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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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Hybridization of deep learning models with crested porcupine optimizer algorithm-based cybersecurity detection on industrial IoT for smart city environments

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Alexandria Engineering Journal 2025年 127卷 239-250页

作者： Alzakari, Sarah A. Aljebreen, Mohammed Asiri, Mashael M. MANSOURI, Wahida Alahmari, Sultan Alqahtani, Mohammed Sorour, Shaymaa Bedewi, Wafi Department of Computer Sciences College of Computer and Information Sciences Princess Nourah bint Abdulrahman University P.O. Box 84428 Riyadh11671 Saudi Arabia Department of Computer Science Community College King Saud University P.O. Box 28095 Riyadh11437 Saudi Arabia Department of Computer Science Applied College at Mahayil King Khalid University Saudi Arabia Department of Computer Science and Information Technology Faculty of Sciences and arts Turaif Northern Border University Arar91431 Saudi Arabia Cybersecurity Institute Riyadh Saudi Arabia Department of Information System and Cyber Security College of Computing and Information Technology University of Bisha Bisha61922 Saudi Arabia Department of Management Information Systems School of Business King Faisal University Al-Ahsa31982 Saudi Arabia Department of Information Systems Faculty of Computing and Information Technology King Abdulaziz University Jeddah Saudi Arabia

Smart cities have attracted extensive coverage from multidisciplinary studies, and many artificial intelligence (AI) solutions have been designed. Conversely, cybersecurity has constantly been a crucial issue and is becoming gradually dangerous in smart cities. The attack defence models are inappropriate for perceiving multistep assaults as the recognition rules are restricted, and efficacy is partial due to many false security alarms. Therefore, an innovative solution is immediately required to progress cybersecurity defence ability. Machine learning (ML) methods are commonly employed to recognize numerous attacks because they could help network administrators grab analogous initials to avert intrusion. This study presents Cybersecurity using a Crested Porcupine Optimizer Algorithm with Hybrid Deep Learning Models (CCPOA-HDLM). The foremost intention of this study is to improve cybersecurity detection and classification in smart city environments. To accomplish that, the CCPOA-HDLM method comprises distinct processes such as min-max normalization, improved Salp swarm algorithm (ISSA)-based feature selection, Multi-Channel Convolutional Neural Network - Recurrent Neural Network (MCNN-RNN)-based cybersecurity detection, and crested porcupine optimizer (CPO)-based parameter selection process. Primarily, data normalization utilizing min-max normalization is implemented. Next, the CCPOA-HDLM method utilizes ISSA based feature selection method to select optimum features. The CCPOA-HDLM technique employs a hybrid of the MCNN-RNN model for the cybersecurity detection and classification process. Moreover, the hyperparameter range of the hybrid of DL techniques occurs using the CPO technique. The experimental validation of the CCPOA-HDLM approach is performed on the UNSW-NB15 dataset, and the outcome portrayed a superior accuracy value of 99.04 % over other recent approaches under various measures. © 2025 The Authors

关键词： Convolutional neural networks

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Dataset Evaluation Framework for Secure Electric Vehicle Systems

Dataset Evaluation Framework for Secure Electric Vehicle Sys...

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Computational Intelligence Approaches and Applications (ICCIAA), International Conference on

作者： Mohammad Alauthman Ghadeer Albesani Ashraf Mashaleh Ammar Almomani Amjad Aldweesh Department of Information Security University of Petra Jordan King Abdullah II School of Information Technology Jordan University Jordan Al-Balqa Applied University Al-Salt Jordan Department of Computer Information Science Higher Colleges of Technology Al-Balqa UAE Applied University Jordan College of computing and IT Shaqra University Shaqra Saudi Arabia

ISBN: (数字)9798331523657

ISBN: (纸本)9798331523664

Intrusion Detection Systems (IDS) play a critical role in safeguarding connected vehicular systems and Electric Vehicle Supply Equipment (EVSE) infrastructures against evolving cyber threats. The effectiveness of these IDS solutions largely depends on the quality and relevance of the datasets used for their training and evaluation. In this paper, we present a systematic framework for assessing datasets intended for IDS development in the automotive and EV charging domains. Our analysis covers essential criteria—such as data completeness, realism, labeling accuracy, and statistical distribution—and provides best practices for evaluating both traditional and novel datasets (e.g., CICIoV2024, CICEVSE2024). We further detail methodologies for intrinsic and extrinsic evaluations, including statistical analysis, time-based assessment, and benchmark testing using machine learning algorithms. Our approach is illustrated through a case study that demonstrates how these criteria and methods can be applied to real-world datasets, highlighting potential pitfalls and improvements. By offering concrete guidelines and metrics for dataset quality, this work aims to advance the development of robust IDS solutions in connected automotive and EV charging environments.

关键词： Training Systematics Statistical analysis Intrusion detection Electric vehicle charging Labeling Computer security Standards Automotive engineering Internet of Vehicles

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Background subtraction based on tensor Robust principal component analysis with side information

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Signal, Image and Video Processing 2025年第8期19卷 1-11页

作者： Xu, Xin Chen, Lixia Wang, Xuewen School of Mathematics and Computing Science Guangxi Colleges and Universities Key Laboratory of Data Analysis and Computation Guilin University of Electronic Technology Guilin People’s Republic of China Center for Applied Mathematics of Guangxi (GUET) Guilin People’s Republic of China School of Computer Science and Information Security Guilin University of Electronic Technology Guilin China

In the field of video image processing, moving target detection remains a hot topic. To address the limitations of existing methods in complex environments, This paper proposes a novel TRPCA model based on Tensor Singular Value Decomposition (T-SVD), incorporating the advantages of side information. Firstly, by imposing $$\gamma $$ -norm constraints, the method incorporates feature side information into the background component processing, addresses the over-penalization issue caused by the nuclear norm in traditional RPCA. Secondly, for the foreground part, $$L_{1,1,2}$$ norm and tensor total variation (TTV) regularization constraints are applied to enhance the model’s sensitivity to tubal sparsity and spatiotemporal continuity, effectively reducing the interference of dynamic backgrounds on foreground extraction. To solve this model, we employ the Alternating Direction Method of Multipliers (ADMM). Extensive experiments on the datasets CDnet2014 and LASIESTA demonstrate that the proposed method achieves optimal or near-optimal performance in terms of F-measure for the majority of cases, highlighting its superiority in foreground detection precision.

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A Survey on Decentralized Identifiers and Verifiable Credentials

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IEEE Communications Surveys and Tutorials 2025年

作者： Mazzocca, Carlo Acar, Abbas Uluagac, Selcuk Montanari, Rebecca Bellavista, Paolo Conti, Mauro University of Salerno Department of Information and Electrical Engineering and Applied Mathematics Fisciano84084 Italy Florida International University Cyber-Physical Systems Security Lab School of Computing and Information Science MiamiFL33174 United States University of Bologna Department of Computer Science and Engineering Bologna40136 Italy University of Padua Department of Mathematics Padua35131 Italy

Digital identity has always been one of the keystones for implementing secure and trustworthy communications among parties. The ever-evolving digital landscape has undergone numerous technological transformations that have profoundly reshaped digital identity management, leading to a major shift from centralized to decentralized identity models. The latest stage of this evolution is represented by the emerging paradigm of Self-Sovereign Identity (SSI), which gives identity owners full control over their data. SSI leverages Decentralized Identifiers (DIDs) and Verifiable Credentials (VCs), which have been recently standardized by theWorldWideWeb Consortium (W3C). These technologies have the potential to build more secure and decentralized digital identity systems, significantly strengthening communication security in scenarios involving many distributed participants. It is worth noting that use of DIDs and VCs is not limited to individuals but extends to a wide range of entities including cloud, edge, and Internet of Things (IoT) resources. However, due to their novelty, existing literature lacks a comprehensive survey on DIDs and VCs beyond the scope of SSI. This paper fills this gap by providing a comprehensive overview of DIDs and VCs from multiple perspectives. It identifies key security threats and mitigation strategies, analyzes available implementations to guide practitioners in making informed decisions, and reviews the adoption of these technologies across various application domains. Moreover, it also examines related regulations, projects, and consortiums emerging worldwide. Finally, it discusses the primary challenges hindering their real-world adoption and outlines future research directions. © 1998-2012 IEEE.

关键词： Decentralized control

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SEDNet: Synergistic Learning Network with Embedded Encoder and Dense Atrous Convolution for Vehicle Re-identification

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Alexandria Engineering Journal 2025年 128卷 297-305页

作者： Mingfu Xiong Tanghao Gui Zhihong Sun Saeed Anwar Aziz Alotaibi Khan Muhammad School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan 430200 China Department of Information Security Naval University of Engineering Wuhan 430030 China The University of Western Australia (UWA) Crawley 6009 Australia Department of Computer Science College of Computing and Information Technology Taif University Taif 21974 Saudi Arabia Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab) Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea

To address the issue of information redundancy (such as color and vehicle model) caused by excessive emphasis on local features in vehicle re-identification, this paper proposes a Synergistic Learning Network with Embedded Encoder and Dense Atrous Convolution (SEDNet). The proposed SEDNet framework consists of three unique branches: a global embedded multi-head encoder (GEME), local dual-dense atrous convolution (LDAC), and auxiliary attribute embedding (AAM). The GEME branch integrates the global appearance features of the vehicle to enhance consistency in descriptions from different perspectives. To suppress redundant information such as color and vehicle model information, and refine local features, the LDAC branch employs an attention mechanism to capture multiscale features using convolutional kernels with varying dilation rates. In addition, the AAM branch uses vehicle metadata, such as direction and camera perspectives, to enhance feature robustness. Our proposed SEDNet method has been rigorously tested on the mainstream benchmark vehicle re-identification datasets, including VeRi-776, VehicleID, and VeRi-Wild. The results show that our method enhances the mAP by 2.2%, 2.2%, and 0.2%, respectively, when compared to the latest methods, all evaluated on a regular scale. Additional experiments conducted on the Market-1501 and DukeMTMC-reID datasets further verify our method’s generalization capability.

关键词： Vehicle re-identification Embedded encoder Dense atrous convolution Intelligent networks Synergistic learning

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On the -differential spectra of two classes of power functions over finite fields

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Applicable Algebra in Engineering, Communication and computing 2025年 1-30页

作者： Liu, Qian Huang, Zhiwei Chen, Zhixiong Jiang, Rong Zhang, Liupiao Key Laboratory of Information Security of Network Systems College of Computer and Data Science Fuzhou University Fuzhou China Hubei Key Laboratory of Applied Mathematics Faculty of Mathematics and Statistics Hubei University Wuhan China Yunnan Key Laboratory of Service Computing Yunnan University of Finance and Economics Kunming China Fujian Key Laboratory of Financial Information Processing Putian University Putian China School of Computer Science and Technology Dongguan University of Technology Dongguan China

Recently, a new concept called multiplicative differential was introduced by Ellingsen et al. [7]. As an extension of the differential uniformity, it is theoretically appealing to determine the properties of c-differential uniformity and the corresponding c-differential spectrum. In this paper, based on certain quadratic character sums and two special elliptic curves over $$\mathbb {F}_p$$ , the $$(-1)$$ -differential spectra of the following two classes of power functions over $$\mathbb {F}_{p^n}$$ is completely determined: (1) $$f_1(x)=x^{\frac{p^n+3}{2}}$$ , where $$p>3$$ and $$p\equiv 3\pmod 4$$ ; (2) $$f_2(x)=x^{p^n-3}$$ , where $$p>3$$ . The obtained result shows that the $$(-1)$$ -differential spectra of $$f_1(x)$$ and $$f_2(x)$$ can be expressed explicitly in terms of n. Moreover, an upper bound of the c-differential uniformity of $$f_2(x)$$ is given.

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Provably Secure APK Redevelopment Authorization Scheme in the Standard Model

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Computers, Materials & Continua 2018年第9期56卷 447-465页

作者： Daofeng Li Mingxing Luo Bowen Zhao Xiangdong Che School of Computer Electrical and informationGuang Xi UniversityNanning530004China Information Security and National Computing Grid Laboratory Southwest Jiaotong UniversityChengdu610031China Guangxi Colleges and Universities Key Laboratory of Multimedia Communications and Information Processing Guangxi UniversityNanning530004China School of Information Security&Applied Computing College of TechnologyEastern Michigan UniversityMichigan48197USA

The secure issues of APK are very important in Android *** order to solve potential secure problems and copyrights issues in redevelopment of APK files,in this paper we propose a new APK redevelopment mechanism(APK-SAN).By exploring sanitizable signature technology,APK-SAN allows the original developer to authorize specified modifier who can redevelop the designated source code of APK *** scheme does not require interactions between the developer and *** can reduce the communication overhead and computational overhead for ***,the signature of redeveloped APK files is valid and maintains the *** proposed APK-SAN signature can effectively protect the security of the redeveloped APK files and copyrights of the developer and modifier.

关键词： Sanitizable signature APK signature mechanism redevelopment ID-based signature scheme

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A Hybrid Deep Learning Paradigm for Robust Feature Extraction and Classification for Cataracts

Applied AI Letters

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applied AI Letters 2025年第2期6卷

作者： Ramakrishnan, Akshay Bhuvaneswari Madavan, Mukunth Manikandan, R. Gandomi, Amir H. Department of Applied Mathematics and Statistics Johns Hopkins University Baltimore MD United States Mukunth Madhavan AI Security Engineer Banglore India School of Computing SASTRA Deemed University Thanjavur India Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia University Research and Innovation Center (EKIK) Óbuda University Budapest Hungary Department of Computer Science Khazar University Baku Azerbaijan

The study suggests using a hybrid convolutional neural networks-support vector machines architecture to extract reliable characteristics from medical images and classify them as an ensemble using four different models. Manual processing of fundus images for the automated identification of ocular disorders is laborious, error-prone, and time-consuming. This necessitates computer-assisted technologies that can automatically identify different ocular illnesses from fundus images. The interpretation of the photos also plays a massive role in the diagnosis. Automating the diagnosing procedure reduces human mistakes and helps with early cataract detection. The oneDNN library available in the oneAPI Environment provided by Intel has been used to optimize all transfer learning models for better performance. The suggested approach is verified through a range of metrics in experiments using the openly accessible Ocular Disease Intelligent Recognition dataset. The MobileNet Model outperformed other transfer learning techniques with an accuracy of 0.9836. © 2025 The Author(s). applied AI Letters published by John Wiley & Sons Ltd.

关键词： cataract computer-assisted fundus hybrid ocular oneAPI oneDNN transfer learning

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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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