides an in-depth review of its components and key ***,it analyzes the latest research achievements in the application of Zero Trust technology across various fields,including network security,6G networks,the Internet of Things(IoT),and cloud computing,in the context of specific use *** paper also discusses the innovative contributions of the Zero Trust model in these fields,the challenges it faces,and proposes corresponding solutions and future research directions.

关键词： Zero trust cybersecurity software-defined perimeter micro-segmentation internet of things

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Classification and Segmentation of Gastroenterology Images Using Saliency Mapping in Convolutional Neural Network 21

Classification and Segmentation of Gastroenterology Images U...

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21st International Bhurban Conference on Applied sciences and Technology, IBCAST 2024

作者： Khan, Yusra Shaukat, Arslan Riaz, Farhan Department of Computer and Software Engineering Islamabad Pakistan School of Computer Science University of Lincoln Lincoln United Kingdom

ISBN: (纸本)9798331516680

This research work has been done towards the treatment of Gastrointestinal (GI) Cancer by experimenting as to how a computer aided design can help oncologists classify and segment GI Cancer using Gastroenterology (GE) Images. The concept of Attention Module by the name of Convolution Block Attention Module (CBAM) using Convolutional Neural Network (CNN) layers has been utilized to achieve saliency mapping in these images. The different layers of the CNN are tweaked and the hyper parameters are consistently checked and changed to achieve maximum accuracy. The CBAM Algorithm is divided into Channel Attention (CA) and Spatial Attention (SA) module. The Channel Attention has been implemented in the HSV Color Space for finding the best color space for segmentation purposes of the Chromo endoscopy medical images. Classification, followed by segmentation, is again implemented using CNN based model for normal and abnormal cancer images. For classification, 92% test accuracy has been achieved, while 70% Dice Similarity Coefficient has been obtained for segmentation. The results achieved are competitive as compared to the previous reported results on the GE images dataset. © 2024 IEEE.

关键词： Convolutional neural networks

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A Multimodal Multiobjective Evolutionary Algorithm for Filter Feature Selection in Multilabel Classification

IEEE Transactions on Artificial Intelligence

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IEEE Transactions on Artificial Intelligence 2024年第9期5卷 4428-4442页

作者： Hancer, Emrah Xue, Bing Zhang, Mengjie Victoria University of Wellington Center for Data Science and Artificial Intelligence School of Engineering and Computer Science Wellington6140 New Zealand Mehmet Akif Ersoy University Department of Software Engineering Burdur15039 Turkey

Multilabel learning is an emergent topic that addresses the challenge of associating multiple labels with a single instance simultaneously. Multilabel datasets often exhibit high dimensionality with noisy, irrelevant, and redundant features. In recent years, multilabel feature selection (MLFS) has gained prominence as a crucial and emerging machine learning task due to its ability to handle such data effectively. However, existing approaches for MLFS often prioritize top-ranked features based on intrinsic data criteria, disregarding relationships within the feature subset. Additionally, compared with conventional feature selection, multiobjective evolutionary algorithms (MOEAs) have not been widely explored in the context of MLFS. This study aims to address these gaps by proposing a multimodal multiobjective evolutionary algorithm (MMOEA) called MMDE_SICD which incorporates a preelimination scheme, an improved initialization scheme, an exploration scheme inspired by genetic operations and a statistically inspired crowding distance scheme. The results show that the proposed MMDE_SICD algorithm can outperform a variety of MOEAs and MMOEAs as well as conventional MLFS algorithms. Notably, this study is the first of its kind to consider MLFS as a multimodal multiobjective problem. © 2020 IEEE.

关键词： Multiobjective optimization

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