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High-Secured Image LSB Steganography Using AVL-Tree with Random RGB Channel Substitution

computers, Materials & Continua 2024年第10期81卷 183-211页

作者： Murad Njoum Rossilawati Sulaiman Zarina Shukur Faizan Qamar Center of Cyber Security Faculty of Information Science and TechnologyUniversiti Kebangsaan MalaysiaBangiSelangor43600Malaysia Computer Science and Cyber Security Faculty of Engineering and Information TechnologyBirZeit UniversityBirZeitRamallahP.O.Box 14Palestine

Random pixel selection is one of the image steganography methods that has achieved significant success in enhancing the robustness of hidden *** property makes it difficult for steganalysts’powerful data extraction tools to detect the hidden data and ensures high-quality stego image ***,using a seed key to generate non-repeated sequential numbers takes a long time because it requires specific mathematical *** addition,these numbers may cluster in certain *** hidden data in these clustered pixels will reduce the image quality,which steganalysis tools can ***,this paper proposes a data structure that safeguards the steganographic model data and maintains the quality of the stego *** paper employs the AdelsonVelsky and Landis(AVL)tree data structure algorithm to implement the randomization pixel selection technique for data *** AVL tree algorithm provides several advantages for image ***,it ensures balanced tree structures,which leads to efficient data retrieval and insertion ***,the self-balancing nature of AVL trees minimizes clustering by maintaining an even distribution of pixels,thereby preserving the stego image *** data structure employs the pixel indicator technique for Red,Green,and Blue(RGB)channel *** green channel serves as the foundation for building a balanced binary ***,the sender identifies the colored cover image and secret *** sender will use the two least significant bits(2-LSB)of RGB channels to conceal the data’s size and associated *** next step is to create a balanced binary tree based on the green *** the channel pixel indicator on the LSB of the green channel,we can conceal bits in the 2-LSB of the red or blue *** first four levels of the data structure tree will mask the data size,while subsequent levels will conceal the remaining digits of secret *** embedding the bits i

关键词： Image steganography pixel random selection(PRS) AVL tree peak signal-to-noise ratio(PSNR) imperceptibility capacity

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Enhancing Drone Security Through Multi-Sensor Anomaly Detection and Machine Learning

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SN computer science 2024年第5期5卷 651页

作者： Alzahrani, Mohammed Y. Department of Information Technology Faculty of Computer Science & amp Information Technology AlBaha University AlBaha Saudi Arabia

Unmanned Aerial Vehicles (UAVs), commonly referred to as drones, have determined numerous applications across industries, ranging from aerial surveillance to package shipping. As drones are used in vital operations, ensuring cyberattacks and anomalies protect them from attackers is now a big challenge. This research study presents a complete approach to enhancing drone safety by integrating multi-sensor anomaly detection and superior machine learning techniques. The proposed methodology capitalizes on the rich sensor suite embedded in present-day drones, encompassing GPS receivers, accelerometers, gyroscopes, cameras, communication modules, and more. Leveraging an array of sensors in drones, our technique detects abnormal drone behavior indicative of unauthorized access, GPS spoofing, communication jamming, and malicious activities. By extracting features from sensor records, we develop a robust anomaly detection framework using the “uav attack dataset” able to identify deviations from normal flight patterns, communication signals, and environmental interactions. Central to our methodology is the utilization of machine learning algorithms. These algorithms are skilled on labeled datasets containing numerous flight eventualities, each normal and hostile, together with the ones discovered inside the “uav attack dataset”. The obtained results are eventually evaluated using rigorous performance metrics to quantify their effectiveness in distinguishing genuine anomalies from benign variations. The findings of our study underscore the capacity of multi-sensor anomaly detection for drones. By harnessing the power of machine learning and sensor fusion, we exhibit the ability to hit upon attacks at an early level, mitigating capability harm and permitting rapid responses. This study contributes now not only to the field of drone safety but also to the broader panorama of self-sustaining systems protection, highlighting the importance of adaptive and proactive protection mech

关键词： Anomaly Detection Drone Security Machine Learning for Drones Multi-Sensor Anomaly Detection Sensor Fusion

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A three-step SEM-Bayesian network approach for predicting the determinants of CloudIoT-based healthcare adoption

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International Journal of information technology (Singapore) 2024年 1-21页

作者： Altawaiha, Iyad Atan, Rodziah Yaakob, Razali Bin Abdullah, Rusli Bin Hj Department of Software Engineering and Information Systems Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia Department of Computer Science Faculty of Computer Science and Information Technology Universiti Putra Malaysia (UPM) Serdang 43400 Malaysia

Adopting the CloudIoT-based healthcare paradigm provides various prospects for medical IT and considerably enhances healthcare services. However, compared to the advanced development of CloudIoT-based healthcare systems, their usage is relatively low among healthcare professionals in hospitals. Thus, a study on the healthcare professionals acceptance and adoption of CloudIoT-based healthcare is critical. This study explores factors influencing healthcare professionals adoption of CloudIoT-based healthcare solutions by extending the Unified Theory of Acceptance and Use of technology 2 model. Data was collected from 300 healthcare professionals in Jordan through an online questionnaire developed using the Google^® form application. The Bayesian Network and Structural Equation Modeling techniques were used to analyze and validate the proposed model. The results revealed that the healthcare professional’s behavioral intention is directly affected by seven factors: performance expectancy, effort expectancy, facilitating conditions, habit, trust, privacy, and security. The results also indicate that trust mediates the influence of privacy, facilitating conditions, and performance expectancy on behavioral intention. The research results will aid CloudIoT service providers, healthcare organizations, designers, and developers by offering comprehensive knowledge of the significant factors of CloudIoT-based healthcare adoption. A better understanding of these factors will help these stakeholder groups enhance their services and speed up the adoption of CloudIoT in the healthcare area. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Bayesian network CloudIoT Healthcare professionals Structural equation modeling UTAUT2

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Assessing and prioritizing crucial drivers for CloudIoT-based healthcare adoption: an analytic hierarchy process approach

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International Journal of information technology (Singapore) 2024年 1-18页

The CloudIoT paradigm has profoundly transformed the healthcare industry, providing outstanding innovation and practical applications. However, despite its many advantages, the adoption of this paradigm in healthcare settings remains limited. There is a lack of comprehensive understanding of the factors that ensure successful implementation. To address this issue, we have used the Analytic Hierarchy Process to prioritize 26 factors that could potentially influence the adoption of CloudIoT-based healthcare. These factors were derived from existing literature and categorized into four main groups: personal-specific, technology-specific, environment-specific, and organization-specific factors. Our findings revealed that performance expectancy, trust, facilitating conditions, task complexity, and perceived security are the top five critical factors influencing CloudIoT-based healthcare adoption. Interestingly, our results indicated that environmental factors were less influential than those in the other categories, with personal, technological, and organizational factors dominating the top ten significant factors. The insights from this study can help policymakers, CloudIoT service providers, healthcare providers, designers, and developers better understand the factors contributing to the successful implementation of CloudIoT-based healthcare. This understanding can guide decision-making and inform the development of strategies to promote the broader adoption of this innovative technology. © The Author(s), under exclusive licence to Bharati Vidyapeeth's Institute of computer Applications and Management 2024.

关键词： Adoption Analytic hierarchy process CloudIoT Factors Healthcare industry

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Machine Learning and Deep Learning Approaches for Fake News Detection: A Systematic Review of Techniques, Challenges, and Advancements

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IEEE Access 2025年 13卷 90433-90466页

作者： Bashaddadh, Omar Omar, Nazlia Mohd, Masnizah Khalid, Mohd Nor Akmal Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Centre for Artificial Intelligence Technology Bangi43600 Malaysia Seiyun University College of Computers Department of Computer Science Hadhramout Yemen Universiti Kebangsaan Malaysia Faculty of Information Science and Technology Centre for Cyber Security Bangi43600 Malaysia

In response to the escalating threat of fake news on social media, this systematic literature review analyzes recent advancements in machine learning and deep learning approaches for its automated detection. Following the PRISMA guidelines, we examined 90 peer-reviewed studies published between 2020 and 2024 to evaluate model effectiveness, identify limitations, and highlight emerging trends. Our analysis shows that deep learning models, particularly transformer-based architectures such as BERT, consistently outperform traditional machine learning methods, often achieving high accuracy (Acc), precision (P), recall (R), and F1-score (F1). For instance, a BERT-based model reported up to 99.9% accuracy on the Kaggle fake news dataset and above 98% on other public datasets, including ISOT, Fake-or-Real, and D3. Similarly, the GANM model demonstrated robust performance on the FakeNewsNet dataset by integrating text and social features. Transfer learning and multimodal models that incorporate user behaviour and network information significantly improve detection in diverse and low-resource environments. However, challenges persist in terms of dataset quality, model interpretability, domain generalisability, and real-time deployment. This review also underscores the limited adoption of few-shot and zero-shot learning techniques, highlighting a promising direction for future research on handling emerging misinformation with minimal training data. To support practical deployment, we advocate the development of explainable, multilingual, and lightweight models with a greater emphasis on human-centred evaluation and ethical considerations. Our findings provide a foundation for researchers and practitioners aiming to build scalable, trustworthy, and context-aware fake news detection systems for global use. © 2013 IEEE.

关键词： Sentiment analysis

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Transfer function adaptation for effective feature selection with the side-blotched lizard algorithm

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Multimedia Tools and Applications 2024年 1-21页

作者： Eisa, Mohamed Abdel-lah, Marwa Farouk, Fifi Abdel-aal, Amr System and Information Technology Department Faculty of Management Technology and Information System Port Said University Port Said Egypt Department of Computer Science Faculty of Computer and Informatics Zagazig University Zagazig Egypt

Feature selection is a crucial preprocessing step in data mining and machine learning, enhancing model performance and computational efficiency. This paper investigates the effectiveness of the Side-Blotched Lizard Optimization Algorithm (SBLA) for feature selection by developing six novel variants: Sbla-s1, Sbla-s2, Sbla-s3, Sbla-v1, Sbla-v2, and Sbla-v3, each employing distinct S-shaped or V-shaped transfer functions to convert the continuous search space to a binary format. These variants were rigorously evaluated on nineteen benchmark datasets from the UCI repository, comparing their performance based on average classification accuracy, average number of selected features, and average fitness value. The results demonstrated the superiority of Sbla-s3, achieving an average classification accuracy of 92.8% across all datasets, a mean number of selected features of 20, and an average fitness value of 0.08. Furthermore, Sbla-s3 consistently outperformed six other state-of-the-art metaheuristic algorithms, achieving the highest average accuracy on sixteen out of nineteen datasets. These findings establish Sbla-s3 as a promising and effective approach for feature selection, capable of identifying relevant features while maintaining high classification accuracy, potentially leading to improved model performance in various machine learning applications. © The Author(s) 2024.

关键词： Heuristic algorithms

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Towards the Development of a Copyright Risk Checker Tool for Generative Artificial Intelligence Systems

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Digital Government: Research and Practice 2024年第4期5卷 1-21页

作者： Billiris, Grace Gill, Asif Oppermann, Ian Niazi, Mahmood Faculty of Engineering and Information Technology University of Technology SydneyNSW Australia Computer Science University of Technology SydneyNSW Australia University of Technology SydneyNSW Australia King Fahd University of Petroleum and Minerals Department of Information and Computer Science Dhahran Saudi Arabia

Generative Artificial Intelligence (GAI) is fundamentally changing the ways of working and blurring the boundaries between human and machine-generated contents. While there is an increasing interest in the adoption of GAI systems, such as ChatGPT and DALL-E, there are also serious concerns about the copyright of the contents - the inputs or generated as outputs by the GAI systems. Such concerns need to be identified and assessed to ensure the ethical and responsible use of GAI systems. Thus, this article aims to address the key research challenge: "how to identify and assess GAI system's copyright concerns"? In response, we propose the development of a Copyright Risk Checker (CRC) Tool. This tool has been formulated and evaluated using a recognised design science research methodology, drawing on an analysis of 10 legal cases across Australia, the United Kingdom, the United States, and Europe. The CRC Tool has undergone evaluation through an experimental scenario, and the results suggest that it is suitable for conducting an indicative copyright risk check of GAI systems. The outcomes of this preliminary assessment can be further examined by expert legal advisors for an in-depth analysis. The development of the CRC Tool provides a foundation for continued research and advancement in this significant area of study. © 2024 Copyright held by the owner/author(s).

关键词： Generative Artificial Intelligence Copyright Concern GAI Governance Copyright Regulations Independent Intellectual Effort Authorship

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MCBAN: A Small Object Detection Multi-Convolutional Block Attention Network

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computers, Materials & Continua 2024年第11期81卷 2243-2259页

作者： Hina Bhanbhro Yew Kwang Hooi Mohammad Nordin Bin Zakaria Worapan Kusakunniran Zaira Hassan Amur Computer and Information Science Department Universiti Teknologi PETRONASSeri Iskandar31750Malaysia Faculty of Information and Communication Technology Mahidol UniversityNakhon Pathom73170Thailand

Object detection has made a significant leap forward in recent ***,the detection of small objects continues to be a great difficulty for various reasons,such as they have a very small size and they are susceptible to missed detection due to background ***,small object information is affected due to the downsampling *** learning-based detection methods have been utilized to address the challenge posed by small *** this work,we propose a novel method,the Multi-Convolutional Block Attention Network(MCBAN),to increase the detection accuracy of minute objects aiming to overcome the challenge of information loss during the downsampling *** multi-convolutional attention block(MCAB);channel attention and spatial attention module(SAM)that make up MCAB,have been crafted to accomplish small object detection with higher *** have carried out the experiments on the Karlsruhe Institute of technology and Toyota Technological Institute(KITTI)and Pattern Analysis,Statical Modeling and Computational Learning(PASCAL)Visual Object Classes(VOC)datasets and have followed a step-wise process to analyze the *** experiment results demonstrate that significant gains in performance are achieved,such as 97.75%for KITTI and 88.97%for PASCAL *** findings of this study assert quite unequivocally the fact that MCBAN is much more efficient in the small object detection domain as compared to other existing approaches.

关键词： Multi-convolutional channel attention spatial attention YOLO

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Text Line Segmentation on Myanmar Handwritten Documents Using Directional Gaussian Filter 21

Text Line Segmentation on Myanmar Handwritten Documents Usin...

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21st IEEE International Conference on computer Applications, ICCA 2024

作者： Wai, Nilar Phyo War, Nu University of Computer Studies Faculty of Computer Science Mandalay Myanmar Myanmar Institute of Information Technology Faculty of Information Technology Mandalay Myanmar

ISBN: (纸本)9789998601673

Text line segmentation is a crucial step in document processing. Although text line segmentation techniques for handwritten documents in other languages (English, Chinese, Arabic) have developed, there is still room for improving text line segmentation in Myanmar handwritten documents. It is a challenging task to segment text lines in handwritten documents due to variations in handwriting styles, irregular spacing between lines and characters, and touching characters between lines. A method for Myanmar handwritten documents proposes to overcome these problems. The proposed system extracts bounding boxes of characters from text images by applying an anisotropic Gaussian method and connected component analysis (CCA). To extract correcting text lines by touching characters between lines, text lines are extracted by using proposed algorithm based on adaptive blob scale. To evaluate the proposed method, Myanmar handwritten documents dataset is collected from undergraduate students and high school students. According to the experimental results, the proposed technique achieves 97.5% accuracy rate with correct lines. © 2024 IEEE.

关键词： Gaussian distribution

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Performance Analysis of Boosting Algorithms on DDoS Attack Detection with Data Balancing Techniques 5

Performance Analysis of Boosting Algorithms on DDoS Attack D...

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5th International Conference on Advanced information Technologies, ICAIT 2024

作者： Naing, Soe Kalayar Thwel, Tin Thein University of Information Technology Faculty of Computer Science Yangon Myanmar University of Computer Studies Yangon Faculty of Information Science Yangon Myanmar

ISBN: (纸本)9798331510046

Today, a lot of people use the Internet of Things (IoT) in organizations, businesses, and daily life. Attackers have taken advantage of this potential for IoT devices to expose the integrity and security of user data. Especially, the security of the internet and user data is seriously threatened by distributed denial of service (DDoS) attacks. A DDoS attack is a cyberattack, that intends to break down or shut legal traffic from leading to its destination. There are various Machine Learning is employed in this work as classification methods for DDoS attack detection to develop an automated system that can recognize an attack immediately. This research intends a lightweight boosting classification approach to detect DDoS attacks of IoT based on the resampling techniques to minimize the data imbalance. It compares the performance of the proposed approach with AdaBoost, CatBoost, XGBoost, and LightGBM of boosting models based on time, ROC ACU, accuracy, MAE, RMSE, and F1-score. The experimental results demonstrated that the model with 4.079 seconds of the shortest runtime, highest accuracy, and low error rate is XGBoost using the SMOTE technique on the CICDDOS2019 dataset and KDDCUP99 dataset. XGBoost illustrates excellent time efficiency between other models, and it is a pleasant option for scenarios where rapid processing is critical. The accuracy of XGBoost illustrates highest than other models with 0.9998 with Smote and 0.9900 with borderline smote by using CICDDOS2019 dataset as performance. Additionally, it illustrates 0.699 accuracy with smote and 0.9699 of accuracy with borderline smote by using KDDCUP99 dataset. This research proposes an XGBoost classification approach for an effective IoT DDoS attack detection model using SMOTE and borderline SMOTE for balanced resampling. © 2024 IEEE.

关键词： Denial-of-service attack

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