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16th International Conference on Society and information Technologies, ICSIT 2025

作者： Ferdousi, Bilquis School of Information Security and Applied Computing College of Engineering and Technology Eastern Michigan University YpsilantiMI United States

ISBN: (纸本)9781950492831

Academic and non-academic research institutes are the two key global sectors that are accomplishing the research agenda. This paper focuses on the implications of using AI tools in academic research. Academic institutions conduct rigorous research to find the impact of different issues on society. Artificial Intelligence (AI) impacts society, organizations, and governance. AI has significantly transformed various domains, including higher education and academic research. Because of its effect on research and education in multidisciplinary fields, there is increasingly significant interest in finding AI's effect on research. In this context, there is a growing concern to find the impact of AI on research in academic institutions. This article aims to engage in a rigorous dialogue on AI’s role in academic research. Conducting a systematic literature review of AI's impact on academic research, this study aims to investigate 1) how AI tools are affecting academic research and 2) ethical dilemmas in using AI in academic research. By exploring the advantages and challenges of using innovative applications of AI tools in academic research, this study will give stakeholders, especially researchers, educators, and students, a comprehensive view of how AI could shape educational and interdisciplinary research practices in the future. © 2025 International Conference on Society and information Technologies. All Rights Reserved.

关键词： Academic Research Artificial Intelligence Ethical Concern

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Detecting Phishing Websites Based on Machine Learning Techniques 1st

Detecting Phishing Websites Based on Machine Learning Techni...

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1st International Conference on Advances in computing Research, ACR’23

作者： Veach, Alexander Abualkibash, Munther School of Information Security and Applied Computing Eastern Michigan University YpsilantiMI48197 United States

ISBN: (纸本)9783031337420

Phishing is one of the most common threats to cybersecurity in the modern world. It is to this regard many look to emergent technologies such as artificial intelligence and machine learning as preventative measures. By using such preventative measures many hope to reduce the potential for data breaches and other harmful occurrences linked to phishing. To that extent this paper investigates creating a Manifest V3 extension that can work alongside a trained machine learning model to give users more information about which sites are potentially hazardous. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Machine learning

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A Survey on Drone Cybersecurity and the Application of Machine Learning on Threat Emergence 1st

A Survey on Drone Cybersecurity and the Application of Machi...

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1st International Conference on Advances in computing Research, ACR’23

作者： Walatkiewicz, Jonathan Darwish, Omar Information Security and Applied Computing Eastern Michigan University 900 Oakwood St YpsilantiMI48197 United States

ISBN: (纸本)9783031337420

Recreational and commercial drones are becoming more common in the United States and globally. As of 2020, recreational drones registered with the Federal Aviation Association (FAA) peaked at 1,195,722 (Federal Aviation Administration, 2021). Threat vectors must be researched, and security concerns mitigated in parallel as drone usage expands. The application of machine learning (ML) is being used to assist in identifying, classifying, and mitigating vulnerabilities and attacks in real time. The emerging threat vector of covert timing channel (CTC) using drones has not been investigated for its feasibility or risk level. This paper summarizes drone vulnerability surveys and seeks to introduce the concept of using ML to assess drone CTC. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Cybersecurity

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A Classifier to Detect Number of Machines Performing DoS Attack Against Arduino Oplà Device in IoT Environment 5

A Classifier to Detect Number of Machines Performing DoS Att...

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5th International Conference on Advanced Communication Technologies and Networking, CommNet 2022

作者： Al-Maani, Mahmoud Frisbie, Gayle Shippy, Frederick Tiseo, Mario Darwish, Omar Abualkibash, Munther Eastern Michigan University Information Security and Applied Computing YpsilantiMI United States

ISBN: (数字)9781665450546

ISBN: (纸本)9781665450546

The use of Internet of Things (IoT) devices is on the rise as they enter our homes and businesses, from smart appliances to increased use in agriculture, health care, and manufacturing. These IoT devices rely on their network connection to send and act on data they are acquiring from the environment around them. Because of this dependency, IoT devices are especially susceptible to denial of service (DoS) and distributed denial of service (DDoS) network attacks. In the following research, a simulated DoS attack will be performed the new Arduino Oplà device. The results are then fed into several machine learning algorithms to review predictability for detection based on packet loss and packet transit time. Results indicated the Decision Tree J48 algorithm with 5-fold cross-validation resulting in an f1-score of 0.848 best predicts the IoT network attack. The results show the methods in which these attacks can be performed, how susceptible the device is to DoS attacks, and the adverse effects that the attacks have on response time and packet loss. © 2022 IEEE.

关键词： Internet of things

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information-Centric IoT-Based Smart Farming with Dynamic Data Optimization

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Computers, Materials & Continua 2023年第2期74卷 3865-3880页

作者： Souvik Pal Hannah VijayKumar D.Akila N.Z.Jhanjhi Omar A.Darwish Fathi Amsaad Department of Computer Science and Engineering Sister Nivedita UniversityKolkataIndia Post Doctoral Researcher Sambalpur UniversitySambalpurIndia Department of Computer Science Anna Adarsh College for WomenChennaiIndia Department of Information Technology Vels Institute of ScienceTechnology and Advanced StudiesChennaiIndia School of Computer Science and Engineering SCETaylor’s UniversitySubang Jaya47500SelangorMalaysia FITTaylor’s UniversitySubang Jaya47500SelangorMalaysia Department of Information Security and Applied Computing Eastern Michigan UniversityUSA School of Information Security and Applied Computing Eastern Michigan UniversityUSA

Smart farming has become a strategic approach of sustainable agriculture management and monitoring with the infrastructure to exploit modern technologies,including big data,the cloud,and the Internet of Things(IoT).Many researchers try to integrate IoT-based smart farming on cloud platforms *** define various frameworks on smart farming and monitoring system and still lacks to define effective data management *** IoT-cloud systems involve massive structured and unstructured data,data optimization comes into the ***,this research designs an information-Centric IoT-based Smart Farming with Dynamic Data Optimization(ICISF-DDO),which enhances the performance of the smart farming infrastructure with minimal energy consumption and improved ***,a conceptual framework of the proposed scheme and statistical design model has beenwell *** information storage and management with DDO has been expanded individually to show the effective use of membership parameters in data *** simulation outcomes state that the proposed ICISF-DDO can surpass existing smart farming systems with a data optimization ratio of 97.71%,reliability ratio of 98.63%,a coverage ratio of 99.67%,least sensor error rate of 8.96%,and efficient energy consumption ratio of 4.84%.

关键词： Smart farming data optimization information management internet of things cloud

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Deep Reinforcement Learning-Based Framework for Enhancing Cybersecurity

International Journal of Interactive Mobile Technologies

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International Journal of Interactive Mobile Technologies 2025年第3期19卷 170-190页

作者： Al-Nawashi, Malek M. Al-Hazaimeh, Obaida M. Tahat, Nedal M. Gharaibeh, Nasr Abu-Ain, Waleed A. Abu-Ain, Tarik Al-Huson University College Al-Balqa Applied University Irbid Jordan Faculty of Information Technology Department of Information Security and Cybersecurity Philadelphia University Amman Jordan Faculty of Science The Hashemite University Zarqa Jordan College of Science and Computer Engineering Taibah University Yanbu Saudi Arabia College of Computing and Informatics Saudi Electronic University Riyadh Saudi Arabia

The detection of cyberattacks has been increasingly emphasized in recent years, focusing on both infrastructure and people. Conventional security measures such as intrusion detection, firewalls, and encryption are insufficient in protecting cyber systems against growing and changing threats. In order to address this problem, scholars have explored reinforcement learning (i.e., RL) as a potential solution for intricate cybersecurity decision-making difficulties. Nevertheless, the use of RL faces several obstacles, including dynamic attack scenarios, insufficient training data, and the challenge of replicating real-world complexities. This study presents a novel framework that uses deep reinforcement learning (i.e., DRL) to simulate harmful cyberattacks and improve cybersecurity. This study presents an agent-based framework that is capable of ongoing learning and adaptation in a dynamic network security environment. The agent determines the optimal course of action by considering the current state of the network and the rewards it receives for its decisions. The CIC-IDS-2018 database, constructed using Python 3.7 programming, was used. The conducted studies yielded outstanding results, with a detection accuracy of 98.82% achieved for the CIC-IDS-2018 database in cyber-attack classification. © 2025 by the authors of this article. Published under CC-BY.

关键词： Cybersecurity

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Applying Digital Technology in Writing Intensive Class in Cyber security and information Technology Programs Identifying Technical and Psychological Issues 26

Applying Digital Technology in Writing Intensive Class in Cy...

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26th World Multi-Conference on Systemics, Cybernetics and Informatics, WMSCI 2022

作者： Ferdousi, Bilquis School of Information Security and Applied Computing Eastern Michigan University YpsilantiMI48197 United States

ISBN: (纸本)9781950492664

This paper studies how to improve students’ technical and academic writing skills by applying digital technology, and identifying the effect of technical and psychological factors. The specific purposes of the study are to determine: 1) How digital technology can improve students’ technical and academic writing performance, 2) What tools and apps are available that can enhance students’ writing skills, 3) What technical and psychological issues need to be addressed to incorporate digital technology into the writing-intensive classes. Given the persistent limited writing performance with low motivation of learning academic and technical writing among the undergraduate students in the computer technology programs such as Cyber security and information Technology, this study is significant. © 2022 *** rights reserved.

关键词： Motivation

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A Communication-efficient Conjunctive Query Scheme under Local Differential Privacy

A Communication-efficient Conjunctive Query Scheme under Loc...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhang, Ellen Z. Guan, Yunguo Lu, Rongxing Zhang, Harry University of New Brunswick Faculty of Computer Science FrederictonE3B5A3 Canada Eastern Michigan University School of Information Security and Applied Computing YpsilantiMI48197 United States

ISBN: (纸本)9798350351255

Crowdsourcing has become a widely used method for data collection and analysis, yet its privacy remains a challenge. In this paper, we present a new efficient and privacy-preserving conjunctive query scheme for crowdsourcing scenarios. The scheme employs the Local Differential Privacy (LDP) technique to ensure both query privacy and high communication efficiency. Specifically, when an aggregator launches a conjunctive query to a set of crowdsourcing users, the query condition will not be leaked. To respond the query, each user just needs to return one bit back to the aggregator. By integrating prefix encoding technique, our proposed scheme can also efficiently support conjunctive queries with one range query condition. Detailed security analysis shows our proposed scheme can achieve desirable security requirements. In addition, performance evaluations also indicate its efficiency. Furthermore, extensive experiments demonstrate our proposed scheme can achieve high accuracy while ensuring ϵ-LDP. © 2024 IEEE.

关键词： Differential privacy

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Hierarchical certification of nonclassical network correlations

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Physical Review A 2024年第2期110卷 022617页

作者： Ming-Xing Luo Xue Yang Alejandro Pozas-Kerstjens Information Security and National Computing Grid Laboratory CAS Center for Excellence in Quantum Information and Quantum Physics Hefei 230026 China Group of Applied Physics 8200 Schaffhausen Switzerland (

With the increased availability of quantum technological devices, it becomes more important to have tools to guarantee their correct nonclassical behavior. This is especially important for quantum networks, which constitute the platforms where multipartite cryptographic protocols will be implemented, and where guarantees of nonclassicality translate into security proofs. We derive linear and nonlinear Bell-like inequalities for networks, whose violation certifies the absence of a minimum number of classical sources in them. We do so, first, without assuming that nature is ultimately governed by quantum mechanics, providing a hierarchy interpolating between network nonlocality and full network nonlocality. Second we insert this assumption, which leads to results more amenable to certification in experiments.

关键词： Nonlocality Quantum communication, protocols & technology Quantum correlations in quantum information Quantum correlations, foundations & formalism Quantum information processing Quantum information theory Quantum networks

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Learning EEG synchronization patterns for epileptic seizure prediction using bag-of-wave features

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Journal of Ambient Intelligence and Humanized computing 2023年第11期14卷 15557-15572页

作者： Cui, Song Duan, Lijuan Qiao, Yuanhua Xiao, Ying Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing Key Laboratory of Trusted Computing National Engineering Laboratory for Critical Technologies of Information Security Classified Protection Beijing100124 China College of Applied Sciences Beijing University of Technology Beijing100124 China

Epileptic seizure prediction has the potential to promote epilepsy care and treatment. However, the seizure prediction accuracy does not satisfy the application requirements. In this paper, a novel framework for seizure prediction is proposed by learning synchronization patterns. For better representation, bag-of-wave (BoWav) feature extraction is proposed for modeling synchronization pattern of electroencephalogram (EEG) signal. An interictal codebook and preictal codebook, representing the local segments, are constructed by a clustering algorithm. Within a period of EEG signal on all electrodes, local segments are projected onto the learned codebooks. The proposed feature expresses the synchronization pattern of EEG signal with the histogram feature. Moreover, extreme learning machine (ELM) is used to classify the sequence of features. Experiments are performed on the Kaggle seizure prediction challenge dataset and the CHB-MIT dataset. The experiment on the CHB-MIT achieves a sensitivity of 88.24% and a false prediction rate per hour of 0.25. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Feature extraction

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