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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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Detecting AI-Generated Text in Educational Content: Leveraging Machine Learning and Explainable AI for Academic Integrity

arXiv

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arXiv 2025年

作者： Najjar, Ayat A. Ashqar, Huthaifa I. Darwish, Omar A. Hammad, Eman Faculty of Modern Science Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Civil Engineering Department Arab American University 13 Zababdeh P.O Box 240 Jenin Palestine Artificial Intelligence Program Fu Foundation School of Engineering and Applied Science Columbia University 500 W 120th St New YorkNY10027 United States Information Security and Applied Computing Eastern Michigan University 900 Oakwood St YpsilantiMI48197 United States iSTAR Lab Engineering Technology & Industrial Distribution Texas A&M University 400 Bizzell St College StationTX77840 United States

This study seeks to enhance academic integrity by providing tools to detect AI-generated content in student work using advanced technologies. The findings promote transparency and accountability, helping educators maintain ethical standards and supporting the responsible integration of AI in education. A key contribution of this work is the generation of the CyberHumanAI dataset, which has 1000 observations, 500 of which are written by humans and the other 500 produced by ChatGPT. We evaluate various machine learning (ML) and deep learning (DL) algorithms on the CyberHumanAI dataset comparing human-written and AI-generated content from Large Language Models (LLMs) (i.e., ChatGPT). Results demonstrate that traditional ML algorithms, specifically XGBoost and Random Forest, achieve high performance (83% and 81% accuracies respectively). Results also show that classifying shorter content seems to be more challenging than classifying longer content. Further, using Explainable Artificial Intelligence (XAI) we identify discriminative features influencing the ML model's predictions, where human-written content tends to use a practical language (e.g., use and allow). Meanwhile AI-generated text is characterized by more abstract and formal terms (e.g., realm and employ). Finally, a comparative analysis with GPTZero show that our narrowly focused, simple, and fine-tuned model can outperform generalized systems like GPTZero. The proposed model achieved approximately 77.5% accuracy compared to GPTZero's 48.5% accuracy when tasked to classify Pure AI, Pure Human, and mixed class. GPTZero showed a tendency to classify challenging and small-content cases as either mixed or unrecognized while our proposed model showed a more balanced performance across the three classes. © 2025, CC BY.

关键词： Adversarial machine learning

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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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A Survey of Traffic Sign Recognition Systems Based on Convolutional Neural Networks

A Survey of Traffic Sign Recognition Systems Based on Convol...

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IEEE International Conference on Electro information Technology

作者： Syed Hussain Munther Abualkibash Samir Tout School of Information Security and Applied Computing Eastern Michigan University

In this paper, we briefly discuss the applications of Convolutional Neural Networks (CNNs) model to traffic sign recognition (TSR) systems. Traditionally, the TSRs have used different techniques to detect and classify visual data. The CNNs have been used separately to extract features and train the classifier as well as simultaneously for detection and classification tasks. One model that has been successful is the Fast Branch CNN model, which imitates biological mechanisms to become more efficient. While it is not the most accurate of the ones presented in this paper, the efficiency it exhibits under time-sensitive conditions is worth exploring because of the potential applications of such technology. The Fast Branch CNN model challenged the assumptions of past models, and this technology can only advance further if new models attempt to do the same.

关键词： TSR (Traffic Sign Recognition) CNN (Convolutional Neural Network) Detection Classification

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Resource Exhaustion Attack Detection Scheme for WLAN Using Artificial Neural Network

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Computers, Materials & Continua 2023年第3期74卷 5607-5623页

作者： Abdallah Elhigazi Abdallah Mosab Hamdan Shukor Abd Razak Fuad A.Ghalib Muzaffar Hamzah Suleman Khan Siddiq Ahmed Babikir Ali Mutaz H.H.Khairi Sayeed Salih Faculty of Engineering Future UniversityKhartoum 10553Sudan Faculty of Computing and Informatics Universiti Malaysia SabahKota Kinabalu88400Malaysia Information Assurance and Security Research School of ComputingUniversiti Teknologi MalaysiaJohor81310Malaysia School of Psychology and Computer Science University of Central LancashirePreston PR12HEUK Deaptement of Management Information Systems College of Administrative ScienceApplied Science UniversityAl Eker623Bahrain Deaptement of Information Technology College of Computer and Information SciencesKing Saud UniversityRiyadh11461Saudi Arabia

IEEE 802.11 Wi-Fi networks are prone to many denial of service(DoS)attacks due to vulnerabilities at the media access control(MAC)layer of the 802.11 *** to the data transmission nature of the wireless local area network(WLAN)through radio waves,its communication is exposed to the possibility of being attacked by illegitimate ***,the security design of the wireless structure is vulnerable to versatile *** example,the attacker can imitate genuine features,rendering classificationbased methods inaccurate in differentiating between real and false *** security standards have been proposed over the last decades to overcome many wireless network attacks,effectively detecting such attacks is crucial in today’s real-world *** paper presents a novel resource exhaustion attack detection scheme(READS)to detect resource exhaustion attacks *** proposed scheme can differentiate between the genuine and fake management frames in the early stages of the attack such that access points can effectively mitigate the consequences of the *** scheme is built through learning from clustered samples using artificial neural networks to identify the genuine and rogue resource exhaustion management frames effectively and efficiently in *** proposed scheme consists of four modules whichmake it capable to alleviates the attack impact more effectively than the related *** experimental results show the effectiveness of the proposed technique by gaining an 89.11%improvement compared to the existing works in terms of detection.

关键词： 802.11 media access control(MAC) wireless local area network(WLAN) artificial neural network denial-of-service(DoS)

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Privacy-Preserving Electricity Theft Detection Based on Blockchain

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IEEE Transactions on Smart Grid 2023年第5期14卷 4047-4059页

作者： Zhao, Zhiqiang Liu, Yining Zeng, Zhixin Chen, Zhixiong Zhou, Huiyu Guilin University of Electronic Technology School of Computer Science and Information Security Guilin541004 China Fujian Key Laboratory of Financial Information Processing The Key Laboratory of Applied Mathematics of Fujian Province University Putian University Putian351100 China University of Leicester School of Computing and Mathematical Sciences LeicesterLE1 7RH United Kingdom

In most electricity theft detection schemes, consumers' power consumption data is directly input into the detection center. Although it is valid in detecting the theft of consumers, the privacy of all consumers is at risk unless the detection center is assumed to be trusted. In fact, it is impractical. Moreover, existing schemes may result in some security problems, such as the collusion attack due to the presence of a trusted third party, and malicious data tampering caused by the system operator (SO) being attacked. Aiming at the problems above, we propose a blockchain-based privacy-preserving electricity theft detection scheme without a third party. Specifically, the proposed scheme uses an improved functional encryption scheme to enable electricity theft detection and load monitoring while preserving consumers' privacy;distributed storage of consumers' data with blockchain to resolve security problems such as data tampering, etc. Meanwhile, we build a long short-term memory network (LSTM) model to perform higher accuracy for electricity theft detection. The proposed scheme is evaluated in a real environment, and the results show that it is more accurate in electricity theft detection within acceptable communication and computational overhead. Our system analysis demonstrates that the proposed scheme can resist various security attacks and preserve consumers' privacy. © 2010-2012 IEEE.

关键词： Long short-term memory

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A new attack on three variants of the RSA cryptosystem 21st

A new attack on three variants of the RSA cryptosystem

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21st Australasian Conference on information security and Privacy, ACISP 2016

作者： Bunder, Martin Nitaj, Abderrahmane Susilo, Willy Tonien, Joseph School of Mathematics and Applied Statistics University of Wollongong Wollongong Australia Département de Mathématiques Université de Caen Caen France School of Computing and Information Technology Centre for Computer and Information Security Research University of Wollongong Wollongong Australia

ISBN: (纸本)9783319403663

In 1995, Kuwakado, Koyama and Tsuruoka presented a new RSA-type scheme based on singular cubic curves y2 = x3 + bx2 (mod N) where N = pq is an RSA modulus. Then, in 2002, Elkamchouchi, Elshenawy and Shaban introduced an extension of the RSA scheme to the field of Gaussian integers using a modulus N = PQ where P and Q are Gaussian primes such that p = |P| and q = |Q| are ordinary primes. Later, in 2007, Castagnos proposed a scheme over quadratic field quotients with an RSA modulus N = pq. In the three schemes, the public exponent e is an integer satisfying the key equation ed - k (p2 - 1) (q2 - 1) = 1. In this paper, we apply the continued fraction method to launch an attack on the three schemes when the private exponent d is sufficiently small. Our attack can be considered as an extension of the famous Wiener attack on the RSA. © Springer International Publishing Switzerland 2016.

关键词： Artificial intelligence

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Game Theory-Based IoT Efficient Power Control in Cognitive UAV

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Computers, Materials & Continua 2022年第7期72卷 1561-1578页

作者： Fadhil Mukhlif Norafida Ithnin Omar B.Abdulghafoor Faiz Alotaibi Nourah Saad Alotaibi Information Assurance and Security Research Group(IASRG) School of ComputingFaculty of EngineeringUniversiti Teknologi MalaysiaJohorMalaysia Electronic and Telecommunication Department College of EngineeringThe American University of KurdistanIraq Faculty of Computer Science and Information Technology Universiti Putra MalaysiaSelangorMalaysia Computer Department Faculty of Applied CollegeImam Abdulrahman Bin Faisal UniversityDammamSaudi Arabia

With the help of network densification,network coverage as well as the throughput can be improved via ultra-dense networks(UDNs).In tandem,Unmanned Aerial Vehicle(UAV)communications have recently garnered much attention because of their high agility as well as widespread *** this paper,a cognitive UAV is proposed for wireless nodes power pertaining to the IoT ground ***,the UAV is included in the IoT system as the source of power for the wireless nodes as well as for resource *** quality of service(QoS)related to the cognitive node was considered as a utility function based on pricing scheme that was modelled as a non-cooperative game theory in order to maximise users’net utility ***,an energy efficiency non-cooperative game theory power allocation with pricing scheme(EE-NGPAP)is proposed to obtain an efficient power control within IoT wireless ***,uniqueness and existence of the Nash equilibrium have been demonstrated mathematically and through *** results show that the proposed energy harvest algorithm demonstrated considerable decrease in transmitted power consumption in terms of average power reduction,which is regarded to be apt with the 5Gnetworks’***,the proposed algorithm requires around 4 iterations only to converge to NE which makes the algorithm more suitable in practical heterogeneous scenarios.

关键词： UAV drones WSN IoT game theory energy efficiency 5G&B5G networks

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Emerging Research in the security of Modern and Autonomous Vehicles

Emerging Research in the Security of Modern and Autonomous V...

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IEEE International Conference on Electro information Technology

作者： Samir Tout Munther Abualkibash Pooja Patil School of Information Security & Applied Computing Department of Computer Science Eastern Michigan University

The last decade has witnessed considerable advancements in automating modern "connected" and Autonomous Vehicles (AVs), which are becoming an integral part of the modern transportation systems. Such advancements have been attributed to major strides in Machine Learning, Artificial Intelligence and processing technology. More importantly, the complexity of such systems and their lack of built-in security have brought to the spotlight the need for extensive research to understand the associated risks and propose mitigations for them. In this paper we briefly discuss the security aspects of modern vehicles and provide a concise literature review of the work that has been done in this domain. We focus on specific areas that are of interest to us and conclude by providing a plan for future related endeavors.

关键词： Vehicle Autonomous security Networks Hacking

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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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