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Vision transformer for contactless fingerprint classification

Multimedia Tools and Applications 2024年 1-21页

作者： Kaplesh, Pooja Gupta, Aastha Bansal, Divya Sofat, Sanjeev Mittal, Ajay Cyber Security Research Centre Punjab Engineering College Chandigarh160012 India STME SVKM’s NMIMS University Chandigarh160014 India Computer Science and Engineering Punjab Engineering College Chandigarh160012 India UIET Panjab University Chandigarh160014 India

Contactless fingerprint recognition has gained attention due to its data security and hygiene as compared to the contact-based counterpart. In the previous years, deep learning models, mainly Convolutional Neural Networks (CNNs), have been extensively applied in this field and have given promising results. Recently, their successor, Vision Transformers (ViTs) have outperformed CNNs in various computer vision tasks due to their reliance on self-attention mechanism that focusses on capturing global and context-aware relationships between the image patches without inherent spatial bias. Due to the underlying advantageous and innovative feature extraction process, we propose to adapt ViT model for contactless fingerprint classification. To the best of our knowledge, this study would be first of its kind that applies ViT for contactless fingerprint classification. The performance of the model when evaluated on publicly available datasets, namely ISPFDv1, ISPFDv2, and UNFIT, results in average accuracies of 96%, 92%, and 91%, respectively. The comparative analysis with state-of-the-art CNN models using equal error rate (EER) metric shows that the proposed model outperforms them in classifying contactless fingerprints with a significant margin. Additionally, the Local Interpretable Model-agnostic Explanations (LIME) technique is integrated with the proposed model to enhance interpretability and explainability of the predictions. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Convolutional neural networks

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Vehicular Intrusion Detection System Based on Hybrid Quantum Neural Networks

Vehicular Intrusion Detection System Based on Hybrid Quantum...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Zhou, Jianhua Liu, Yueheng Li, Sifan Cao, Yue Chen, Wenkai Ahmed, Naveed Wuhan University School of Cyber Science and Engineering China Wuhan Cyber Security Association China Technology Co. Ltd China Prince Sultan University College of Computer and Information Sciences Riyadh Saudi Arabia

ISBN: (纸本)9798350351255

Traditional Deep Learning (DL) method is increasingly used in vehicular Intrusion Detection Systems (IDSs). However, there are still some limitations. It combines various models, resulting in a massive model size and numerous parameters, requiring more computational resources. Furthermore, real-time performance is crucial in vehicular IDS. Large models typically require more time for detection, failing to meet the timeliness requirements. Quantum computing offers parallel computing capabilities, prompting our proposal of a Hybrid Quantum Neural Network (HQNN)-based IDS to improve the timeliness of intrusion detection. This IDS employs HQNN to enhance feature extraction and speed up traditional convolution. It retains most of the structure of classical Convolutional Neural Network (CNN), comprising attention layers, batch normalization layers, and quantum circuits. Through quantum superposition and entanglement, certain complex non-linear functions are compressed. Furthermore, residual connections enable seamless gradient flow during back propagation. This accelerates computation and expedites convergence. Comparative experiments on the Car-hacking dataset show improved detection time and convergence speed, with exceptionally high accuracy in practical applications. © 2024 IEEE.

关键词： Network intrusion

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Personalized Educational Video Evaluation Combining Student's Cognitive and Teaching Style

Personalized Educational Video Evaluation Combining Student'...

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2023 IEEE International Conference on Systems, Man, and cybernetics, SMC 2023

作者： Weng, Jinta Dong, Haoyu Deng, Yifan Hu, Yue Wu, Hao Huang, Heyan School of Cyber Security University of Chinese Academy of Sciences China School of Computer Science and Cyber Engineering Guangzhou University China School of Computer Science and Technology Southeast Academy of Information Technology Beijing Institute of Technology China

ISBN: (纸本)9798350337020

AI-powered technologies, like ChatGPT and learning analytic technologies, have encouraged the sharing of online teaching resources and the transformation of teaching methods and learning pathways. However, the mixed resources and the result-oriented video evaluation repeatedly let students fall into an information trap and only appeal to students' attention to unsuitable resources. Inspired by human-computer interaction, a novel online video assessment LPSA(Linguistic- Presentative-scientific-Artistic) is proposed, integrated by cognitive style and teaching style, to realize more precise learning detection and teaching quality assessment. The LPSA evaluation consists of a four-level classification and eight secondary indexes quantified by machine learning algorithms. By automatically searching for an appropriate threshold of all secondary indexes, a real-time video assessment system is developed to certify its technical feasibility and pedagogical availability. The results show that the proposed AI-assisted assessment could realize practical pedagogical recommendations and real-time supervision. © 2023 IEEE.

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IoT-Based Plant Health Monitor Using NodeMCU and ESP8266 5

IoT-Based Plant Health Monitor Using NodeMCU and ESP8266

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5th International Conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Tandel, Rajat Ebenezer, V. Santhiya, P. Edwin, Bijolin Stewart Kirubakaran, S. Thanka, Roshni Karunya Institute of Technology and Sciences Division of Computer Science and Engineering Coimbatore India Karunya Institute of Technology and Sciences Division of Data Sciences and Cyber Security Coimbatore India

ISBN: (纸本)9798331518097

Plant Monitor Using NodeMCU(ESP8266)is a technology that is advanced;it uses IoT-Internet of Things to measure and monitor different plant phenomena. This system, which is based on sensors that collect data on different factors such as soil type, temperature, humidity, and other vital metrics vital for plant growth, is also integrated. Bringing IoT into the picture, the system can remotely sense the metrics and having control over other factors which include watering, shading, and even security provision for the plants to make it thrive. Besides that, it will also send notifications to users, hence allowing them to engage in the monitoring process through mobile applications with support from their smartphones. The Smart Plant Monitoring System though creates an impact on the efficiency of plant care also impacts on water conservation, automation of plant care process and better agricultural practices. © 2024 IEEE.

关键词： Efficiency

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cyberbullying Detection in Hinglish Language

Cyberbullying Detection in Hinglish Language

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2024 International Conference on Electrical, Electronics and Computing Technologies, ICEECT 2024

作者： Kumar, Rajiv Rai, Vipin Joshi, Shivani Raj, Daksh Amrita Galgotias University Department of Computer Science & Engineering U.P. Greater Noida India Galgotias College of Engineering and Technology Department of Computer Science and Engineering U.P. Greater Noida India Sharda University Center for Cyber Security and Cryptology Computer Science & Engineering Sharda School of Engineering & Technology Greater Noida India

ISBN: (纸本)9798350378092

cyberbullying represents one of the most recent challenges stemming from the widespread adoption of social media platforms. With the surge in social media usage, the right to express oneself is being misused. Statistics reveal that approximately 36.7% of individuals believe that they have encountered cyberbullying at some point in their lives. These figures have more than doubled since 2007, and a noticeable increase occurred from 2018 to 2019, indicating an unfavorable trend. While there are existing solutions in the market aimed at mitigating this issue, many of them come with usage limitations or employ fewer effective algorithms. The primary objective of this work is to explore innovative methods for comprehending and automatically identifying instances of cyberbullying across online text messages and comments on different social media platforms. For achieving this goal, real-time data are gathered, including comments, headlines and text messages from trending posts on twitter, and devised a labelling study for categorizing cyberbullying. An analysis is presented of this labelled data, which includes an examination of the relationships between various features and both cyberbullying and cyberaggression. The core focus of this work is the early detection of cyberbullying as it originates, while being drafted in real-time. The aim of this work is to enhance the efficiency of cyberbullying detection by using various machine learning techniques and natural language processing. © 2024 IEEE.

关键词： Phishing

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Credit Card Fraud Detection using ML: A Survey

Credit Card Fraud Detection using ML: A Survey

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2023 IEEE International Conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Bonkoungou, Samiratou Roy, Nihar Ranjan Ako, Nomel Haymes Axel-Elie Batra, Usha Sharda University Department of Computer Science and Engineering India Center of Cyber Security and Cryptography Sharda University India MRIIRS Faridabad India

ISBN: (纸本)9781665462631

The popularity of credit card fraud is rising as the number of cashless transactions increases. Online transactions are expanding as more people use credit cards and mobile wallets. The growth of internet commerce has also accelerated the spread of credit card fraud. Examining the cardholder's spending patterns can help identify credit card theft. Any odd behavior is grounds for the transaction to be deemed fraudulent. There are many difficulties in detection, such as frequent profile changes between fraudulent and normal transactions. Researchers use numerous techniques to detect credit card fraud, among them are hidden markov model, naives bayes classifier, decision tree, k-nearest neighbour classifier, logistic regression. The aim of this research paper is to examine divergent approaches of credit card fraud detection. © 2023 IEEE.

关键词： Hidden Markov models

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AI-Driven Solutions for Cloud security Implementing Intelligent Threat Detection and Mitigation Strategies

AI-Driven Solutions for Cloud Security Implementing Intellig...

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2024 IEEE International Conference on Intelligent Computing and Emerging Communication Technologies, ICEC 2024

作者： Yasani, Rajashekar Reddy Prasad, Putalpattu Muni Srinivas, Pattlola Reddy, N V Raja Sekhar Jawarkar, Parag Raghunath, Vedaprada Cloud Security Cloud Computing Cyber Security India University of Rome Tor Vergata Department of Industrial Engineering Italy Department of Computer Science and Engineering Telangana India Mlr Institute of Technology Department of It India Shri Ramdeobaba College of Engineering and Management Electronics Engineering Nagpur India Visveswaraya Technological University India

ISBN: (纸本)9798331508432

Adapting threat detection and response procedures is essential for businesses to stay ahead of the constantly developing cyberattack landscape. Researching cutting-edge tools like XDR, SIEM, SOAR, and NDR - which shed light on the dynamic realm of detection and response systems - is crucial. Other areas of investigation include real-time monitoring and network forensics. security and threat detection have both been greatly enhanced since businesses began storing their apps and data in the cloud. In order to safeguard vulnerable network infrastructures in cloud settings against advanced attacks, traditional security procedures must be revised. Artificial intelligence (AI) is used to learn more about this problem so that threat reaction and identification can be done more quickly and accurately. Artificial intelligence has changed cloud security and threat identification, as this study shows. Because cyberattacks on cloud infrastructures and service providers are becoming more common, strong security steps that are also easy to use are still needed. This piece will talk about how cloud security and AI operations work together, with a focus on how this collaboration speeds up the time it takes to respond to incidents. The paper will also show how this relationship helps a company improve its defences and lessen the impact of security events. Organisations that want to keep up with the constantly changing danger landscape need to use cloud security and AI and understand how the two work together. This is because it is necessary to keep a security stance that is both flexible and strong. © 2024 IEEE.

关键词： Cloud security

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A Survey on Trustworthy Edge Intelligence: From security and Reliability To Transparency and Sustainability

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IEEE Communications Surveys and Tutorials 2024年第3期27卷 1-1页

作者： Wang, Xiaojie Wang, Beibei Wu, Yu Ning, Zhaolong Guo, Song Yu, Fei Richard School of Communications and Information Engineering Chongqing University of Posts and Telecommunications Chongqing China School of Cyber Security and Information Law Chongqing University of Posts and Telecommunications Chongqing China Department of Computer Science and Engineering The Hong Kong University of Science and Technology Kowloon Hong Kong China School of Information Technology Carleton University Ottawa ON Canada

Edge Intelligence (EI) integrates Edge Computing (EC) and Artificial Intelligence (AI) to push the capabilities of AI to the network edge for real-time, efficient and secure intelligent decision-making and computation. However, EI faces various challenges due to resource constraints, heterogeneous network environments, and diverse service requirements of different applications, which together affect the trustworthiness of EI in the eyes of stakeholders. This survey comprehensively summarizes the characteristics, architecture, technologies, and solutions of trustworthy EI. Specifically, we first emphasize the need for trustworthy EI in the context of the trend toward large models. We then provide an initial definition of trustworthy EI, explore its key characteristics and give a multi-layered architecture for trustworthy EI. Then, we present enabling technologies for trustworthy EI systems. Subsequently, we summarize several important issues that hinder the achievement of trustworthy EI and provide an in-depth literature review of the state-of-the-art solutions for realizing the trustworthiness of EI. Finally, we discuss the corresponding research challenges and open issues. IEEE

关键词： Mobile edge computing

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Enhancing Wireless Sensor Network Data Collection through Aerial Unmanned Vehicles

Enhancing Wireless Sensor Network Data Collection through Ae...

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2024 International Conference on Decision Aid sciences and Applications, DASA 2024

作者： Ahmed, Amjed Abbas Ali Alheeti, Khattab M. Najim, Ali Hamza Satar, Nurhizam Safie Mohd Aliesawi, Salah A. Hashim, Aisha Hassan Abdallah Universiti Kebangsaan Malaysia Center for Cyber Security Faculty of Information Science and Technology Bangi Malaysia Department of Computer Techniques Engineering Baghdad Iraq College of Computer and Information Technology Computer Networking Systems Department University of Anbar Anbar Iraq Department of Computer Technical Engineering Al-Diwaniyah Iraq Center for Cyber Security Faculty of Information Science and Technology Bangi43600 Malaysia College of Computer and Information Technology Department of Computer Networking Systems University of Anbar Anbar Iraq Faculty of Engineering Department of Electrical and Computer Engineering International Islamic University Malaysia Selangor Malaysia

ISBN: (纸本)9798350369106

This study addresses the critical aspect of data collection within Wireless Sensor Networks (WSNs), which consist of autonomous, compact sensor devices deployed to monitor environmental conditions. These networks have limited processing capacity, storage, short-range communication capabilities, and constrained battery power. A primary challenge in WSN s revolves around data gathering, particularly when employing static sinks, leading to premature energy depletion in nodes near these sinks. Integrating mobile collectors introduces the potential for energy conservation by shortening data routes. This article introduces three distinct algorithms for optimizing data collection in WSN s. Our approach involves an in-depth analysis of diverse strategies to mitigate packet collisions and facilitate the retransmission of lost packets. Our findings underscore the considerable impact that packet collisions can exert on the overall network performance during data gathering within wireless sensor networks. Through empirical exploration and analysis, we highlight the significance of these algorithms in enhancing the efficiency, energy conservation, and reliability of data collection processes within WSNs. This research sheds light on these algorithms' vital role in addressing the pressing challenges of data gathering and network performance optimization in wireless sensor networks. © 2024 IEEE.

关键词： Aircraft accidents

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A novel coarse-grained knowledge graph embedding framework for platform risk identification from relational data

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Neural Computing and Applications 2025年 1-21页

作者： Zhang, Qi Wang, Shicheng Wang, Lihong Sheng, Jiawei Guo, Shu Li, Chen He, Min Zhang, Renqiang School of Economics and Management Beihang University Beijing China Institute of Information Engineering Chinese Academy of Sciences Beijing China School of Cyber Security University of Chinese Academy of Sciences Beijing China National Computer Network Emergency Response Technical Team Coordination Center of China Beijing China School of Computer Science and Engineering Beihang University Beijing China

Platform risk identification from relational data aims to determine whether the target platforms are risky or not. However, in such a scenario, the relation data involve massive types of relations and numerous numerical-aware relations composed of attributes and infinite continuous values are challenges in this field. It is paramount to capture semantic information of relations and precise control power, drastically affecting the power of learning and the performance of identifying risky platforms. Recent financial risk studies often employed graph neural networks (GNNs) to model relational data, which fall short of capturing the intricacies of such complex relationships resulting in incorrect identification of platforms. To solve this problem, we propose a novel coarse-grained knowledge graph embedding (CKGE) framework that integrates coarse-grained knowledge graph construction and adopts five well-known knowledge graph embedding methods and ensemble learning. The extensive experimental results based on a real-world peer-to-peer lending platform dataset demonstrate that the CKGE framework achieves state of the art over GNNs for platform risk identification. Our study highlights the importance of utilizing the relational data of multi-type and multi-numerical relations for revealing platform risk situations and provides an alternative solution for identifying platform risk. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Knowledge graph

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