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Cyber Resilience (ICCR), International Conference on

作者： Hazem Al-Najjar Nadia Al-Rousan Department of Computer Science College of Information Technology University of Petra Amman Jordan Department of Computer Engineering Faculty of Electrical Engineering and Information Systems German Jordanian University

ISBN: (数字)9798350394962

ISBN: (纸本)9798350394979

This paper investigated the predictive capabilities of three decision tree models for IoT botnet attack prediction using packet information while minimizing the number of predictors. The study employed three decision tree models (C5, CHAID, and Random Forest) and two additional models (Logistic Regression and Bayesian Network) for comparison purposes. The IoT botnet attack dataset comprised various devices, mainly two types of attacks (Gafgyt and Mirai) and 23 feature engineering variables. Simulation results from all models showed that Accuracy, Precision, Recall, and False Omission Rate (FOR) values are approximately one, with an F1 score of around 0.5. CHAID and C5 models outperform other models in predicting IoT botnet attacks, as they are developed using only four and two variables, respectively. These results demonstrated that decision tree models with fewer variables can perform better than models that utilize all predictors.

关键词： Training Logistic regression Botnet Predictive models Bayes methods Internet of Things Decision trees

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Sociotechnical Transformation in the Decade of Healthy Ageing to empower the Silver Economy: Bridging the Silver Divide through Social and Digital Inclusion 24

Sociotechnical Transformation in the Decade of Healthy Agein...

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25th Annual International Conference on Digital Government Research, DGO 2024

作者： Nikiforova, Anastasija Tseng, Hsien-Lee Butt, Sidra Azmat Daheim, Dirk Liu, Li-Chuan University of Tartu Faculty of Science and Technology Institute of Computer Science Tartu Estonia National University of Tainan Department of Public Administration and Management Tainan Taiwan Tallinn University of Technology Information Systems Group Tallinn Estonia National Taitung University Department of Public and Cultural Affairs Taitung Taiwan

ISBN: (纸本)9798400709883

The current decade has been marked as the Decade of Healthy Ageing, where increased participation, social and digital inclusion for the silver population within and for the Silver Economy has become imperative. This panel discusses current activities in the regions where the largest numbers of older people live - Asia and Europe seeking to gain a diverse perspective on current development in the area from two leaders in the field of ICT and digital government – Taiwan and Estonia (with a more diverse view of the Baltic Sea region). The panel discusses the feasibility and effectiveness of these developments, identifies the obstacles these projects face, and examine their limitations. It also explores alternative solutions to address these gaps and the potential for adopting similar projects in other regions, thereby contributing to the guiding principles of"The plan for a Decade of Healthy Ageing 2021-2030" and the Sustainable Development Goals (SDGs). © 2024 Copyright held by the owner/author(s).

关键词： Silver

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SSCM: Self-Supervised Critical Model for Reducing Hallucinations in Chinese Financial Text Generation

SSCM: Self-Supervised Critical Model for Reducing Hallucinat...

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2025 IEEE International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2025

作者： Jin, Keyan Wang, Yapeng Santos, Leonel Fang, Tao Yang, Xu Im, Sio Kei Faculty of Applied Sciences Macao Polytechnic University China Computer Science and Communication Research Centre Polytechnic of Leiria Leiria Portugal Hithink RoyalFlush Information Network Co. Ltd. Hangzhou China Macao Polytechnic University China

ISBN: (纸本)9798350368741

Large Language Models (LLMs) show strong performance in natural language processing tasks, but their application in the financial domain is limited. Current methods rely on large datasets and manual prompt engineering, resulting in high data demands, long inference times, and frequent hallucinations. To address these limitations, we propose a novel self-supervised prompt optimization framework tailored for the financial domain. Our approach involves training a critical model that evaluates and ranks generated outputs using both good and bad answers generated from various revised prompts. Experiments on a large Chinese financial corpus show that our framework significantly improves performance on tasks such as summarization and event-based question answering, as evidenced by higher scores on both automated metrics like ROUGE, BLEU, and BERTScore, and also through human evaluations. These results validate the effectiveness of our method in reducing hallucinations and improving the quality of financial text generation. © 2025 IEEE.

关键词： Critical Model Financial Applications Hallucination Large Language Models (LLMs)

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Maize Crop Yield Prediction Using Machine Learning Regression Approach 5th

Maize Crop Yield Prediction Using Machine Learning Regressio...

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5th International Conference on Advances in Distributed Computing and Machine Learning, ICADCML 2024

作者： Bedane, Tarikwa Tesfa Kebede, Kidistie Bizuneh Mohapatra, Sudhir Kumar Das, Tapan Kumar Tripathy, Asis Kumar Department of Software Engineering Addis Ababa Science and Technology University Addis Ababa Ethiopia Agricultural Transformation Institute Addis Ababa Ethiopia Faculty of Emerging Technologies Sri Sri University Cuttack India School of Computer Science Engineering and Information Systems Vellore Institute of Technology Vellore India

ISBN: (纸本)9789819735228

Given maize’s significant role as a staple crop, it becomes imperative to carry out precise crop yield prediction to ensure food security. This research employs machine learning algorithms to analyze historical data related to maize crop yields, with the aim of developing a predictive model. The outcome of this study can serve as a valuable tool for pre-harvest planning and measuring crop production per unit of land area. The dataset utilized in this research spans from 2010 to 2021 and was sourced from Ethiopia’s Central Statistical Agency. Three machine learning regression algorithms—Logistic Regression, XGBoost, and Random Forest—were employed to design the predictive model. After data preparation and preprocessing, a total of 436,358 records were used for model development. The models were further trained with reduced features using principal component analysis (PCA). The experimental results demonstrate that the Random Forest algorithm, trained with a reduced number of features through PCA, yielded superior performance, with a lower error rate and a high R2 score of 0.98. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Machine learning

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Reference Tracking Control of a Nonlinear Epidemiological Model with State Estimation 9

Reference Tracking Control of a Nonlinear Epidemiological Mo...

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9th International Conference on Control, Decision and information Technologies, CoDIT 2023

作者： Csutak, Balázs Szederkényi, Gábor Pazmany Peter Catholic University Faculty of Information Technology and Bionics Budapest Hungary Institute for Computer Science and Control Systems and Control Laboratory Kende u. 13-17 BudapestH-1111 Hungary

ISBN: (纸本)9798350311402

In this paper, a reference tracking controller for an 8-compartment epidemic model is proposed. The dynamical model describing the disease spread and progression is given in nonlinear input-affine form. The manipulable input is the transmission rate, while the output to be tracked is the number of infected people. The model parameters correspond to the COVID-19 pandemic. The control design uses a simple SEIR model and it is based on feedback linearization combined with extended Kalman filter for state estimation. Simulation results show good tracking performance even with model mismatch and significant parameter uncertainty. © 2023 IEEE.

关键词： COVID-19

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An Automated Platform for Gathering and Managing Open-Source Cyber Threat Intelligence 2

An Automated Platform for Gathering and Managing Open-Source...

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2nd International Conference on Business Analytics for Technology and Security, ICBATS 2023

作者： Al-Dmour, Nidal A. Kamrul Hasan, Mohammad Ajmal, Masood Ali, Muhammad Naseer, Iftikhar Ali, Atif Hamadi, Hussam Al Ali, Nasir Mutah University College of Engineering Department of Computer Engineering Jordan Center for Cyber Security Malaysia Riphah International University Islamabad Pakistan University of Central Punjab Pakistan The Superior University Faculty of Computer Science & Information Technology Lahore Pakistan Cyberjaya Malaysia College of Engineering and IT University of Dubai United Arab Emirates School of Computer Science NCBA&E Lahore Pakistan Applied Science Private University Applied Science Research Center Amman11937 Jordan

ISBN: (纸本)9798350335644

The community has begun paying more attention to source OSCTI Cyber Threat Intelligence to stay informed about the rapidly changing cyber threat landscape. Numerous reports from the OSCTI frequently provide information about dangers. However, current OSCTI gathering and management tools have mainly concentrated on individual minor compromise indicators, despite the urgent need for high-quality OSCTI. The relationship between higher-level notions (including the strategies, methods, and processes) and the connections between them, which hold crucial information about dangerous behaviors and are crucial to revealing the full dangerous situation, have been disregarded. Therefore, we present SecurityKG, an automated OSCTI collection and administration system. SecurityKG collects OSCTI to extract high-fidelity knowledge about threat behaviours to address the void. Using a mixture of AI and NLP approaches, a security know-how graph is then constructed from a wide variety of sources. To facilitate knowledge graph exploration, SecurityKG provides a user interface (UI) that supports multiple forms of interactivity. © 2023 IEEE.

关键词： User interfaces

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Intrusion Detection in 5G Cellular Network Using Machine Learning

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computer systems science & Engineering 2023年第11期47卷 2439-2453页

作者： Ishtiaque Mahmood Tahir Alyas Sagheer Abbas Tariq Shahzad Qaiser Abbas Khmaies Ouahada Knowledge Unit of Systems and Technology UMT Sialkot CampusSialkot51040Pakistan Department of Computer Science Lahore Garrison UniversityLahore54000Pakistan Faculty of Computer Science National College of Business Administration and EconomicsLahore54660Pakistan Department of Electrical and Computer Engineering COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Faculty of Computer and Information Systems Islamic University of Madinah Madinah42351Saudi Arabia Department of Computer Science&IT University of SargodhaSargodha40100Pakistan Department of Electrical and Electronic Engineering Science University of JohannesburgP.O Box 524Auckland ParkJohannesburg2006South Africa

Attacks on fully integrated servers,apps,and communication networks via the Internet of Things(IoT)are growing *** devices’effectiveness harms end users,increases cyber threats and identity theft,raises costs,and negatively impacts income as problems brought on by the Internet of Things network go unnoticed for extended *** on Internet of Things interfaces must be closely monitored in real time for effective safety and *** the 1,2,3,and 4G cellular networks,the 5th generation wireless 5G network is indeed the great invasion of mankind and is known as the global advancement of cellular *** to this day,experts are working on the evolution’s sixth generation(6G).It offers amazing capabilities for connecting everything,including gadgets and machines,with wavelengths ranging from 1 to 10 mm and frequencies ranging from 300 MHz to 3 *** gives you the most recent *** countries have already established this technology within their *** is the most crucial aspect of using a 5G *** of the absence of study and network deployment,new technology first introduces new gaps for attackers and *** Protocol(IP)attacks and intrusion will become more prevalent in this *** efficient approach to detect intrusion in the 5G network using a Machine Learning algorithm will be provided in this *** research will highlight the high accuracy rate by validating it for unidentified and suspicious circumstances in the 5G network,such as intruder hackers/*** applying different machine learning algorithms,obtained the best result on Linear Regression Algorithm’s implementation on the dataset results in 92.12%on test data and 92.13%on train data with 92%precision.

关键词： Intrusion detection system machine learning confidentiality integrity availability

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Evolutionary Algorithsm with Machine Learning Based Epileptic Seizure Detection Model

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computers, Materials & Continua 2022年第9期72卷 4541-4555页

作者： Manar Ahmed Hamza Noha Negm Shaha Al-Otaibi Amel AAlhussan Mesfer Al Duhayyim Fuad Ali Mohammed Al-Yarimi Mohammed Rizwanullah Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz University16278AlKharjSaudi Arabia Department of Computer Science College of Science and ArtsKing Khalid UniversityMahayilAsir62529Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Natural and Applied Sciences College of Community-AflajPrince Sattam bin Abdulaziz University16278Saudi Arabia

Machine learning (ML) becomes a familiar topic among decisionmakers in several domains, particularly healthcare. Effective design of MLmodels assists to detect and classify the occurrence of diseases using healthcaredata. Besides, the parameter tuning of the ML models is also essentialto accomplish effective classification results. This article develops a novelred colobuses monkey optimization with kernel extreme learning machine(RCMO-KELM) technique for epileptic seizure detection and *** proposed RCMO-KELM technique initially extracts the chaotic, time,and frequency domain features in the actual EEG signals. In addition, the minmax normalization approach is employed for the pre-processing of the EEGsignals. Moreover, KELM model is used for the detection and classificationof epileptic seizures utilizing EEG signal. Furthermore, the RCMO techniquewas utilized for the optimal parameter tuning of the KELM technique insuch a way that the overall detection outcomes can be considerably *** experimental result analysis of the RCMO-KELM technique has beenexamined using benchmark dataset and the results are inspected under severalaspects. The comparative result analysis reported the better outcomes of theRCMO-KELM technique over the recent approaches with the accuy of 0.956.

关键词： Epileptic seizures eeg signals machine learning kelm parameter tuning rcmo algorithm

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Exploring Auditory Hand Guidance for Eyes-free 3D Path Tracing

Exploring Auditory Hand Guidance for Eyes-free 3D Path Traci...

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2025 CHI Conference on Human Factors in Computing systems, CHI EA 2025

作者： Abe, Yuki Hara, Kotaro Sakamoto, Daisuke Ono, Tetsuo Human-Computer Interaction Lab. Hokkaido University Hokkaido Sapporo Japan School of Computing and Information Systems Singapore Management University Singapore Singapore Division of Computer Science and Information Technology Hokkaido University Hokkaido Sapporo Japan Faculty of Engineering Kyoto Tachibana University Kyoto Japan

ISBN: (纸本)9798400713958

Guiding a user’s hand along a 3D path can help individuals avoid obstacles and manipulate everyday items with eyes-free. While prior work focused on haptic approaches using robots, auditory approaches for 3D path guidance remain underexplored. We prototyped and assessed two auditory hand guidance techniques for 3D path tracing: (1) VERBAL repeating spoken directional prompts;(2) Follow-Your-Finger (FYF) using the user’s index finger in moving hand as an embodied reference and sonifying whether the user should follow the index finger direction. In a controlled pilot study with 12 sighted participants under an eyes-free condition, VERBAL yielded about 1.5 times faster performance than FYF, while FYF achieved an error rate of less than half VERBAL’s. We thus recommend using VERBAL for wider paths with fewer obstacles and FYF for narrower paths with higher collision risks. We also discuss the research direction for applying these findings to real-world tasks. © 2025 Copyright held by the owner/author(s).

关键词： Three dimensional computer graphics

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Hunger Search Optimization with Hybrid Deep Learning Enabled Phishing Detection and Classification Model

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computers, Materials & Continua 2022年第12期73卷 6425-6441页

作者： Hadil Shaiba Jaber S.Alzahrani Majdy M.Eltahir Radwa Marzouk Heba Mohsen Manar Ahmed Hamza Department of Computer Sciences College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Science Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Phishing is one of the simplest ways in cybercrime to hack the reliable data of users such as passwords,account identifiers,bank details,*** general,these kinds of cyberattacks are made at users through phone calls,emails,or instant *** anti-phishing techniques,currently under use,aremainly based on source code features that need to scrape the webpage *** third party services,these techniques check the classification procedure of phishing Uniform Resource Locators(URLs).Even thoughMachine Learning(ML)techniques have been lately utilized in the identification of phishing,they still need to undergo feature engineering since the techniques are not well-versed in identifying phishing *** tremendous growth and evolution of Deep Learning(DL)techniques paved the way for increasing the accuracy of classification *** this background,the current research article presents a Hunger Search Optimization with Hybrid Deep Learning enabled Phishing Detection and Classification(HSOHDL-PDC)*** presented HSOHDL-PDC model focuses on effective recognition and classification of phishing based on website *** addition,SOHDL-PDC model uses character-level embedding instead of word-level embedding since the URLs generally utilize words with no ***,a hybrid Convolutional Neural Network-Long Short Term Memory(HCNN-LSTM)technique is also applied for identification and classification of *** hyperparameters involved in HCNN-LSTM model are optimized with the help of HSO algorithm which in turn produced improved *** performance of the proposed HSOHDL-PDC model was validated using different datasets and the outcomes confirmed the supremacy of the proposed model over other recent approaches.

关键词： Uniform resource locators phishing cyberattacks machine learning deep learning hyperparameter optimization

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