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4th International Conference Advancement in Data Science, E-Learning and information systems, ICADEIS 2022

作者： Priandhika Izzulhaq, Afif Fauzi, Rahmat Suakanto, Sinung Kadir Hassan Disina, Abdul Mahdin, Hairulnizam Anka Salihu, Ibrahim Information System Telkom University Bandung Indonesia Nigeria Army University Biu Borno State Department of Cyber Security Nigeria Universiti Tun Hussein Onn Malaysia Faculty of Computer Science and Information Technology Johor Malaysia Nile University of Nigeria Abuja Nigeria

ISBN: (纸本)9781665463874

information systems have an essential role in the development of technology and information in Indonesia. information systems are employed and utilized in many facets of society and organizations. Digitizing and utilizing this information system will often form an integrated relationship between several digital sectors into an ecosystem or what is commonly called a digital ecosystem. Digital ecosystem provides benefits for users by exchanging data between systems within it since only one user account could access several systems or services in the ecosystem. The development of user management in the digital ecosystem will serve as a solution to accommodate the increasing number of users of the digital ecosystem. Iterative incremental development methods are used in the development of user management in Ihya digital ecosystem. The system was also tested using scenario tests, user acceptance tests, and load tests. Based on the results of the tests, the Ihya digital ecosystem user management module has met user needs with a User Acceptance Test value of 82.5%. Further development can be done in this research, including using APIs in the data exchange process and adding a two-factor authentication feature to improve data security. © 2022 IEEE.

关键词： information systems

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Petri net-based illegal information flow detection method for node-chain access control model 23

Petri net-based illegal information flow detection method fo...

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Proceedings of the 2023 International Conference on Communication Network and Machine Learning

作者： Binyong Li Jie Fang Xianhui Deng Yuhan Sheng School of Cybersecurity (Xin Gu Industrial College) Chengdu University of Information Technology China and Advanced Cryptography and System Security Key Laboratory of Sichuan Province China Sichuan Institute of Computer Sciences China School of Cybersecurity Chengdu University of Information Technology China

ISBN: (纸本)9798400716683

To address the problem of illegal information flow in the node chain access control model constructed by the existing attribute-based access control (ABAC) model when applied to a large-scale node chain scenario with more than 100 nodes, an illegal information flow detection method is proposed for the node chain access control model based on the classical Petri net. The method introduces the reachable identity and reachable identity graph of the classical single-Token Petri net, which can analyze the illegal information flow in the ABAC-based node-chain access control model conveniently and visually. Meanwhile, it can effectively use the reachable identity state in the Petri net to detect the illegal information flow in the model. The case analysis shows that the method can effectively detect the illegal information flow appearing in the model.

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User Interface Design’s Impact on Customer Satisfaction and Loyalty in SaaS E-Commerce

User Interface Design’s Impact on Customer Satisfaction and...

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Creative Communication and Innovative technology (ICCIT), IEEE

作者： Eka Purnama Harahap Pandu Hermawan Dhiyah Ayu Rini Kusumawardhani Nina Rahayu Maulana Arif Komara Harry Agustian Master of Technology Information University of Raharja Tangerang Indonesia Dept. of Information System Bina Bangsa University Serang Indonesia Dept. of Information System Pandawan Sejahtera Indonesia Tangerang Indonesia Dept. of Computer Systems University of Raharja Tangerang Indonesia Dept. of Digital Bussiness CAI Sejahtera Indonesia Tangerang Indonesia Dept. of Computer Systems Sesindo Tangerang Indonesia

ISBN: (数字)9798350367492

ISBN: (纸本)9798350367508

This study aims to analyze the impact of user interface (UI) design on customer satisfaction and loyalty in SaaS e-commerce applications, using a quantitative approach to measure the perceptions of SaaS e-commerce application users and determine how visual aspects and interface functionality contribute to the user experience, with data analysis conducted using the Partial Least Squares Structural Equation Modeling (PLS-SEM) method, indicating that an intuitive and responsive interface design significantly influences increased customer satisfaction, which in turn strengthens their loyalty to the application; the study seeks to provide insights that developing an effective user interface is crucial for building long-term relationships with customers and reaffirms the importance of integrating good interface design in e-commerce application development strategies, and further suggests that incorporating user-centered design principles and continuously collecting user feedback can enhance the user experience, thereby allowing developers to create interfaces that not only attract users but also retain them, essential for e-commerce businesses aiming to improve user engagement and loyalty in a highly competitive market.

关键词： Customer satisfaction Buildings Psychology Virtual reality User interfaces User experience Mathematical models Electronic commerce Usability Business

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Enhancing Network Intrusion Detection Using Effective Stacking of Ensemble Classifiers With Multi-Pronged Feature Selection Technique 2

Enhancing Network Intrusion Detection Using Effective Stacki...

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2nd International Conference on Emerging Trends in Electrical, Control, and Telecommunication Engineering, ETECTE 2023

作者： Rahman, Saifur Mursal, Salim Nasar Faraj Latif, Muhammad Armghan Mushtaq, Zohaib Irfan, Muhammad Waqar, Ali Najran University College of Engineering Department of Electrical Engineering Najran Saudi Arabia Cleveland State University Department of Computer and Information System United States University of Sargodha College of Engineering and Technology Department of Electrical Engineering Sargodha Pakistan North China electric power University Beijing China

ISBN: (纸本)9798350305654

information security depends on Network Intrusion Detection (NID), which properly identifies network threats. This work explores simulating a NID system by stacking ensemble classifiers with various feature selection methods. We used the NSL-KDD dataset for the investigation. The binary classification performance integrates all assaults into one class and treats another target variable as usual. Random Forest (RF), Bagging Classifier (BC), and Extra Tree Ensemble (ETE) using Logistics Regression (LR) as a meta estimator. Chi2-square (Chi2) and Recursive Feature Elimination (RFE) are also used for feature selection. The experimental findings suggest that the proposed technique is ideal for developing a classification model with high accuracy and outperforms standard machine learning classification methods. The Chi 2 attribute selection strategy with modest characteristics has the greatest accuracy of 99.84 %. A careful comparison with prior research shows that our technique produces state-of-the-art performance with reduced computing costs. © 2023 IEEE.

关键词： Feature Selection

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Modified Aquila Optimizer Feature Selection Approach and Support Vector Machine Classifier for Intrusion Detection system

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Multimedia Tools and Applications 2024年第21期83卷 59887-59913页

作者： Abualigah, Laith Ahmed, Saba Hussein Almomani, Mohammad H. Zitar, Raed Abu Alsoud, Anas Ratib Abuhaija, Belal Hanandeh, Essam Said Jia, Heming Elminaam, Diaa Salama Abd Elaziz, Mohamed Abd Computer Science Department Al Al-Bayt University Mafraq25113 Jordan Department of Electrical and Computer Engineering Lebanese American University Byblos13-5053 Lebanon Hourani Center for Applied Scientific Research Al-Ahliyya Amman University Amman19328 Jordan MEU Research Unit Middle East University Amman11831 Jordan Applied Science Research Center Applied Science Private University Amman11931 Jordan School of Computer Sciences Universiti Sains Malaysia Pulau Pinang11800 Malaysia School of Engineering and Technology Sunway University Malaysia Petaling Jaya27500 Malaysia Department of Mathematics Facility of Science The Hashemite University P.O box 330127 Zarqa13133 Jordan Sorbonne Center of Artificial Intelligence Sorbonne University-Abu Dhabi Abu Dhabi United Arab Emirates Department of Computer Science Wenzhou-Kean University Wenzhou China Department of Computer Information System Zarqa University P.O. Box 13132 Zarqa Jordan School of Information Engineering Sanming University Sanming365004 China Information Systems Department Faculty of Computers and Artificial Intelligence Benha University Benha12311 Egypt Computer Science Department Faculty of Computer Science Misr International University Cairo12585 Egypt Faculty of Computer Science & Engineering Galala University Suze435611 Egypt Ajman University Ajman346 United Arab Emirates Department of Mathematics Faculty of Science Zagazig University Zagazig44519 Egypt

With the ever-expanding ubiquity of the Internet, wireless networks have permeated every facet of modern life, escalating concerns surrounding network security for users. Consequently, the demand for a robust Intrusion Detection system (IDS) has surged. The IDS serves as a critical bastion within the security framework, a significance further magnified in wireless networks where intrusions may stem from the deluge of sensor data. This influx of data, however, inevitably taxes the efficiency and computational speed of IDS. To address these limitations, numerous strategies for enhancing IDS performance have been posited by researchers. This paper introduces a novel feature selection method grounded in Support Vector Machine (SVM) and harnessing the innovative modified Aquila Optimizer (mAO) for Intrusion Detection systems in Wireless Sensor Networks. To evaluate the efficacy of our approach, we employed the KDD'99 dataset for testing and benchmarking against established methods. Multiple performance metrics, including accuracy, detection rate, false alarm rate, feature count, and execution time, were utilized for assessment. Our comparative analysis reveals the superiority of the proposed method, with standout results in terms of feature reduction, detection accuracy, and false alarm mitigation, yielding significant improvements of 11%, 98.76%, and 0.02%, respectively. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2023.

关键词： Network security

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A Hierarchical Traffic Sign Image Recognition Algorithm Keeping the Local Topology

A Hierarchical Traffic Sign Image Recognition Algorithm Keep...

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Advanced Algorithms and Control Engineering (ICAACE), International Conference on

作者： Longhui Liu Wenhui Gong Wenjie Wang Zhiqiang Zeng Wei Li Xiamen Global Navigation Satellite System Development & Application Co. Ltd. Xiamen China School of Computer and Information Engineering Xiamen University of Technology Xiamen China

ISBN: (数字)9798350361445

ISBN: (纸本)9798350361452

Traffic sign recognition is crucial in enabling intelligent driving. This paper proposes a robust hierarchical traffic sign image recognition algorithm that maintains local topology. The algorithm is divided into two matching levels: rough matching and fine matching. The multi-scale image's Oriented Fast and Rotated BRIEF (ORB) [1] are extracted in coarse matching. Hamming distance is adopted to measure the similarity of feature points to execute rough and fast matching. In precise matching, the matched candidate feature points are further filtered using the number of features of surrounding points, that is, the Shape Context [2] feature, to achieve precise matching. This hierarchical matching method maintains the local topological characteristics of the image while performing robust and fast matching. In the experiment, the data set provided by Changsha University of Science and technology was used to assess the performance of the algorithm presented in this article.

关键词： Matched filters Image recognition Costs Shape Feature extraction Mobile handsets Topology

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Robust Phishing URL Classification Using FastText Character Embeddings and Hybrid Deep Learning 3

Robust Phishing URL Classification Using FastText Character ...

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3rd IEEE International Conference on Robotics, Automation, Artificial-Intelligence and Internet-of-Things, RAAICON 2024

作者： Al-Sakib, Abdullah Limon, Zishad Hossain Sakib, Anamul Pranto, Md Nuruzzaman Islam, Md Ariful Sultana, Shayma Ahmed, Md. Redwan Rahman, Shafiur Westcliff University Dept. of Information Technology CA92614 United States Westcliff University Dept. of Computer Science CA92614 United States International American University Dept. of Business Administration CA90010 United States International American University Dept. of Management Information System CA90010 United States East West University Dept. of Computer Science and Engineering Dhaka1212 Bangladesh Daffodil International University Dept. of Software Engineering Dhaka1216 Bangladesh

ISBN: (纸本)9798331534400

Phishing attacks are a major cybersecurity threat that resulted in over 1.2 million incidents in the first half of 2020. These attacks caused substantial financial losses and posed risks to individuals and organizations. Being able to identify fraudulent websites is crucial in order to effectively address these potential risks. This study introduces a novel method for detecting phishing URLs by using word and character embeddings to capture complex URL patterns. We used a dataset of 80,000 URLs, including 50,000 legitimate ones and 30,000 phishing instances, and applied thorough preprocessing techniques. We utilized word embeddings in FastText to handle unseen words, with the added advantage of n-gram representations. Additionally, we captured character-level features through dense character embeddings. We trained several machine learning and deep learning models, and one model, the Convolutional Bidirectional LSTM (CBiLSTM), stood out with an accuracy of 99.01% and an F1-score of 99.08%. Furthermore, we made a thorough comparison with the most advanced techniques available, and our findings demonstrated clear superiority over previous research. This study presents an effective approach for classifying phishing URLs, providing a valuable tool to combat fraud and protect against identity theft, thereby helping to minimize the financial and emotional harm experienced by victims. © 2024 IEEE.

关键词： Phishing

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Improved Attentive Recurrent Network for Applied Linguistics-Based Offensive Speech Detection

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computer systems Science & Engineering 2023年第11期47卷 1691-1707页

作者： Manar Ahmed Hamza Hala J.Alshahrani Khaled Tarmissi Ayman Yafoz Amira Sayed A.Aziz Mohammad Mahzari Abu Sarwar Zamani Ishfaq Yaseen Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Applied Linguistics College of LanguagesPrincess Nourah Bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Computer Sciences College of Computing and Information SystemUmm Al-Qura UniversityMakkahSaudi Arabia Department of Information Systems Faculty of Computing and Information TechnologyKing Abdulaziz UniversityJeddahSaudi Arabia Department of Digital Media Faculty of Computers and Information TechnologyFuture University in EgyptNew Cairo11835Egypt Department of English College of Science&HumanitiesPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia

Applied linguistics is one of the fields in the linguistics domain and deals with the practical applications of the language studies such as speech processing,language teaching,translation and speech *** ever-growing Online Social Networks(OSNs)experience a vital issue to confront,i.e.,hate *** the OSN-oriented security problems,the usage of offensive language is the most important threat that is prevalently found across the *** on the group targeted,the offensive language varies in terms of adult content,hate speech,racism,cyberbullying,abuse,trolling and *** these,hate speech is the most intimidating form of using offensive language in which the targeted groups or individuals are intimidated with the intent of creating harm,social chaos or *** Learning(ML)techniques have recently been applied to recognize hate speech-related *** current research article introduces a Grasshopper Optimization with an Attentive Recurrent Network for Offensive Speech Detection(GOARN-OSD)model for social *** GOARNOSD technique integrates the concepts of DL and metaheuristic algorithms for detecting hate *** the presented GOARN-OSD technique,the primary stage involves the data pre-processing and word embedding ***,this study utilizes the Attentive Recurrent Network(ARN)model for hate speech recognition and *** last,the Grasshopper Optimization Algorithm(GOA)is exploited as a hyperparameter optimizer to boost the performance of the hate speech recognition *** depict the promising performance of the proposed GOARN-OSD method,a widespread experimental analysis was *** comparison study outcomes demonstrate the superior performance of the proposed GOARN-OSD model over other state-of-the-art approaches.

关键词： Applied linguistics hate speech offensive language natural language processing deep learning grasshopper optimization algorithm

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A Comparative Analysis of Joomla, Drupal, Wordpress and ***: Exploring Features, Performance, and Suitability

A Comparative Analysis of Joomla, Drupal, Wordpress and ***:...

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computer Science and Data Engineering (CSDE), IEEE Asia-Pacific Conference on

作者： Ashwin Ashika Prasad Neeraj Anand Sharma Anal Kumar Department of Computer Science and Mathematics School of Science and Technology The University of Fiji Department of Computing Sciences and Information System College of Engineering Science and Technology Fiji National University

In the dynamic landscape of web development, the judicious choice of platform and structure plays a pivotal role. This comprehensive study conducts a thorough comparison of four prominent web development technologies: ***, Joomla, Drupal, and WordPress. With a primary focus on scrutinizing features, performance, and overall suitability, the research aims to provide valuable insights for developers, businesses, and decision-makers. By objectively evaluating the merits and limitations of each technology, this study seeks to empower stakeholders with essential knowledge, enabling them to make informed decisions at the onset of web development projects.

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A Comparison Study of FC-RDVM and RIWM Router Placement Methods for WMNs: Performance Evaluation Results by WMN-PSOHC Simulation system Considering Chi-Square Distribution and Different Instances 1

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37th International Conference on Advanced information Networking and Applications, AINA 2023

作者： Sakamoto, Shinji Barolli, Admir Liu, Yi Kulla, Elis Barolli, Leonard Takizawa, Makoto Department of Information and Computer Science Kanazawa Institute of Technology 7-1 Ohgigaoka Ishikawa Nonoichi921-8501 Japan Department of Information Technology Aleksander Moisiu University of Durres L.1 Rruga e Currilave Durres Albania Department of Computer Science National Institute of Technology Oita College 1666 Maki Oita870-0152 Japan Department of System Management Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Information and Communication Engineering Fukuoka Institute of Technology 3-30-1 Wajiro-Higashi Higashi-Ku Fukuoka811-0295 Japan Department of Advanced Sciences Faculty of Science and Engineering Hosei University Tokyo Kajino-Machi Koganei-Shi184-8584 Japan

ISBN: (数字)9783031290565

ISBN: (纸本)9783031290558

In this work, we deal with the node placement problem in Wireless Mesh Networks (WMNs). We present a hybrid intelligent simulation system called WMN-PSOHC, which combines Particle Swarm Optimization (PSO) and Hill Climbing (HC). We implement in WMN-PSOHC system the Fast Convergence Rational Decrement of Vmax Method (FC-RDVM) and Random Inertia Weight Method (RIWM) router replacement methods. By using WMN-PSOHC system, we carry out simulations the access the performance of these two methods considering Chi-Square distribution of mesh clients and two different instances. The simulation results show that FC-RDVM performs better then RIWM in the considered scenario. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Particle swarm optimization (PSO)

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