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30th Americas Conference on information systems, AMCIS 2024

作者： Im, Ghiyoung P. Cho, Sunyoung Chung, Claris Computer Information Systems University of Louisville United States Information Systems University of Texas Rio Grande Valley United States Accounting & Information Systems University of Canterbury New Zealand

ISBN: (纸本)9798331307066

Business analytics (BA) has been highlighted over the past decade as a critical tool for improving organizations' decision-making. Despite a heavy research focus on the relationship between BA and firm performance, few studies have addressed dynamic managerial capabilities and how ambidextrous BA use contributes to better decision-making in organizations. To address these research gaps, the study proposes a theoretical model that investigates the antecedents and consequences of ambidextrous BA use. Dynamic managerial capabilities, as the antecedents, include paradoxical managerial cognition (integrative complexity and holistic thinking) and data literacy. © 2024 30th Americas Conference on information systems, AMCIS 2024. All rights reserved.

关键词： Ambidexterity Business Analytics Data Literacy Decision-Making effectiveness Managerial Capabilities

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An intrusion attack classification using bio-inspired optimization technique and ensemble learning model for edge computing environments

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Multimedia Tools and Applications 2024年 1-28页

作者： Rahamathulla, Mohemmed Yousuf Ramaiah, Mangayarkarasi School of Computer Science Engineering and Information Systems Vellore Institute of Technology Tamilnadu Vellore India

The phenomenal rise in network traffic across various sectors, driven by advancements in network communication, has led to an explosion of connected devices. While internet-based service providers have enhanced smart environments, these environments remain highly vulnerable to security risks. Due to their resource- constrained nature, Internet of Things (IoT) systems rely on edge servers for processing. Cutting-edge machine learning algorithms have significantly improved the security of edge computing systems by enabling early intrusion detection. However, the dynamic nature of our society demands more sophisticated Network Intrusion Detection systems (NIDS) that can balance the challenge of managing the explosive growth of network traffic data with effectively responding to attacks. This paper proposes a novel NIDS that can train and learn from massive amounts of data in a centralized environment. To identify the best subset of features for improved data representation and classification, a bio-inspired algorithm, the Improved Firefly Optimization Algorithm (IFOA), is employed. Furthermore, a hyper-tuned Extremely Randomized Trees (ERT) attack detection model is developed to accurately identify malicious behavior while minimizing false positives. The experimental results demonstrate that the proposed approach achieves superior accuracy and execution speed compared to alternative solutions and its baseline versions. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Network intrusion

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A Reputation-Based AODV Protocol for Blackhole and Malfunction Nodes Detection and Avoidance

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computers, Materials & Continua 2024年第8期80卷 1867-1888页

作者： Qussai M.Yaseen Monther Aldwairi Ahmad Manasrah Artificial Intelligence Research Center(AIRC) College of Engineering and Information TechnologyAjman UniversityAjman346United Arab Emirates Department of Computer Information Systems Faculty of Computer and Information TechnologyJordan University of Science and TechnologyIrbid22110Jordan College of Technological Innovation Zayed UniversityAbu Dhabi144534United Arab Emirates Faculty of Information Technology and Computer Science Yarmouk UniversityIrbid21163Jordan Computer Information Systems Higher Colleges of TechnologySharjah341214United Arab Emirates

Enhancing the security of Wireless Sensor Networks(WSNs)improves the usability of their ***,finding solutions to various attacks,such as the blackhole attack,is crucial for the success of WSN *** paper proposes an enhanced version of the AODV(Ad Hoc On-Demand Distance Vector)protocol capable of detecting blackholes and malfunctioning benign nodes in WSNs,thereby avoiding them when delivering *** proposed version employs a network-based reputation system to select the best and most secure path to a *** achieve this goal,the proposed version utilizes the Watchdogs/Pathrater mechanisms in AODV to gather and broadcast reputations to all network nodes to build the network-based reputation *** minimize the network overhead of the proposed approach,the paper uses reputation aggregator nodes only for forwarding reputation ***,to reduce the overhead of updating reputation tables,the paper proposes three mechanisms,which are the prompt broadcast,the regular broadcast,and the light broadcast *** proposed enhanced version has been designed to perform effectively in dynamic environments such as mobile WSNs where nodes,including blackholes,move continuously,which is considered a challenge for other *** the proposed enhanced protocol,a node evaluates the security of different routes to a destination and can select the most secure routing *** paper provides an algorithm that explains the proposed protocol in detail and demonstrates a case study that shows the operations of calculating and updating reputation values when nodes move across different ***,the paper discusses the proposed approach’s overhead analysis to prove the proposed enhancement’s correctness and applicability.

关键词： AODV blackhole malfunction nodes pathrater reputation system wireless sensor networks(WSNs) watchdogs

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Teaching How to Select an Optimal Agile, Plan-Driven, or Hybrid Software Development Approach: Lessons from Enterprise Software Development Leaders

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Journal of information systems Education 2023年第2期34卷 148-178页

作者： Spurrier, Gary Topi, Heikki Department of Computer Information Systems Bentley University WalthamMA02452 United States

Over 20 years after introducing and popularizing agile software development methods, those methods have proven effective in delivering projects that meet agile assumptions. Those assumptions require that projects be small and simple in scope and utilize small, colocated teams. Given this success, many agile advocates argue that agile should replace plan-driven methods in most or all project contexts, including those projects that deviate significantly from agile assumptions. However, today’s reality is that a diversity of agile, plan-driven, and hybrid approaches continue to be widely used, with many individual organizations using multiple approaches across different projects. Furthermore, while agile advocates argue that the primary barrier to agile adoption is the inertia of traditional organizational cultures, there are, in fact, many rational motivations for utilizing plan-driven and hybrid methods based on individual project characteristics. For information systems students, this creates confusion in two ways: 1) understanding that there is no single best way to develop software in all circumstances but, rather, teams should choose an optimal project approach based on project characteristics, and 2) unpacking and analyzing the wide range of project characteristics – including multiple dimensions in functional requirements, non-functional requirements (NFRs), and team characteristics – that impact that choice. This paper addresses both sources of confusion by utilizing case studies from 22 interviews of enterprise software development leaders. The paper analyzes each case utilizing a "home grounds" model that graphically portrays key project characteristics and their impact on the optimal choice of software development project approach. © 2023, Journal of information systems Education. All Rights Reserved.

关键词： Software design

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Acoustic modelling using deep learning for Quran recitation assistance

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International Journal of Speech Technology 2023年第1期26卷 113-121页

作者： Ghori, Ammar Farid Waheed, Aisha Waqas, Maria Mehmood, Aqsa Ali, Syed Abbas Department of Computer and Information Systems Engineering NED University of Engineering and Technology Karachi Pakistan

Recent advancements in computer science and engineering have significantly boosted interest in and success rate of speech recognition systems. Sophisticated speech recognition systems are being developed for a variety of languages having wide-spread applications. Arabic being one of the most widely used languages in the world, augmented by religious importance, is a potential candidate in this domain, however rich vocabulary and multiple dialects open up new challenges for development of Arabic speech recognition processes. This paper focuses on development of an Arabic isolated speech recognition system for the vocabulary of Holy Quran using Mel frequency cepstral coefficients and deep neural networks. The proposed system is capable of identifying individual words from a recited verse with reasonable accuracy. It targets the 362 unique words of the first and the last 19 chapters of the Holy Quran and uses 14 hours of audio data to demonstrate the working prototype of the system. A user-friendly, web-based application has also been developed for transcription of recitations. The software is specified to make an effort in spreading the right way of recitation of the Holy Quran. The presented architecture can be extended to a complete automatic speech recognition system to help people practice recitations, mapping speech to transcription and then matching the transcribed utterance with standard templates of correct recitation to identify reciter’s short comings. © 2022, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.

关键词： Deep neural networks

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Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets

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computer systems Science & Engineering 2023年第8期46卷 2555-2570页

作者： Aisha M.Mashraqi Hanan T.Halawani College of Computer Science and Information Systems Najran UniversityNajran61441Saudi Arabia

Sentiment Analysis(SA)is one of the Machine Learning(ML)techniques that has been investigated by several researchers in recent years,especially due to the evolution of novel data collection methods focused on social *** literature,it has been reported that SA data is created for English language in excess of any other *** is challenging to perform SA for Arabic Twitter data owing to informal nature and rich morphology of Arabic *** earlier study conducted upon SA for Arabic Twitter focused mostly on automatic extraction of the features from the *** word embedding has been employed in literature,since it is less labor-intensive than automatic feature *** ignoring the context of sentiment,most of the word-embedding models follow syntactic data of *** current study presents a new Dragonfly Optimization with Deep Learning Enabled Sentiment Analysis for Arabic Tweets(DFODLSAAT)*** aim of the presented DFODL-SAAT model is to distinguish the sentiments from opinions that are tweeted in Arabic *** first,data cleaning and pre-processing steps are performed to convert the input tweets into a useful *** addition,TF-IDF model is exploited as a feature extractor to generate the feature ***,Attention-based Bidirectional Long Short Term Memory(ABLSTM)technique is applied for identification and classification of *** last,the hyperparameters of ABLSTM model are optimized using DFO *** performance of the proposed DFODL-SAAT model was validated using the benchmark dataset and the outcomes were investigated under different *** experimental outcomes highlight the superiority of DFODL-SAAT model over recent approaches.

关键词： Natural language processing sentiment analysis arabic tweets deep learning metaheuristics lexicon approach

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Forework: A Real-Time, Distributed Digital Forensic Analysis Framework 32

Forework: A Real-Time, Distributed Digital Forensic Analysis...

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32nd National Conference with International Participation, TELECOM 2024

作者： Penmetsa, Sahil Varma University of Central Missouri Computer Information Systems and Information Technology United States

ISBN: (纸本)9798350355000

The increasing volume of data in digital forensic investigations has outpaced the capabilities of existing forensic systems. Traditional systems such as The Sleuth Kit Hadoop, OCFA, and Hansken, though capable of handling large-scale data, fail to offer real-Time analysis, which is crucial for prioritizing critical evidence in investigations. This paper addresses this gap by proposing a modern, real-Time analysis approach and introducing Forework, a proof-of-concept implementation. Forework aims to deliver real-Time, prioritized analysis of forensic artifacts, providing an interactive and scalable solution. It leverages parallel and distributed computing to manage large datasets efficiently, ensuring investigators can quickly focus on the most relevant evidence. This system represents a significant step forward in digital forensics, aligning with contemporary needs for swift and effective data analysis. © 2024 IEEE.

关键词： computer forensics

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Securing Cloud-Encrypted Data:Detecting Ransomware-as-a-Service(RaaS)Attacks through Deep Learning Ensemble

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computers, Materials & Continua 2024年第4期79卷 857-873页

作者： Amardeep Singh Hamad Ali Abosaq Saad Arif Zohaib Mushtaq Muhammad Irfan Ghulam Abbas Arshad Ali Alanoud Al Mazroa School of Computer Information Sciences University of CumberlandsWilliamsburgKentucky40769USA Computer Science Department College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia Department of Mechanical Engineering College of EngineeringKing Faisal UniversityAl-Ahsa31982Saudi Arabia Department of Electrical Electronics and Computer SystemsCollege of Engineering and TechnologyUniversity of SargodhaSargodha40100Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Electrical Engineering The University of LahoreLahore54000Pakistan Faculty of Computer and Information Systems Islamic University of MadinahMadinah42351Saudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia

Data security assurance is crucial due to the increasing prevalence of cloud computing and its widespread use across different industries,especially in light of the growing number of cybersecurity threats.A major and everpresent threat is Ransomware-as-a-Service(RaaS)assaults,which enable even individuals with minimal technical knowledge to conduct ransomware *** study provides a new approach for RaaS attack detection which uses an ensemble of deep learning *** this purpose,the network intrusion detection dataset“UNSWNB15”from the Intelligent Security Group of the University of New South Wales,Australia is *** the initial phase,the rectified linear unit-,scaled exponential linear unit-,and exponential linear unit-based three separate Multi-Layer Perceptron(MLP)models are ***,using the combined predictive power of these three MLPs,the RansoDetect Fusion ensemble model is introduced in the suggested *** proposed ensemble technique outperforms previous studieswith impressive performance metrics results,including 98.79%accuracy and recall,98.85%precision,and 98.80%*** empirical results of this study validate the ensemble model’s ability to improve cybersecurity defenses by showing that it outperforms individual *** expanding the field of cybersecurity strategy,this research highlights the significance of combined deep learning models in strengthening intrusion detection systems against sophisticated cyber threats.

关键词： Cloud encryption RaaS ensemble threat detection deep learning cybersecurity

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Comparative Analysis of Machine Learning Models for Real-Time Network Threat Detection 2

Comparative Analysis of Machine Learning Models for Real-Tim...

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2nd International Conference on Artificial Intelligence and Automation Control, AIAC 2024

作者： Penmetsa, Sahil Varma University of Central Missouri Computer Information Systems & Information Technology United States

ISBN: (纸本)9798331529147

This research explores the efficacy of various machine learning models in detecting network threats, focusing on their performance across known, similar, and new attack datasets. I evaluated models including Random Forest, XGBoost, Logistic Regression, and Gaussian Naive Bayes, utilizing different preprocessing pipelines such as scaling, PCA, and correlation-based feature selection. Our findings reveal that while most models achieve near-perfect performance on known and similar threats, their ability to generalize to new threats is limited, with Gaussian Naive Bayes showing the highest F1 score on new attacks. Furthermore, Logistic Regression and Gaussian Naive Bayes models demonstrate the fastest detection times. The study highlights the importance of preprocessing techniques and model selection in enhancing threat detection systems. Deployment strategies for these models are discussed, emphasizing potential online and offline applications. Despite achieving significant insights, the study acknowledges the limitations posed by dataset size and computational resources, suggesting areas for future improvement. © 2024 IEEE.

关键词： Adversarial machine learning

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Intelligent integration of AI and IoT for advancing ecological health, medical services, and community prosperity

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Alexandria Engineering Journal 2025年 127卷 522-540页

作者： Alzahrani, Abdulrahman Kostkova, Patty Alshammari, Hamoud Habibullah, Safa Alzahrani, Ahmed Department of Information System and Technology College of Computer Science and Engineering University of Jeddah Saudi Arabia University College London United Kingdom Department of Information systems College of Computer and Information Sciences Jouf University Saudi Arabia

Artificial Intelligence (AI) and the Internet of Things (IoT) are developing so fast that they can bring revolutionary changes in ecological sustainability, public health, and community welfare. In contrast, the present waste management system has a set of inefficiencies due to some challenges, such as poor waste stream segregation, limited real-time data analysis, and negligible integration of recent technology. These challenges lead to environmental degradation, public health hazards, and inefficient usage of resources. This research targets these challenges by designing an IWM framework like AI-IoT for smart waste management. The system employs AI models powered by IoT sensors for efficient waste collection, classification, and optimization of recycling schedules. CNN (convolutional neural networks) with transfer learning enabled by Res-Net provides high-accuracy image recognition, which can be used for waste classification. Bidirectional Encoder Representations from Transformers (BERT) allow multilingual users to interact and communicate properly in any linguistic environment. Data collected from IoT-enabled smart bins is transmitted in real-time to a central control system for dynamic decision-making and follow-up analysis. A pilot exercise to verify the system's effectiveness was implemented in metropolitan settings to show the transformation: landfill dependency was decreased by 30 %, recycling efficiency was greatly increased to 90 %, and thus the cost of waste management was optimized. At the same time, environmental health inequity, causing pathogen-related threats, was reduced by 35 %. The model has an accuracy of 96.8 %. The features of the proposed framework not only provide solutions to the existing inefficiencies but also enhance scalability, cost-effectiveness, and global environmental standardization. This dawns the futuristic growth of AI- and IoT-enabled waste management systems, which hinge on sustainability, public health, and resource efficienc

关键词： Artificial Intelligence Internet of Things BERT Medical Services Res-Net Waste Management

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