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R3 - Rescale, Rotate, and Randomize: A Novel Image Cryptosystem Utilizing Chaotic and Hyper-Chaotic systems

IEEE Access 2023年 11卷 119284-119312页

作者： Gabr, Mohamed Korayem, Yousef Chen, Yen-Lin Yee, Por Lip Ku, Chin Soon Alexan, Wassim Faculty of Media Engineering and Technology Computer Science Department Cairo11835 Egypt National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Malaya Faculty of Computer Science and Information Technology Department of Computer System and Technology Kuala Lumpur50603 Malaysia Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Faculty of Information Engineering and Technology Communications Department Cairo11835 Egypt New Administrative Capital Mathematics Department Cairo13507 Egypt

This work proposes a novel image encryption algorithm that integrates unique image transformation techniques with the principles of chaotic and hyper-chaotic systems. By harnessing the unpredictable behavior of the Chua system and the hyper-chaotic nature of the Chen system, the algorithm carries out rescaling, rotation, and randomization on the target image. The intrinsic unpredictability and sensitivity to initial conditions of these chaotic systems endow the encryption algorithm with an expansive key space of 25208. This feature not only bolsters its resilience against brute-force attacks but also magnifies its overall security profile. The algorithm's efficiency is evident in its rapid computational speed and lean resource consumption, making it suitable for real-time applications. To gauge its robustness, a battery of rigorous tests and analyses, spanning differential attacks, statistical attacks, and brute-force assaults, were employed. The results validate its ability to resist a diverse spectrum of threats. With its expansive key space, exceptional efficiency, and sturdy defenses, the proposed algorithm emerges as a potential cornerstone for safeguarding digital images in arenas like secure communication, data storage, and multimedia transmission. In sum, this research pushes the boundaries of high-security image encryption methodologies, catering to the burgeoning demands of the digital age. © 2013 IEEE.

关键词： Cryptography

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Improving Route-Finding Performance of Dijkstra Algorithm and Maintaining Path Visual Cue Using Douglas-Peucker Algorithm 23

Improving Route-Finding Performance of Dijkstra Algorithm an...

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8th International Conference on Sustainable information Engineering and Technology, SIET 2023

作者： Pinandito, Aryo Kharisma, Agi Putra Akbar, Muhammad Aminul Information System Department Faculty of Computer Science Universitas Brawijaya Malang Indonesia Media Game and Mobile Technology Laboratory Information System Department Faculty of Computer Science Universitas Brawijaya Malang Indonesia

ISBN: (纸本)9798400708503

Smartphones and their mobile applications have become an inseparable part of modern daily life. One of them is a public transportation app that helps people commute with public transportation. Many public transportation service providers have mobile applications as a companion for their customers to improve their quality of service. The application provides useful information and/or important features regarding their service, such as timetables or route finding for public transit. Such features run a slow algorithm to provide real-time or time-based information to the users. Dijkstra is one example of an algorithm that allows users to find the shortest path from one location to another in a public transit network. However, Dijkstra is categorized as a greedy algorithm because it looks at all possible solutions. It takes longer for Dijkstra to find the optimum solution when the number of points in a network graph is high. This study attempted to improve the overall route-finding processing time and reduce the memory footprint while keeping the visual cue of a route path reasonably consistent by utilizing Douglas-Peucker line simplification algorithm to simplify the path and reduce the number of points that Dijkstra algorithm processes. The result suggested that Douglas-Peucker simplification process took 0.5% additional memory footprint and put 5.13% additional processing time into the overall route-finding processing time. However, it reduces the overall memory footprint and processing time by 72.56% and 80.83%, respectively. The result conveyed a significant improvement in the practical implementation of Douglas-Peucker algorithm to reduce the input for the Dijkstra algorithm in finding the optimal route, thereby improving the overall route-finding performance. © 2023 ACM.

关键词： Transportation routes

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Intelligent Decision Support system for Higher Education Institutions 9

Intelligent Decision Support System for Higher Education Ins...

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9th International Conference on Signal Processing and Intelligent systems, ICSPIS 2023

作者： Afriliana, Nunik Meyliana Gaol, Ford Lumban Soeparno, Haryono Bina Nusantara University Binus Graduate Program Computer Science Program Computer Science Department Jakarta Indonesia Bina Nusantara University School of Information System Information System Department Jakarta Indonesia

ISBN: (纸本)9798350369922

To ensure the sustainability of their operations, higher education institutions have to establish a robust strategic plan. Higher education institutions have an obligation to continuously assess the quality of their educational offerings in every program to comply with the standards established by the government and other educational entities. The implementation of intelligent decision support systems is a feasible approach among other techniques for addressing the aforementioned challenges. An intelligent decision support system has the potential to enable the business to uphold its adherence to regulations, enhance operational effectiveness, and optimize overall performance. This study aimed to determine the current state of intelligent decision support systems through the implementation of a comprehensive literature review. The second goal is to evaluate the best practices, methods, and implementation of intelligent decision support systems within higher education institutions. The findings indicate that intelligent decision support systems have the capacity to serve as a viable option for higher education, particularly in terms of enhancing the accuracy and efficiency of decision-making processes. Intelligent decision support systems exhibit a strong correlation with the fields of data mining and computational intelligence techniques. Nevertheless, the amount of research conducted on this subject is limited. © 2023 IEEE.

关键词： Data mining

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Exploring the Flexibility and Accuracy of Sentiment Scoring Models through a Hybrid KNN-RNN-CNN Algorithm and ChatGPT

HighTech and Innovation Journal

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HighTech and Innovation Journal 2023年第2期4卷 315-326页

作者： Hariguna, Taqwa Ruangkanjanases, Athapol Department Information System Faculty of Computer Science Universitas Amikom Purwokerto Purwokerto Indonesia Chulalongkorn Business School Chulalongkorn University Bangkok Thailand

This study aimed to address the limitations of sentiment analysis by developing a more accurate and flexible sentiment scoring model using ChatGPT in combination with KNN, RNN, and CNN algorithms. To achieve this, primary data from ChatGPT and secondary data from Kaggle were utilized for training. The model's performance was evaluated, yielding an impressive accuracy rate of 88.17%. This research underscores ChatGPT's pivotal role in offering theoretical insights and precise data for diverse applications. The novelty of this study lies in its innovative approach of combining KNN, RNN, and CNN algorithms to create a more adaptable and accurate sentiment scoring model. Additionally, the primary data from ChatGPT greatly enhances the creation of precise and relevant training data across various topics and languages. Despite these achievements, there remains a need for further exploration of testing methods to mitigate the impact of data limitations on result generalizability. Moreover, it is acknowledged that the model's effectiveness may be diminished when applied to languages other than English. Nevertheless, this research provides a promising avenue for users seeking enhanced and precise sentiment analysis by integrating KNN, RNN, and CNN algorithms with ChatGPT. The findings of this study can serve as a solid foundation for future research endeavors in the advancement of sophisticated and effective sentiment analysis technologies. © 2023, Ital Publication. All rights reserved.

关键词： Sentiment analysis

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Dataset of Large Gathering Images for Person Identification and Tracking

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computers, Materials & Continua 2023年第3期74卷 6065-6080页

作者： Adnan Nadeem Amir Mehmood Kashif Rizwan Muhammad Ashraf Nauman Qadeer Ali Alzahrani Qammer H.Abbasi Fazal Noor Majed Alhaisoni Nadeem Mahmood Faculty of Computer and Information System Islamic University of MadinahMadinah42351Saudi Arabia Department of Computer Science and Information Technology Sir Syed University of Engineering and TechnologyKarachi75300Pakistan Department of Computer Science Federal Urdu University of ArtsScience&TechnologyIslamabad45570Pakistan Department of Physics Federal Urdu University of ArtsScience&TechnologyKarachi75300Pakistan James Watt School of Engineering University of GlasgowGlasgowG128QQUK Computer Sciences Department College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityRiyadh11671Saudi Arabia Department of Computer Science University of KarachiKarachi75270Pakistan

This paper presents a large gathering dataset of images extracted from publicly filmed videos by 24 cameras installed on the premises of Masjid Al-Nabvi,Madinah,Saudi *** dataset consists of raw and processed images reflecting a highly challenging and unconstraint *** methodology for building the dataset consists of four core phases;that include acquisition of videos,extraction of frames,localization of face regions,and cropping and resizing of detected face *** raw images in the dataset consist of a total of 4613 frames obtained fromvideo *** processed images in the dataset consist of the face regions of 250 persons extracted from raw data images to ensure the authenticity of the presented *** dataset further consists of 8 images corresponding to each of the 250 subjects(persons)for a total of 2000 *** portrays a highly unconstrained and challenging environment with human faces of varying sizes and pixel quality(resolution).Since the face regions in video sequences are severely degraded due to various unavoidable factors,it can be used as a benchmark to test and evaluate face detection and recognition algorithms for research *** have also gathered and displayed records of the presence of subjects who appear in presented frames;in a temporal *** can also be used as a temporal benchmark for tracking,finding persons,activity monitoring,and crowd counting in large crowd scenarios.

关键词： Large crowd gatherings a dataset of large crowd images highly uncontrolled environment tracking missing persons face recognition activity monitoring

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Clustering Rental Houses in Bahrain Using K Modes Algorithm 5

Clustering Rental Houses in Bahrain Using K Modes Algorithm

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5th International Conference on Data Analytics for Business and Industry, ICDABI 2024

作者： Khunji, Hooreya A. Zeki, Ahmed M. University of Bahrain Department of Computer Science Sakhir Bahrain University of Bahrain Department of Information System Sakhir Bahrain

ISBN: (纸本)9798350368710

Clustering is a key application in big data, essential for unsupervised learning. This study tackles the challenge of clustering a dataset with categorical housing attributes in Bahrain to uncover meaningful patterns and insights. Using the K-Modes algorithm, tailored for categorical data, the analysis includes features such as property type, location, number of bedrooms, and amenities. The performance is evaluated using the Silhouette score, which was 0.208, indicating moderate clustering effectiveness. The findings highlight distinct categories of residential properties in Bahrain, with Governorate being the feature with the highest differences among clusters. This research provides valuable insights into the Bahraini real estate market, aiding stakeholders in property development, urban planning, and housing policy formulation to make informed decisions and address market needs. © 2024 IEEE.

关键词： Adversarial machine learning

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Enhancing Cybersecurity with Transformers: Preventing Phishing Emails and Social Media Scams 7

Enhancing Cybersecurity with Transformers: Preventing Phishi...

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7th IEEE Conference on Dependable and Secure Computing, DSC 2024

作者： Al Daoud, Essam Al Daoud, Laith Asassfeh, Mahmoud Al-Shaikh, Ala'a Al-Sherideh, Ala'a Saeb Afaneh, Suha Zarqa University Department of Cybersecurity Zarqa Jordan Jordan University of Science and Technology Computer Information System Jordan

ISBN: (纸本)9798331540289

In this study, the enhancement of cybersecurity through the use of transformer models is explored, specifically focusing on the prevention of phishing emails and social media scams. Two modern datasets were utilized: the Phishing_Pot dataset, which provided phishing emails across 19 languages, and a collection of legitimate emails. Additionally, a dataset from Baaz, an Arabic social media platform, was compiled. Four approaches to phishing detection were compared: zero-shot learning with large language models (LLMs), transformers for feature extraction followed by random forest classification, fine-tuning of small language models (SLMs) on newly generated datasets, and an ensemble method combining the top models from the previous approaches. The experiments demonstrated that LLMs such as GPT-4o, Gemini 1.5, and Claude 3 achieved high accuracy, while SLMs showed improved performance post fine-tuning. The ensemble methods can be used to enhance the accuracy of fine-tuned SLM for phishing detection and scam prevention, reaching an accuracy of 98.50% for the first dataset and 95.25% for the second. © 2024 IEEE.

关键词： Phishing

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Comparing Linear Regression, Random Forest and Support Vector Machine for Rental House Price Forecasting in Bahrain 5

Comparing Linear Regression, Random Forest and Support Vecto...

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5th International Conference on Data Analytics for Business and Industry, ICDABI 2024

作者： Khunji, Hooreya A. Zeki, Ahmed M. University of Bahrain Department of Computer Science Sakhir Bahrain University of Bahrain Department of Information System Sakhir Bahrain

ISBN: (纸本)9798350368710

The real estate sector in Bahrain plays a pivotal role in its economy, with factors such as land price and house price predictions being crucial. Previous studies have addressed the importance of land price prediction in Bahrain [1]. In this study, data was collected from three famous real estate websites in Bahrain for a single day in March 2024. The dataset contains 14 attributes (such as property type, size, number of bedrooms, number of bathrooms, etc.) and 3084 instances, with 80% used for training and 20% for testing. 'A comparative analysis of three data mining methods, namely Linear Regression, Random Forest, and Support Vector Machine', for rental house price forecasting in Bahrain was conducted. Unlike previous studies, this research focuses on rental house prices in Bahrain using a diverse set of attributes. 'The performance of each model was evaluated using metrics such as Mean Absolute Error (MAE) and Root Mean Square Error (RMSE)'. Linear Regression (LR) had the highest MAE value of 0.1746, while SMOreg had a lower MAE of 0.1681. The lowest MAE was 0.1385, found with Random Forest (RF). The RMSE value for SMOreg was 0.2976, which was higher than LR's 0.2935. In contrast, the lowest RMSE value was for RF with 0.2394. This paper found the Random Forest data mining method to perform the best. © 2024 IEEE.

关键词： Support vector regression

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Enhancing Security and Privacy in Cloud Computing: Challenges and Solutions in the Digital Age 3

Enhancing Security and Privacy in Cloud Computing: Challenge...

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3rd International Conference on Creative Communication and Innovative Technology, ICCIT 2024

作者： Ginanjar, Muhamad Gilang Lubis, Muharman Ramadani, Luthfi Dwi Handayani, Dini Oktarina Telkom University Department of Information System Indonesia International Islamic University Malaysia Department of Computer Science Malaysia

ISBN: (纸本)9798350367492

The development of cloud computing offers significant benefits such as reduced operational costs, increased flexibility, and enhanced accessibility to technological resources. With the growing use of data driven by technological advancements and business expansion, organizations adopting cloud computing face various security and privacy challenges due to the involvement of sensitive data. This article aims to discuss the types of cloud computing deployment and services and examine critical security and privacy issues, including governance, data availability, data loss, data breaches, access control, data control, and the trust relationship between customers and service providers. A comprehensive review of existing literature and current practices is conducted to identify these challenges. Specific solutions are proposed for each identified issue to address these challenges effectively. The findings underscore the importance of continuously updating security strategies to protect data privacy. Therefore, it is imperative for organizations utilizing cloud services to adopt these strategies to safeguard their data privacy. © 2024 IEEE.

关键词： Cloud security

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Securing the IoT Cyber Environment: Enhancing Intrusion Anomaly Detection With Vision Transformers

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IEEE Access 2024年 12卷 82443-82468页

作者： Sana, Laraib Nazir, Muhammad Mohsin Yang, Jing Hussain, Lal Chen, Yen-Lin Ku, Chin Soon Alatiyyah, Mohammed Alateyah, Sulaiman Abdullah Por, Lip Yee Lahore College for Women University Department of Computer Science Punjab Lahore54000 Pakistan Universiti Malaya Department of Computer System and Technology Kuala Lumpur50603 Malaysia The University of Azad Jammu and Kashmir Department of Computer Science and IT Neelum Campus Azad Kashmir13230 Pakistan King Abdullah Campus The University of Azad Jammu and Kashmir Department of Computer Science and IT Azad Kashmir Muzaffarabad13100 Pakistan National Taipei University of Technology Department of Computer Science and Information Engineering Taipei106344 Taiwan Universiti Tunku Abdul Rahman Department of Computer Science Kampar31900 Malaysia Prince Sattam Bin Abdulaziz University College of Sciences and Humanities-Aflaj Department of Computer Science Al-Kharj16278 Saudi Arabia Qassim University College of Computer Department of Computer Engineering Buraydah52571 Saudi Arabia

The ever-expanding Internet of Things (IoT) landscape presents a double-edged sword. While it fosters interconnectedness, the vast amount of data generated by IoT devices creates a larger attack surface for cybercriminals. Intrusions in these environments can have severe consequences. To combat this growing threat, robust intrusion detection systems (IDS) are crucial. The data comprised by this attack is multivariate, highly complex, non-stationary, and nonlinear. To extract the complex patterns from this complex data, we require the most robust, optimized tools. Machine learning (ML) and deep learning (DL) have emerged as powerful tools for IDSs, offering high accuracy in detecting and preventing security breaches. This research delves into anomaly detection, a technique that identifies deviations from normal system behavior, potentially indicating attacks. Given the complexity of anomaly data, we explore methods to improve detection performance. This research investigates the design and evaluation of a novel IDS. We leverage and optimize supervised ML methods like tree-based Support Vector Machines (SVM), ensemble methods, and neural networks (NN) alongside the cutting-edge DL approach of long short-term memory (LSTM) and vision transformers (ViT). We optimized the hyperparameters of these algorithms using a robust Bayesian optimization approach. The implemented ML models achieved impressive training accuracy, with Random Forest and Ensemble Bagged Tree surpassing 99.90% of accuracy, an AUC of 1.00, an F1-score, and a balanced Matthews Correlation Coefficient (MCC) of 99.78%. While the initial deep learning LSTM model yielded an accuracy of 99.97%, the proposed ViT architecture significantly boosted performance with 100% of all metrics, along with a validation accuracy of 78.70% and perfect training accuracy. This study demonstrates the power of our new methods for detecting and stopping attacks on Internet of Things (IoT) networks. This improved detection offers

关键词： Intrusion detection

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