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Deep learning and encryption algorithms based model for enhancing biometric security for artificial intelligence era

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Journal of Ambient Intelligence and Humanized Computing 2024年 1-14页

作者： Byeon, Haewon Shabaz, Mohammad Surbakti, Herison Keshta, Ismail Soni, Mukesh Bhatnagar, Vaibhav Department of Digital Anti-Aging Healthcare Inje University Gimhae50834 Korea Republic of Model Institute of Engineering and Technology J&K Jammu India Information and Communication Technology Rangsit University International College Pathumthani Lak Hok Thailand Computer Science and Information Systems Department College of Applied Sciences AlMaarefa University Riyadh Saudi Arabia Department of CSE University Centre for Research & ampDevelopment Chandigarh University Punjab Mohali140413 India Department of Computer Applications Manipal University Jaipur Jaipur India

The significance of facial recognition in the era of artificial intelligence lies in its utilization of facial features as a type of biometric characteristic possessing uniqueness and irreversibility. However, exposing these features to attacks, tampering, or unauthorized disclosure poses considerable threats to user privacy and security. A privacy and security solution based on deep learning and encryption algorithms is proposed to tackle this issue. This solution employs the FaceNet deep learning algorithm to extract facial features efficiently. The combination of biometric feature blurriness and cryptographic system precision is achieved, utilizing the CKKS fully homomorphic encryption algorithm for operations in the ciphertext domain of facial recognition. The SM4 algorithm is used to enhance the resilience of facial feature ciphertext against malicious attacks. By leveraging the properties of symmetric ciphers, a balance is achieved between security and computational efficiency. The management of the symmetric key used in the SM4 algorithm is conducted through the employment of the SM9 asymmetric encryption algorithm. Experimental results and analysis demonstrate that the proposed solution enhances the security of data transmission, storage, and comparison without compromising the accuracy and efficiency of facial recognition. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2024.

关键词： Differential privacy

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Misbehavior Detection in the Internet of Vehicles using Machine Learning based Situation Identification 8

Misbehavior Detection in the Internet of Vehicles using Mach...

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8th International Conference on communication and Electronics Systems, ICCES 2023

作者： Kelagadi, Hemantaraj M. Chandra Sekhar, Jampani Rawat, Ruchira Dhaliwal, Navdeep Singh, Swati Saravana, B. Shanthi Kle Technological University School of Electronics and Communication Engineering Karnataka Hubballi India Nri Institute of Technology Department of Computer Science and Engineering Andhra Pradesh Guntur India Graphic Era Deemed to Be University Department of Computer Science and Engineering Uttarakhand Dehradun India Lovely Professional University Division of Research and Development Punjab Phagwara India Uttaranchal University Division of Research and Innovation Uttarakhand Dehradun India Adhiparasakthi College of Engineering Department of Electrical and Electronics Engineering Tamil Nadu Kalavai India

ISBN: (纸本)9798350396638

To improve communications network efficiency, situation identification is essential. The requirement for real-time scenario identification of vehicular communications is growing more pressing provided a certain vehicle movement makes vehicle interaction circumstances vibrant and that there are significant variations in signal propagation, vehicle speeds, and vehicular concentrations among possible situations. On the other hand, it has been demonstrated that cryptography methods are less effective at preventing insider attacks that originate inside the automotive communication network. A misbehaviour detection system has been found to be more effective in identifying and preventing insider attacks. This research study presents a misbehaviour detection system based on machine learning and is learned utilizing datasets produced by numerical simulations in a genuine vehicular distributed system. Vehicle communication systems may choose the proper spatial multiplexing and system integration by correctly detecting the current conditions in order to maintain an efficient and reliable operating condition. A back-propagation neural network (BPNN) is trained, and a scenario identification model is developed, using datasets that comprise channel properties acquired from channel measurements in various scenarios. Additionally, the proposed identification model's best performance-enabling model configuration technique is analysed and offered. The identification accuracy is then verified using validation information from the relevant circumstances. © 2023 IEEE.

关键词： Vehicles

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A Platform for Monitoring Student Commuting in the Use of School Transport in Smart Cities - A Facial Recognition Based Approach

A Platform for Monitoring Student Commuting in the Use of Sc...

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2023 International Conference on Smart Applications, communications and Networking, SmartNets 2023

作者： Da Costa Rocha, Jesse De Souza, Marcela Alves Cardoso, Evelin Helena Silva Vijaykumar, Nandamudi De Araujo, Jasmine Priscyla Leite Frances, Renato Lisboa Graduate Program in Electrical Engineering Belém Para Brazil Information and Communication Technology Center Marabá Para Brazil Department of Computer Science Capitão Poço Para Brazil Coordination of Applied Research and Technological Development São Paulo Brazil

ISBN: (纸本)9798350302523

This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and competent authorities in various situations of school life, such as: entering and leaving the school bus, entering and leaving school, entering the school cafeteria, etc. The first application aims to control access to the school bus through facial recognition. In the tests carried out, the recognition system achieved excellent results in all metrics. © 2023 IEEE.

关键词： Students

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The Novel use of AI and ML System Integration in Developing Spruce Metropolis 4

The Novel use of AI and ML System Integration in Developing ...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Singh, Rahul Maheswari, K. Sridevi, G. Pant, Ruby Alzubaidi, Laith H. Shetty, Mahalakshmi Chitkara University Chitkara Centre for Research and Development Himachal Pradesh 174103 India Cmr Technical Campus Department of Cse Telangana Hyderabad India Aditya Engineering College Department of Electronics and Communication Engineering Surampalem India Uttaranchal Institute of Technology Uttaranchal University Department of Mechanical Engineering Dehradun India The Islamic University Najaf Iraq R.L Jallappa Institute of Technology Department of Computer Science Engineering Doddabalapura India

ISBN: (纸本)9798350360165

This paper presents a pioneering routing protocol, particularly in urban infrastructures, by the utilization of a combination of SDN and machine learning technologies, mostly Naïve Bayes, to the programmer. The emerging Internet of Things and 5 G will offer new improvements in connectivity and infrastructure management in smart cities. This paper therefore targets transportation systems of smart cities in order to reduce end-to-end delays and reduce air congestion through predictive routing optimization. The proposed framework is conducted through several steps, among them the generation of a complete dataset derived from the real-world traffic simulations, and realized through the utilization of a mechanism that is commonly known as the Simulation of Urban Mobility (SUMO). This will help the proposed protocol to know the good approaches based on predictive routing with the help of defined Naïve Bayes algorithms in the SDN controller. This architecture introduces a central controller responsible for coordinating communication in the network. The evaluation of the performance of existing routing methods, including multipath routing, optimized link state routing (OLSR), and along with Q learning, shows the importance of the proposed SDN-Naïve Bayes approach. The metrics are packet delivery ratio, throughput, packet loss ratio, end-to-end delay, packet jitter, etc., which will show the effectiveness of the new protocol when enjoying the high quality of the network. The proposed protocol has great potentials in improving the efficiency and reliability of communications in smart city environments using the capacities of SDN and ML. Introducing changes in the route in a predictive manner will not only optimize the use of resources, but also help in the achievement of reliable and reliable connectivity, which holds importance for the development of urban infrastructure. © 2024 IEEE.

关键词： Traffic congestion

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Influence of Access Points’ Height and High Signal Relation in WLAN Fingerprinting-Based Indoor Positioning Systems’ Accuracy 1

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26th International Conference on Distributed computer and communication Networks: Control, Computation, communications, DCCN 2023

作者： Nicholaus, Mrindoko R. Ruambo, Francis A. Masanga, Elijah E. Muthanna, Mohammed Saleh Ali Lashchev, Andrei Computer Science and Engineering Department College of Information and Communication Technology Mbeya University of Science and Technology 131 Mbeya Tanzania United Republic of Information Systems and Technology Department College of Information and Communication Technology Mbeya University of Science and Technology 131 Mbeya Tanzania United Republic of School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Research and Development Department Aifrruis Laboratories Mbeya Tanzania United Republic of Institute of Computer Technologies and Information Security Southern Federal University Taganrog347922 Russia

ISBN: (数字)9783031504822

ISBN: (纸本)9783031504815

Wireless Local Area Network (WLAN) Fingerprinting-based Indoor Positioning Systems (IPS) offer several key advantages over other indoor positioning technologies, including cost-effectiveness with high accuracy and passive tracking of entities’ location without Global Positioning System (GPS) dependence. However, achieving a satisfactory accuracy performance of the WLAN fingerprinting-based IPS is still challenging, as it is affected by several factors, such as obstacles (e.g., walls and furniture) that can block or weaken wireless signals. Additionally, the IPS’s accuracy can be affected by environmental changes, such as the height, addition, or removal of a WLAN Access Point (AP) or changes in radio frequency interference. This paper presents a novel algorithm incorporating a probabilistic analytical model employing only high signal relations that mitigate the effect of low signal relation on the WLAN fingerprinting-based IPS, thus improving the accuracy performance. Furthermore, it analyses the impact of AP heights on the accuracy performance of the WLAN IPS. Upon extensive experiments, the proposed algorithm improves the accuracy of the IPS by an average of 32.74% in terms of Root Mean Square Error (RMSE). These results imply that incorporating APs’ height and high signal relations can significantly improve the accuracy performance of the WLAN fingerprinting-based IPS. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Cost effectiveness

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Cognitive Twins for Predictive Maintenance and Security in IoT Software Systems 4

Cognitive Twins for Predictive Maintenance and Security in I...

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4th IEEE International Conference on Mobile Networks and Wireless communications, ICMNWC 2024

作者： Swamy, B. Venkata Barmola, Pareshwar Prasad Thangavel, Senthil Kaliappan, S. Patel, Hardik Abhyankar, Girish B V Raju Institute of Technology Department of Chemistry Medak India Graphic Era Deemed to be University Department of Computer Science & Engineering Dehradun India Paypal Inc CA United States Lovely Professional University Division of Research and Development Phagwara India Parul institute of Engineering and Technology Parul University Faculty of Engineering and Technology Department of Electronics & Communication Engineering Post Limda India Faculty of Law Pune India

ISBN: (纸本)9798350352931

The increasing complexity and interconnectedness of Internet of Things (IoT) software systems necessitate the development of intelligent solutions for predictive maintenance and security. Conventional techniques often fail to provide real-time insights and proactive responses due to the diverse and dynamic nature of IoT environments. To address these challenges, cognitive technologies offer promising avenues for enhancing the operational efficiency and security of IoT networks. This paper introduces Cognitive Twins, an AI-driven framework designed to optimize predictive maintenance and strengthen security in IoT software systems. Cognitive Twins leverage advanced machine learning models and real-time data streams to create dynamic digital replicas of IoT devices and software components. The framework employs a combination of deep learning-based anomaly detection, reinforcement learning for proactive maintenance scheduling, and natural language processing (NLP) for automated security log analysis. By continuously learning from device interactions and evolving threat patterns, Cognitive Twins predict potential failures and detect security threats before they occur, enabling real-time decision-making and automated responses. Cognitive Twins were evaluated on a large-scale IoT network, consisting of 500 nodes across multiple application domains. The framework achieved a predictive maintenance accuracy of 96.8%, reducing downtime by 35% compared to traditional models. In security applications, Cognitive Twins identified cyber threats with a detection rate of 98.3%, lowering the false positive rate to 1.5%. © 2024 IEEE.

关键词： Reinforcement learning

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A Way of Making Smart Health Through Collaborating Machine Learning with the Bibliometrics 4

A Way of Making Smart Health Through Collaborating Machine L...

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4th IEEE International Conference on Advance Computing and Innovative Technologies in engineering, ICACITE 2024

作者： Vignesh, T. Sruthi, P.S. Mohanraj, T. Kumar, Gaurav Varkale, Akshay Sheela, M.Sahaya Prince Shri Venkateshwara Padmavathy Engineering College Department of Computer Science and Engineering TN India Nehru Institute of Technology Department of Computer Science and Business System Tamil Nadu T.M. Palayam India Karpagam Academy of Higher Education Department of Computer Science Engineering India Chitkara University Chitkara Centre for Research and Development Himachal Pradesh India M.P Bhopal India Vel Tech Rangarajan Dr. Sagunthala R&d Institute of Science and Technology Department of Electronics and Communication Engineering Tamil Nadu Chennai India

ISBN: (纸本)9798350360165

It datasets are widely utilized, shared, and repurposed without providing enough context for understanding the decision-making processes that led to their creation. It is necessary to modify system creation and use procedures to handle the concrete effects of how knowledge for model building is envisioned and used in real-world applications as AI systems assume more and more important roles. Documents are created at every phase of the data generation lifecycle to improve communication, aid in decision-making, and highlight the need of careful data work. By putting out this paradigm, we want to shed light on the choices and duties that are often disregarded while creating datasets, which is a crucial first step in bridging the duty of care gap in AI. This framework provides an essential and important resource for guaranteeing ethical AI activities, and it is in line with current developments in auditing procedures. © 2024 IEEE.

关键词： Adversarial machine learning

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Access Control in E-Healthcare Records Employing Mobile Cloud Computing Model and Big Data Analytics 6

Access Control in E-Healthcare Records Employing Mobile Clou...

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6th International Conference on Inventive Computation Technologies, ICICT 2023

作者： Sekhar, Jampani Chandra Ezhilarasi, P. Bordoloi, Dibyhash Dhaliwal, Navdeep Sisodia, Shivani Shobana, A. Department of Computer Science and Engineering NRI Institute of Technology Andhra Pradesh Guntur India Department of Electronics and Communication Engineering St. Joseph's College of Engineering Tamil Nadu Chennai India Department of Computer Science and Engineering Graphic Era Deemed to be University Uttarakhand Dehradun India Division of Research and Development Lovely Professional University Punjab Phagwara India Division of Research and Innovation Uttaranchal University Uttarakhand Dehradun India Department of Computer Applications Sri Krishna Arts and Science College Tamil Nadu Coimbatore India

ISBN: (纸本)9798350398496

In the IoT era, mobile sensors may be used to supervise inhabitants' health and publish acquired health data to cloud servers for communication, encouraging the creation of e-healthcare record (e-hcr) systems. Mobile cloud computing combines the features and advantages of mobile and cloud computing while overcoming this. However, before it can be extensively utilised, e-hcr systems must solve issues about privacy and efficiency. One of the most important aspects is the confidentiality of e- hcr's. Another challenge with mobile cloud computing is that wearable devices are typically resource constrained. The goal of this article is to discuss networked healthcare can help with application. Furthermore, if e-hcr data access laws are known, the online phase may create the final cypher messages fast. Proposed e-hcr access control framework supports access rules specified systems. © 2023 IEEE.

关键词： Data Analytics

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Cloud Computing Improved Clustering using Intrusion Detection

Cloud Computing Improved Clustering using Intrusion Detectio...

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2023 International Conference in Advances in Power, Signal, and Information Technology, APSIT 2023

作者： Bansal, Saloni Singh, Amandeep Bijlwan, Sheela Goyal, Bhawna Dogra, Ayush Lepcha, Dawa Chyophel GLA University Department of Electronics and Communication Engineering Mathura India Gulzar Group of Institutions Computer Science and Engineering Punjab Ludhiana India Uttaranchal Institute of Technology Uttaranchal University Computing Sciences Dehradun248007 India University Center for Research and Development Chandigarh University Punjab Mohali India Chitkara University Institute of Engineering and Technology Chitkara University Punjab India Chandigarh University Department of Electronics and Communication Engineering Punjab Mohali India

ISBN: (纸本)9798350339369

It is one of the more difficult tasks and calls for highly skilled specialists to deal with the aspects and systems of action of intruders and then unauthorized users who attempt to restrict the data with the computer network. The development of the computer network, the number of intruders, and then the infiltration are all possible effects of incorporating cloud computing. Generally speaking, the updating that comes from properly managed vocations is wasted through its count above the time and it rises before it manifests with the technique. There are many unthinkable state gaps for spotting invasions, and then the numerous communications are what matter most. In order to achieve infiltration through the capture of many capability systems (maximum and less expensive tasking), the usage of advanced detection with the new then the discovering systems is utilized with many than the previous in the cloud. The network warning identification and variation systems based on machine learning, which is a demanding warning identification technology, were separately examined in research and development with research precision. Where there is still room for improvement, the work detailed here elaborates on the forest by choosing its merits and then uses an improved density-based spatial clustering of applications with noise (DBSCAN) through grouping and then the intrusion identification with the cloud environment to remove these flaws. © 2023 IEEE.

关键词： Cloud computing

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Context-Aware based personalized recommendation on mobile for monitoring excessive sugar consumption of thai adolescents

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Journal of Mobile Multimedia 2022年第6期18卷 1879-1912页

作者： Suwan, Rodjana Temdee, Punnarumol Prasad, Ramjee Computer and Communication Engineering for Capacity Building Research Center School of Information Technology Mae Fah Luang University Chiang Rai Thailand CTIF Global Capsule (CGC) Department of Business Development and Technology Aarhus University Denmark

Given the harmful effects of excessive sugar consumption, everyone should be aware of the amount of sugar they consume in daily life. Generally, managing nutrition and preventing negative effects from sugar intake requires a nutritional specialist or specialized knowledge, which may not always be readily accessible or understandable. Therefore, there is a need for a mobile application that can track excessive sugar consumption. This study proposes a context-Aware personalized recommendation mobile application for monitoring excessive sugar consumption and providing individual recommendations (based on a predefined set of 144 rules) to Thai adolescents. The application is user friendly and can be used to provide recommendations to users for sugar consumption and proper exercise each day. Personal, health, and lifestyle data are collected and analyzed to provide individualized recommendations to each user. Experiments were conducted with 140 Thai adolescents aged 15 to 25 years old. Users preferences regarding degree of awareness in presentation style were also investigated. Users rated their satisfaction with the proposed mobile application as very high in terms of both function and personalization. In addition, fully automated recommendations were found to be the preferred degree of awareness among the test group. © 2022 River Publishers. All rights reserved.

关键词： Context-Awareness Lifestyle Nutrition Personalized recommendations Sugar consumption

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