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Empirical Evaluations of Machine Learning Effectiveness in Detecting Web Application Attacks 7th

Empirical Evaluations of Machine Learning Effectiveness in...

7th EAI International Conference on Future Access Enablers of Ubiquitous and Intelligent Infrastructures, EAI FABULOUS 2023

作者： Ismail, Muhusina Alrabaee, Saed Harous, Saad Choo, Kim-Kwang Raymond Department of Information Systems and Security CIT United Arab Emirates University Al Ain United Arab Emirates Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio United States

ISBN: (纸本)9783031500503

Web applications remain a significant attack vector for cybercriminals seeking to exploit application vulnerabilities and gain unauthorized access to privileged data. In this research, we evaluate the efficacy of eight supervised machine learning algorithms - Naive Bayes, Decision Tree, AdaBoost, Random Forest, Logistic Regression, K-Nearest Neighbor (KNN), Support Vector Machine (SVM), and Artificial Neural Network (ANN) - in detecting and countering web application attacks. Our results indicate that KNN and Random Forest classifiers achieve an accuracy rate of 89% and an area under the curve of 94% on the CSIC HTTP dataset, a commonly used benchmark in the field. Meanwhile, the Naive Bayes classifier proves the most efficient, taking the least computational time when differentiating between malicious and benign HTTP requests. These findings may help direct future efforts towards more efficient, machine learning-driven defenses against web application attacks. © ICST Institute for Computer Sciences, Social informatics and Telecommunications Engineering 2024.

关键词： Machine Learning Web Attacks Web Vulnerabilities

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Incompletion Rate Factors in MOOC: A Systematic Literature Review

Incompletion Rate Factors in MOOC: A Systematic Literature R...

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2023 International Conference on University Teaching and Learning, InCULT 2023

作者： Karsen, Marisa Uzir, Nora'ayu Ahmad Abdul Rahman, Safawi Bin Kurniawan, Yohannes School of Information Science College of Computing Informatics and Mathematics University Teknologi Mara Information Systems Department Puncak Perdana Campus Malaysia School of Information Systems Bina Nusantara University Jakarta11480 Indonesia School of Information Systems Bina Nusantara University Information Systems Department Jakarta11480 Indonesia

ISBN: (纸本)9798350315745

Massive Online Open Courses (MOOCs) that are based on open e-learning courses might be one of the most versatile ways to offer access to quality education, regardless of the learners background. However, literature indicate that less than 10% of learners completed the MOOC courses they registered. Despite the efficient creation, and distribution of knowledge by taking advantage of advanced educational technology, yet the minimal completion rate in MOOCs remains unsolved. With the research question formulated as 'Why is the MOOCs completion rate among learners and professionals in higher education institution is low?' this paper presents the systematic literature review in which the researchers identified the factors that the learners acknowledged as the reason in the MOOC incompletion. Researchers also identified the factors that influence learners to complete MOOC courses and the research instruments were used by the prior studies to examine the motivation factors of learners in completing the MOOC courses. Based on the findings of the study and through the synthesis of the literature, this study presents a new perspective that can be used as a point of reference for future research and development to promote a higher MOOC completion rate. © 2023 IEEE.

关键词： Motivation

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Integrating Deep Learning and Ant Colony Optimization for Improved Shortest Route Problem Solving 5

Integrating Deep Learning and Ant Colony Optimization for Im...

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5th International Conference on Big-data Service and Intelligent Computation, BDSIC 2023

作者： Turky, Ayad Hussain, Abir Bettayeb, Anissa M. Serhani, Mohamed Adel Department of Computer Science College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates Department of Electrical Engineering College of Engineering University of Sharjah Sharjah United Arab Emirates Information Technology Center University of Sharjah Sharjah United Arab Emirates Department of Information Systems College of Computing and Informatics University of Sharjah Sharjah United Arab Emirates

ISBN: (纸本)9798400708923

Determining the best shortest path between locations in intelligent transportation systems is crucial but challenging. Traditional approaches, which assume fixed travel times, fall short of accurately reflecting dynamic factors such as weather, time, and day. This paper introduces a novel two-stage approach that integrates deep learning and ant colony optimization to address this limitation. Firstly, deep learning is employed to predict travel times, incorporating weather conditions as input for improved accuracy. Secondly, an ant colony optimization algorithm is utilized to identify the shortest route based on the predicted travel times. We evaluated the proposed approach using the New York City taxi benchmark dataset and observed superior performance compared to current state-of-the-art algorithms. © 2023 ACM.

关键词： Travel time

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Fostering interdisciplinary data cultures through early career development: The RDA/US data share fellowship

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Data Science Journal 2021年第1期20卷 1-19页

作者： Kouper, Inna Scheidt, Lois A. Plale, Beth A. Department of Informatics School of Informatics Computing and Engineering Indiana University Bloomington United States School of Information Studies University of Wisconsin Milwaukee United States Department of Intelligent Systems and Engineering School of Informatics Computing and Engineering Indiana University Bloomington United States

Openness and interdisciplinarity in research and data are among the challenges that are frequently discussed in the context of changing scientific and scholarly practices. Gradually, the visions of open and widely shared data are being reconciled with complex realities that stem from the disciplinary differences in data cultures. In this paper we discuss interdisciplinarity through data as a way to create research environments that are more flexible and, as a result, more amenable to change. We report our findings from facilitating and evaluating a data-oriented early-career fellowship program that was administered as part of the Research Data Alliance (RDA), a global organization that aims to enable open sharing and re-use of data. We identify ways to foster interdisciplinary data cultures among the future researchers and professionals and propose recommendations for future programs. While the short-term early career programs cannot address the systemic factors that impact openness and interdisciplinarity, such as the systems of reward and recognition or the funding structures, they can introduce mechanisms that support diversity, learning, and leadership and, ultimately, contribute to a culture change. © 2021 The Author(s).

关键词： Data culture Data stewardship Early career training Open science

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Gauging Public Interest in the Digital Sovereignty Domain: A Social Sensor Analysis

Gauging Public Interest in the Digital Sovereignty Domain: A...

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International Symposium on Technology and Society (ISTAS)

作者： Kuheli Sai David Tipper Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798331540708

ISBN: (纸本)9798331540715

Societal dependence on the digital domain has resulted in governments increasingly concerned with digital sovereignty. The major digital sovereignty issues revolve around how to ensure the security, privacy and availability of government and citizens digital information in any form, from sensing/creation of information to communication through networks to processing on computing platforms to storage in the cloud. While there is clearly interest in the digital sovereignty topic by governments, academics, network operators, equipment vendors and policy organizations, it is unclear if there is corresponding citizen engagement. This paper seeks to gauge public interest in the digital sovereignty using social sensor data from platforms such as X and Google. The findings suggest that while governments and industry stakeholders may prioritize digital sovereignty, the general public exhibits limited interest and online engagements in the topic.

关键词： Industries Cloud computing Time-frequency analysis Privacy Terminology Social networking (online) Market research Sensors Stakeholders Security

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Sustainability and Power Outage-aware Placement of Edge computing in NextG RANs

Sustainability and Power Outage-aware Placement of Edge Comp...

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International Symposium on Technology and Society (ISTAS)

作者： Kuheli Sai David Tipper Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798331540708

ISBN: (纸本)9798331540715

The advent of Next Generation (NextG) Radio Access Networks (RANs) necessitates the strategic geographical placement of edge computing infrastructure to meet the demands for low latency, high bandwidth, reliable network connectivity and support for Artificial Intelligence (AI) based applications. This paper investigates the use of solar powered micro edge data centers in NextG RANs, which will reduce the carbon emissions of network operators and can ensure continuous operation during power outages. We adopt a nanogrid power architecture that integrates solar power generation and energy storage co-location with small edge data centers. A novel methodology is proposed that uses data driven techniques to identify the power outage prone sections of a city and the solar generation potential of rooftops in that part of the city. Given a set of rooftops that meet or exceed the edge data center energy requirements, unsupervised machine learning is used to determine the geographic locations to place micro edge data centers. We illustrate our methodology using New Your City as a case study. This work provides insights for urban planners, network operators, and policymakers aiming to develop sustainable and resilient NextG RANs for societal good.

关键词： Cellular networks Data centers Renewable energy sources Urban areas Power system reliability Solar power generation Sustainable development Socioeconomics Radio access networks Edge computing

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Determining Digital Divide in Local-Level ICT Usage for 311 Civic Applications

Determining Digital Divide in Local-Level ICT Usage for 311 ...

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IEEE Global Humanitarian Technology Conference (GHTC)

作者： Kuheli Sai David Tipper Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350377347

ISBN: (纸本)9798350377354

Information and Communication Technology (ICT) have transformed society and enabled numerous societal applications. However, there is limited understanding whether ICT usage is equitable or not across different neighborhoods (in which public are often geographically clustered based on their socio-economic compositions) within a city. One such application is 311 service, which allows public to report neighborhood specific concerns to their local authorities (e.g., city departments) using various ICT methods. An access to ICTs, willingness, and basic digital literacy skills are required to use these services. Here, we examine if digital divide exist in ICT usage for requesting services specific to 311 applications across diverse neighborhoods in Pittsburgh by analyzing publicly available open 311 civic data. Our data-driven empirical findings reveal the following: First, different neighborhoods with distinct socio-economic and demographic profiles have varying categories of 311 requests. Second, the type of ICT usage and patterns for 311 requests differ across these neighborhoods. Third, higher home prices and presence of a higher proportion of female residents correlate positively with submitted 311 requests. Fourth, while empirical study indicates the possibility of having digital divide in 311 application specific ICT usage, corresponding statistical hypothesis testing based on t-test fails to reject the null hypothesis and further demands a thorough analysis with more number of neighborhoods. This empirical study and corresponding insights can help city policymakers construct data-driven decisions, guiding policy formulation and local-level resource allocation. The methodology provides a blueprint for similar analyses in other cities.

关键词： Correlation Local government Urban areas Information and communication technology Digital divide Resource management Digital intelligence Socioeconomics Portfolios Testing

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Neural Evolutionary Architecture Search for Compact Printed Analog Neuromorphic Circuits

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IEEE Transactions on Computer-Aided Design of Integrated Circuits and systems 2024年

作者： Zhao, Haibin Pal, Priyanjana Hefenbrock, Michael Wang, Yuhong Beigl, Michael Tahoori, Mehdi B. Chair for Pervasive Computing Systems Institute of Telematics Germany Chair of Dependable Nano Computing Institute of Computer Engineering Department of Informatics Germany RevoAI GmbH Germany

Printed electronics (PE) is an additive fabrication technology for manufacturing electronic circuits which not only allows for a highly flexible printing of arbitrary circuit patterns, but also produce soft, non-Toxic, and degradable electronics at an extremely low cost. These properties are unmatched by silicon based electronics, making PE an enabler of new application domains, e.g., fast moving consumer goods, wearables, and disposable healthcare devices. A particularly promising class of circuits in this technology is the printed analog neuromorphic circuits, offering efficient and highly tailored computational functionalities. In this work, we leverage the highly flexible fabrication process of PE to address the bottleneck of PE, i.e., the large feature sizes and low device counts. This issue is crucial, as it impairs the integration of printed circuits into target applications with limited footprint, such as smart band aids. We also propose an evolutionary algorithm (EA) to improve the circuit compactness through circuit architecture optimization. As baseline, we compare the proposed EA method with a state-of-The-Art pruning method and a modified area-Aware pruning method. All of them are able to optimize circuit architecture alongside the component values of printed neuromorphic circuits. Experimental simulation reveals that the proposed EA approach can effectively achieve compact circuits and outperform the pruning method by 3.1× lower area with no loss of accuracy. As a byproduct, the power is reduced by 3.0×, paving the way to energy-harvested printed systems. © 1982-2012 IEEE.

关键词： Flexible electronics

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Bridging NextG Network Connectivity Induced Digital Divide with Sustainable ICT

Bridging NextG Network Connectivity Induced Digital Divide w...

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International Conference on Collaborative computing: Networking, Applications and Worksharing (CollaborateCom)

作者： Kuheli Sai David Tipper Department of Informatics and Networked Systems School of Computing and Information University of Pittsburgh Pittsburgh PA USA

ISBN: (数字)9798350386707

ISBN: (纸本)9798350386714

Next-Generation (NextG) wireless networks could widen the digital divide if not deployed equitably. Without intentional planning and provisioning of services from the network service providers and government, underserved and marginalized communities could be left behind, exacerbating pre-existing societal inequalities further in the digital domain. To address this societal problem, we propose a data-driven solution that prioritizes the installation of micro edge data centers, a key component of NextG networks, in socio-economically distressed neighborhoods within an urban area. Leveraging publicly available open 311 data as an indirect measurement approach, we demonstrate that public from these geographic localities possess the digital literacy skills needed to benefit from faster internet connectivity. Further, to improve the sustainability of micro edge data centers we propose powering them with solar powered nano-electrical grids. This approach is cost-effective for providers, as cost of buildings and housings in geographic regions having lower socio-economic conditions are smaller compared with other localities. In addition, we discuss the cost of deployment for these sustainable information and communication technology (ICTs) too. We demonstrate the feasibility of our proposed solution approach through a case study based on city of Pittsburgh.

关键词： Bridges Data centers Costs Wireless networks Urban areas Buildings Digital divide Socioeconomics Next generation networking Telecommunication services

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Empowering the adoption of blockchain-based cryptocurrency as a payment method: A user-friendly educational approach

IET Blockchain

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IET Blockchain 2025年第1期5卷

作者： Alrehaili, Ahmed White, Martin Beloff, Natalia University of Sussex School of Engineering and Informatics Informatics Brighton United Kingdom Islamic University of Madinah College of Computing and Information Systems Computer Science Department Medina Saudi Arabia

Organizations are adopting technological innovations to transform payment systems due to challenges with traditional methods, such as slow speed and high fees. These challenges have prompted a shift towards blockchain-based cryptocurrencies. However, cryptocurrency adoption for payments remains limited, especially in Saudi Arabia. This study adapts the technology acceptance model to explore cryptocurrency adoption through the blockchain-based cryptocurrency as a payment method in Saudi Arabia (BCAP-SA) model. Factors within the model are assessed using an experimental vignette-task methodology and surveys. A key component is an educational package, offering comprehensive materials to explain blockchain technology. The findings confirm the reliability of surveys. Most model factors are statistically significant in influencing users’ intention to use cryptocurrency. The study finds that perceived ease of use, perceived usefulness, and perceived trust significantly impact participants’ intentions. Additionally, low transaction fees and age are the most influential factors on the technology acceptance model's core constructs. Statistical analysis indicates that decentralization and anonymity were insignificant and thus excluded from the revised BCAP-SA model. These findings highlight the potential to enhance cryptocurrency adoption in Saudi Arabia. The study's insights can guide strategies to promote wider cryptocurrency usage in the region. © 2025 The Author(s). IET Blockchain published by John Wiley & Sons Ltd on behalf of The Institution of Engineering and Technology.

关键词： blockchain platforms blockchains cryptocurrencies models and analysis

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