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Information Technology (ACIT)

作者： Osama S AlJamal Mahmoud AlJamal Ayoub Alsarhan Mohammad Aljaidi Saif Okour Issa Al-Aiash Ahmad Al-Qerem Essam Al Daoud Ali Elrashidi Department of Computer Science Al Hussein Technical University Amman Jordan Department of Cybersecurity Faculty of Science and Information Technology Irbid National University Irbid Jordan Department of Information Technology The Hashemite University Zarqa Jordan Department of Computer Science Zarqa University Zarqa Jordan Department of Software Engineering Irbid National University Irbid Jordan Department of Cybersecurity Zarqa University Zarqa Jordan Department of Electrical Engineering University of Business and Technology Jeddah Saudi Arabia

ISBN: (数字)9798331540012

ISBN: (纸本)9798331540029

Designing and simulating a network infrastructure for multi-branch organizations is essential for ensuring seamless connectivity and efficient operations. This paper addresses the challenges faced by organizations in maintaining consistent and reliable network connectivity across multiple locations, including managing network traffic, ensuring security, and optimizing resource allocation while keeping costs under control. The study presents a comprehensive methodology involving the conceptualization of network ideas, designing the network architecture, adding necessary devices and tools, configuring these devices according to the network topology, and troubleshooting to ensure optimal performance. Key elements such as VLANs, routers, switches, and routing protocols like RIP, OSPF, and EIGRP are incorporated, with the entire design and simulation conducted using Cisco Packet Tracer. The findings demonstrate that full connectivity between headquarters and branch sites can be achieved within a budget of $40,000. This paper provides a practical guide for implementing similar network projects, emphasizing the importance of detailed configurations and troubleshooting steps, and recommends adopting this methodology to enhance network infrastructure efficiently and cost-effectively.

关键词： Performance evaluation Costs Organizations Telecommunication traffic Network architecture Routing protocols Security Resource management Reliability Information technology

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A Two-stage Anomaly Detection System for Fog Computing Services 5

A Two-stage Anomaly Detection System for Fog Computing Servi...

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5th IEEE International Conference on Electro-Computing Technologies for Humanity, NIGERCON 2024

作者： Ogundoyin, Sunday Oyinlola Adeniyi Kamil, Ismaila Ojedokun, Isaac Adewale Babalola, John Oluwaseun Nyangaresi, Vincent Omollo Bowen University Electrical and Electronic Engineering Programme Iwo Nigeria University of Ibadan Department of Electrical and Electronic Engineering Ibadan Nigeria Jaramogi Oginga Odinga University of Science and Technology Department of Computer Science and Software Engineering Bando40601 Kenya Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Department of Applied Electronics Chennai602105 India

ISBN: (纸本)9798331542559

Fog computing enhances traditional cloud computing by bringing resources closer to edge devices, enabling faster response times and supporting mission-critical applications. However, its distributed nature makes it vulnerable to cyber-attacks like distributed denial-of-service (DDoS), resulting in service disruption and performance degradation. To mitigate this attack, a two-stage anomaly detection system (ADS) is proposed in this paper. The first stage uses AdaBoost (AB) ensemble machine learning to classify events as obstructive or non-obstructive. Obstructive events are offloaded to the cloud where they are classified into normal or specific attack categories using Long Short-term Memory Encoder-Decoder (LSTM-ED). The results demonstrate that the AB-based model achieves exceptional performance, with nearly 100% in accuracy, precision, recall, and a minimal mean squared error (MSE) of 8.34 × 10-5. The LSTM-ED model also performs well, with accuracy and MSE at 99.50% and 0.02686, respectively. © 2024 IEEE.

关键词： Fog computing

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Towards Efficient Auction Design with ROI Constraints 24

Towards Efficient Auction Design with ROI Constraints

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Xinyu Tang Hongtao Lv Yingjie Gao Fan Wu Lei Liu Lizhen Cui School of Software & C-FAIR Shandong University Jinan China Department of Computer Science and Engineering Shanghai Jiao Tong University Shanghai China School of Software & C-FAIR Shandong University & Shandong Research Institute of Industrial Technology Jinan China

ISBN: (纸本)9798400704864

Online advertising stands as a significant revenue source of the Internet. Recently, the trend among advertisers tilting towards the use of auto-bidding tools has heralded the emergence of a new model of bidders operating with constraints related to return on investment (ROI). However, most of the current research on ROI-constrained bidders in auction design only focuses on either the ROI constraints or values of bidders being private, while it is more practical to keep them both private in reality. Designing a truthful mechanism for bidders with both private values and ROI constraints introduces complexities because of the characteristics of designing mechanisms with multiple parameters. To remedy this, we divide bidders into binary classes: the traditional utility maximizers (UMs) who can be viewed as having an ROI constraint of 1, and the ROI-constrained bidders (RBs) who share a fixed ROI constraint denoted as γ. This framework retains the essence of multi-parameter mechanism design but transitions this into a more tractable form. Then we introduce a novel auction mechanism, cleverly combining the conventional VCG mechanism and an existing mechanism for public ROI-constrained bidders which is called Cavallo's mechanism. Our mechanism can achieve an approximation ratio of 3 over 2 on social welfare. Additionally, we unearth new insights into the limitations posed by ROI constraints. When the ROI constraint γ exceeds 2, the lower bound of social welfare is 5 over 4; when it falls below 2, the lower bound becomes 3+γ over 2+3γ-γ2.

关键词： incentive compatibility mechanism design return on investment

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Medical Expert System to Diagnose the Most Common Psychiatric Diseases 21

Medical Expert System to Diagnose the Most Common Psychiatri...

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7th International Conference on engineering and MIS, ICEMIS 2021

作者： Alsagheer, Eman A. Rajab, Hamzah A. Elnajar, Khadiga M.Y. Department of Computer Science Faculty of Information Technology University of Benghazi Benghazi Libya Department of Software Engineering Faculty of Information Technology University of Benghazi Benghazi Libya

ISBN: (纸本)9781450390446

Expert Systems are programs that employ artificial intelligence and mimic the performance of human experts in a certain field by gathering and capturing expert information. In this paper, we present a rule-based expert system to provide the diagnosis for the most common psychiatric diseases to facilitate the diagnosis process. This system diagnoses four types of psychiatric diseases Anxiety Disorder (generalized anxiety, panic disorder, OCD, and phobic disorder), Stress-related disorder, Depression, and Schizophrenia using the International Classification of Diseases 10th Revision. The knowledge used in this system is acquired from domain experts, and relevant medical books and documents to capture knowledge that is valid as much as possible. Then, the acquired knowledge is demonstrated using a diagnostic chart, and decision table that represents the signs and symptoms involved in the diagnosis. A rule-based expert system and production rules that represent the domain knowledge are built using SWI-PROLOG Editor. The system is based on backward chaining which starts with possible goals and attempts to collect information that confirms it. The testing result shows the average accuracy of the system register 80% in diagnosing patients with psychiatric diseases. © 2021 ACM.

关键词： Expert systems

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Robust Adversarial Defenses in Federated Learning: Exploring the Impact of Data Heterogeneity

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IEEE Transactions on Information Forensics and Security 2025年 20卷 6005-6018页

作者： Li, Qian Wu, Di Zhou, Dawei Lin, Chenhao Liu, Shuai Wang, Cong Shen, Chao Xi’an Jiaotong University Ministry of Education Key Laboratory for Intelligent Networks and Network Security School of Cyber Science and Engineering Xi’an 710049 China Xi’an Jiaotong University School of Computer Science and Technology Xi’an 710049 China Xidian University Xi’an 710071 China Xi’an Jiaotong University School of Software Xi’an 710049 China City University of Hong Kong Department of Computer Science Hong Kong

Federated Learning (FL) enables geographically distributed clients to collaboratively train machine learning models by exchanging local model parameters while preserving data privacy. In practice, FL faces two critical challenges. First, it is vulnerable to security issues as malicious clients would artificially harm the functionality of FL by launching poisoning attacks. Second, the inherent data heterogeneity among clients (termed Non-IID data in FL) naturally arises from distributed data ownership and significantly degrades model convergence and accuracy. However, with studies separately devoted to these two research lines, the interplay between data heterogeneity and security remains poorly understood. In this paper, we systematically investigate the relationship between data heterogeneity and adversarial robustness in FL. Specifically, we propose novel data partitioning algorithms that simulate Label-Conditional Non-IID and Feature-Conditional Non-IID with quantifiable heterogeneity levels. Further, we conduct extensive experiments to evaluate classical defense methods in the practical FL environment under state-of-the-art untargeted attacks. With results in various settings, we separately analyze the connection between Non-IID to defenses and attacks. Regarding attacks, with similar effects on models, Non-IID impacts the training in a different way compared with attacks. The interaction between attacks and Non-IID provides an opportunity to cause severe damage to FL. Regarding defenses, Non-IID induces heterogeneity in model distribution among clients which raises the difficulty of maintaining fidelity and robustness for defense methods. © 2005-2012 IEEE.

关键词： Adversarial Defenses Federated Learning Poisoning attacks Security

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Authentic Learning of Machine Learning in Cybersecurity with Portable Hands-on Labware: Neural Network Algorithms for Network Denial of Service (DOS) Detection

Authentic Learning of Machine Learning in Cybersecurity with...

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2022 IEEE International Conference on Big Data, Big Data 2022

作者： Faruk, Md Jobair Hossain Shahriar, Hossain Qian, Kai Lo, Dan Whitman, Michael Cuzzocrea, Alfredo Wu, Fan Clincy, Victor Department of Software Engineering and Game Development Kennesaw State University United States Department of Information Technology Kennesaw State University United States Institute for Cyber Workforce Development Kennesaw State University United States Department Computer Science Kennesaw State University United States IDEA Lab University of Calabria Rende Italy Loria Nancy France Department of Computer Science Tuskegee University United States

ISBN: (纸本)9781665480451

The primary goal of the authentic learning approach is to engage and motivate students in a learning environment that encourages all students in learning. This approach provides students with hands-on experiences in solving real-world security problems. We designed and developed ten learning modules based on 10 cybersecurity cases with different ML solutions. Each learning module consists of pre-lab, lab, and post-lab (Pre/Lab/Post) activities. All portable labs are made available on Google CoLab for ML to cybersecurity so that students can access and practice these hands-on labs anywhere and anytime without time tedious installation and configuration which will engage students in learning concepts and getting more experience for hands-on problem-solving skills. In this paper, we adopt Neural Network Algorithms for Network Denial of Service (DOS) Detection where we apply the KDDCup 1999 datasets contain a standard set of data to be audited, which includes a wide variety of intrusions simulated in a military network environment. Our primary goal of this lab is to show whether a link is a malicious or safe connection. Our demonstration shows an achieved accuracy of 99.89%. © 2022 IEEE.

关键词： Students

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Enhancing Content-Based Histopathology Image Retrieval Using QR Code Representation

Enhancing Content-Based Histopathology Image Retrieval Using...

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IEEE Symposium Series on Computational Intelligence (SSCI)

作者： Hamidreza Rouzegar Shahryar Rahnamayan Azam Asilian Bidgoli Masoud Makrehchi Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Engineering Department Brock University St. Catharines ON Canada Faculty of Science Wilfrid Laurier University Waterloo ON Canada

The growing field of Content-Based Medical Image Retrieval (CBMIR) plays an integral role in the diagnosis and treatment plan of numerous diseases, including cancer. However, the effective representation of gigapixel medical images via a large number of extracted features remains a challenging task, crucially influencing other processes in a digital workflow. In this paper, we propose a novel QR code representation strategy to enhance retrieval performance. Unlike the traditional one-dimensional binary representation methods (i.e., barcodes), this 2D approach captures more intricate and informative patterns from the features by differentiating each pair of features resulting in a 2D binary vector. We delve into three distinct QR code generation strategies, namely, the Thresholding QR, the MinMax QR, and the Hybrid QR, each offering unique strengths. Our experiments on representing whole slide images of the Cancer Genome Atlas (TCGA) dataset reveal that while the Hybrid QR tends to provide balanced performance, there are instances where the other two methods outshine. Even though this approach requires more memory usage, the considerable enhancement in accuracy justifies this trade-off; obviously, in medical applications, accuracy holds the highest priority. Hence, the findings indicate that QR codes can effectively improve the performance of CBMIR systems by not only accelerating the retrieval process but also increasing the accuracy of image retrieval, leading to potentially more accurate diagnoses and treatment planning.

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Impact of COVID-19 on Predicting 2020 US Presidential Elections on Social Media 16th

Impact of COVID-19 on Predicting 2020 US Presidential Electi...

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16th International Conference on Information Technology and applications, ICITA 2022

作者： Khan, Asif Zhang, Huaping Boudjellal, Nada Hayat, Bashir Dai, Lin Ahmad, Arshad Al-Hamed, Ahmed School of Computer Science and Technology Beijing Institute of Technology Beijing100081 China The Faculty of New Information and Communication Technologies University Abdelhamid Mehri Constantine 2 Constantine25000 Algeria Institute of Management Sciences Peshawar Peshawar Pakistan Institute of Software Systems Engineering Johannes Kepler University Linz4040 Austria Department of IT and Computer Science Pak-Austria Fachhochschule: Institute of Applied Sciences and Technology Mang Khanpur Road Haripur22620 Pakistan

ISBN: (纸本)9789811993305

By the beginning of 2020, the world woke up to a global pandemic that changed people’s everyday lives and restrained their physical contact. During those times Social Media Platforms (SMPs) were almost the only mean of individual-to-individual and government-to-individuals communications. Therefore, people’s opinions were more expressed on SM. On the other hand, election candidates used SM to promote themselves and engage with voters. In this study, we investigate how COVID-19 affected voters’ opinions through the months of the US presidential campaign and eventually predict the 2020 US Presidential Election results using Twitter’s data. Mainly two types of experiments were conducted and compared;(i) transformer-based, and (ii) rule-based sentiment analysis (SA). In addition, vote shares for the presidential candidates using both approaches were predicted. The results show that the rule-based approach nearly predicts the right winner, Joe Biden with MAE 2.1, outperforming the predicted results from CNBC, Economist/YouGov, and transformer-based (BERTweet) approach, except for RCP (MAE 1.55). © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： COVID-19

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New Bounds on the Accuracy of Majority Voting for Multi-Class Classification

arXiv

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arXiv 2023年

作者： Aeeneh, Sina Zlatanov, Nikola Yu, Jiangshan The Department of Electrical and Computer Systems Engineering Monash University ClaytonVIC3800 Australia The Department of Computer Science and Engineering Innopolis University Respublika Tatarstan Innopolis420500 Russia The Department of Software Systems and Cybersecurity Monash University ClaytonVIC3800 Australia

Majority voting is a simple mathematical function that returns the value that appears most often in a set. As a popular decision fusion technique, the majority voting function (MVF) finds applications in resolving conflicts, where a number of independent voters report their opinions on a classification problem. Despite its importance and its various applications in ensemble learning, data crowd-sourcing, remote sensing, and data oracles for blockchains, the accuracy of the MVF for the general multi-class classification problem has remained unknown. In this paper, we derive a new upper bound on the accuracy of the MVF for the multi-class classification problem. More specifically, we show that under certain conditions, the error rate of the MVF exponentially decays toward zero as the number of independent voters increases. Conversely, the error rate of the MVF exponentially grows with the number of independent voters if these conditions are not met. We first explore the problem for independent and identically distributed voters where we assume that every voter follows the same conditional probability distribution of voting for different classes, given the true classification of the data point. Next, we extend our results for the case where the voters are independent but non-identically distributed. Using the derived results, we then provide a discussion on the accuracy of the truth discovery algorithms. We show that in the best-case scenarios, truth discovery algorithms operate as an amplified MVF and thereby achieve a small error rate only when the MVF achieves a small error rate, and vice versa, achieve a large error rate when the MVF also achieves a large error rate. In the worst-case scenario, the truth discovery algorithms may achieve a higher error rate than the MVF. Finally, we confirm our theoretical results using numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Data fusion

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Towards a global machine learning model to fill gaps in flood frequency: Random Forest to estimate MNDWI in Bangladesh

Towards a global machine learning model to fill gaps in floo...

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Machine Intelligence for GeoAnalytics and Remote Sensing (MIGARS), International Conference on

作者： James Houghton Carolynne Hultquist James Atlas Andrew Zimmer Computer Science and Software Engineering University of Canterbury Christchurch NZ School of Earth and Environment University of Canterbury Christchurch NZ Department of Earth Sciences Montana State University Bozeman MT USA

ISBN: (数字)9798350389678

ISBN: (纸本)9798350389685

An understanding of historical trends of flooding is essential to capture current risk as more people are exposed to flood hazards than ever before. Flood modeling relies on accurate and frequent remote sensing inputs to produce reliable flood risk results. However, remote sensing datasets are limited by temporal resolution which, in the case of Landsat, leaves 87% of days without observation. This study aims to address these gaps in observation using a Random Forest model to predict the Modified Normalized Difference Water Index (MNDWI) in Landsat images by fusing gridded precipitation and elevation data. The trained model is then used to estimate MNDWI for when there are no Landsat observations. The model was evaluated on a 50 by 50 pixel test patch achieving a residual squared error between the true average MNDWI and the estimated average MNDWI of 0.48.

关键词： Time-frequency analysis Precipitation Predictive models Market research Data models Floods Reliability

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