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A Self-Adaptive Access Control Framework Based on User Attributes Among Heterogeneous Internet of Things Platforms 9

A Self-Adaptive Access Control Framework Based on User Attri...

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Joint 9th International Conference on Digital Arts, Media and Technology with 7th ECTI Northern Section Conference on Electrical, Electronics, computer and Telecommunications engineering, ECTI DAMT and NCON 2024

作者： Park, Jihoon Koo, Jahoon Kim, Young-Gab Sejong University Dept. of Computer and Information Security and Convergence Engineering for Intelligent Drone Seoul Korea Republic of Sejong University Dept. of Computer and Information Security Seoul Korea Republic of

ISBN: (纸本)9798350318241

Internet of Things (IoT) platforms are being actively designed to provide various services to users. However, most IoT platforms are developed in independent environments, causing difficulties in their interoperability. Even though heterogeneous IoT platforms employ the same attribute-based access control (ABAC) model to protect their resources, the attribute representation methods used by these platforms differ. Consequently, a semantic interoperability problem arises, wherein users of one platform cannot access the resources from other platforms, even though these users possess valid attributes. Therefore, this paper proposes a framework that can interoperate by understanding different attribute representation methods among heterogeneous IoT platforms that employ the same ABAC model. Our framework uses self-adaptive modules monitor-analyze-plan-execute over a shared knowledge (MAPE-K) to solve semantic interoperability problems arising from differences in attribute representations, even with valid attributes, enabling access to resources between heterogeneous IoT platforms. © 2024 IEEE.

关键词： Internet of things

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Lung Cancer Image Identify and Categorization using Artificial Neural Networks

Lung Cancer Image Identify and Categorization using Artifici...

引用

2024 International Conference on Computational Intelligence for Green and Sustainable Technologies, ICCIGST 2024

作者： Lingappa, Ediga Begum, D Jareena Pachouri, Priya Rashmi, Kuderu Varalakshmi, Thokala Tharuna Kumar, Anoop Mallareddy College of Engineering Maisammaguda Dept of Computer Science and Engineering TS Secunderabad500100 India CMR Institute of Technology Dept. of Artificial Intelligence and Data Science Bengaluru560037 India Malla Reddy University Dept of Cyber Security Hyderabad India

ISBN: (纸本)9798350381887

Lung cancer that is made up of small cells is called small cell lung cancer (SCLC). Smoking tobacco in any form-cigarettes, cigars, or pipes-is by far the most significant risk factor for developing lung cancer, though it is only one of many. Cancer of the lungs is known as lung cancer. When cells in the body multiply uncontrollably, cancer develops. If cancer cells enter the lymphatic system or the bloodstream, they can travel to other organs and spread the disease. Cheap and fast cancer diagnostics could save millions. Latestage cancer treatment is hard. Lung cancer is diagnosed non-invasively. Biopsy requires careful ANN architecture selection. This study proposes a diagnosis ANN model. ANN research is 'hot' in cardiology, cancer, and radiology. Medical research uses ANNs. Lung cancer diagnosis was tested. MR images assess lung health for the neural network. MATLAB created ANNs and processed images. Training data was extracted from MR images using Harlick texture features. MATLAB made ANNs. Elman Network exceeds ANNs with 90.30 %. Lung cancer can develop. MRIs detect lung cancer. MRIs show lung development. Many lung tumour detection research use machine learning and deep learning. MRI algorithms swiftly detect lung malignancies, improving patient therapy. Radiologists use these forecasts. The study evaluates a self-defined ANN for lung tumour identification. The radiologist needs time to find and remove the cancer from the MR. Technology improves performance and segmentation. Machines always predict and detect. Neural networks outperformed SVM, Random forest, regression, and others. © 2024 IEEE.

关键词： Lung cancer

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A Comprehensive Threat Model for Autonomous Vehicle Communication security 6

A Comprehensive Threat Model for Autonomous Vehicle Communic...

引用

6th International Conference on Advancements in Computing, ICAC 2024

作者： Meegammana, Niranjan W. Fernando, Harinda Shilpa Sayura Foundation Cyber Security Research Lab Kandy Sri Lanka Sri Lanka Institute of Information Technology Dept. of Computer Systems Engineering Malabe Sri Lanka

ISBN: (纸本)9798331517878

The rapid advancement of autonomous vehicles (AVs) promises to enhance transportation efficiency, reliability, and safety. However, these systems, particularly multi-channel vehicular communications, are vulnerable to cyber attacks that threaten authenticity, authorization, confidentiality, integrity, availability, and non-repudiation. The 2014 Chrysler Jeep hack highlights the urgent need for robust security measures in vehic-ular communications. This study utilizes the STRIDE, DREAD, and MITRE ATT&CK security frameworks to develop a comprehensive threat model that identifies and assesses critical threats and risks to AV communications. By detailing specific attack vectors and their multidimensional impacts and criticality, the study establishes a baseline of threats for communication security in vehicular networks. It produces visual threat maps, impact graphs of various attacks, and a color-coded threat grid to guide stakeholders in developing effective cyber security measures for protecting AV communications, thereby help stakeholders to improve the reliability and safety of AV navigation for fostering growth in the AV industry. © 2024 IEEE.

关键词： cyber attacks

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A Unified Deep Learning Approach for Integrating Retinal Image Diagnosis and Surgical Tool Classification 1

A Unified Deep Learning Approach for Integrating Retinal Ima...

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1st IEEE International Conference on Cognitive Robotics and Intelligent Systems, ICC - ROBINS 2024

作者： Jesica, J. Anitha, J. Sujitha Juliet, D. Belfin, R.V. Karunya Institute of Technology and Sciences Dept. of Data Science and Cyber Security Coimbatore India Karunya Institute of Technology and Sciences Dept. of Computer Science and Engineering Coimbatore India University of North Carolina Dept. of Neurology Chapel Hill United States

ISBN: (数字)9798350372748

ISBN: (纸本)9798350372748

Early and accurate diagnosis by using retinal image processing is critical for enabling optimized patient care. Existing techniques for the diagnosis in medical image processing often face limitations. This research study leverages the comparative analysis of advanced Convolutional Neural Networks (CNNs), including AlexNet, InceptionV3, Xception, and EfficientNetB3;out of which EfficientNetB3 gives the comparatively higher accuracy of 84%. This research work presents a novel deep learning-based approach for detecting diseases along with the integration of Lenscraft AI. Lenscraft AI helps to segment surgical masks and identify the surgical tools used in surgical recordings using YOLOv8. The developed tool and tracking method offers a higher precision value of 0.8863 and efficiency in identifying the surgical instruments and potentially improving surgical technique for the trainee surgeons and patient safety. © 2024 IEEE.

关键词： Convolutional neural networks

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QaSAL: QoS-aware State-Augmented Learnable Algorithms for Coexistence of 5G NR-U/Wi-Fi 59

QaSAL: QoS-aware State-Augmented Learnable Algorithms for Co...

引用

59th Annual Conference on Information Sciences and Systems, CISS 2025

作者： Fasihi, Mohammad Reza Mark, Brian L. George Mason University Dept. of Electrical and Computer Engineering and Wireless Cyber Center FairfaxVA United States

ISBN: (纸本)9798331513269

With the increasing demand for wireless connectivity, ensuring the efficient coexistence of multiple radio access technologies in shared unlicensed spectrum has become an important issue. This paper focuses on optimizing Medium Access Control (MAC) parameters to enhance the coexistence of 5G New Radio in Unlicensed Spectrum (NR-U) and Wi-Fi networks operating in unlicensed spectrum with multiple priority classes of traffic that may have varying quality-of-service (QoS) requirements. In this context, we tackle the coexistence parameter management problem by introducing a QoS-aware State-Augmented Learnable (QaSAL) framework, designed to improve network performance under various traffic conditions. Our approach augments the state representation with constraint information, enabling dynamic policy adjustments to enforce QoS requirements effectively. Simulation results validate the effectiveness of QaSAL in managing NR-U and Wi-Fi coexistence, demonstrating improved channel access fairness while satisfying a latency constraint for high-priority traffic. © 2025 IEEE.

关键词： Wireless local area networks (WLAN)

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Experimental Approach to Internal security Threats for 5G-Advanced Core Networks 35

Experimental Approach to Internal Security Threats for 5G-Ad...

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35th IEEE International Symposium on Personal, Indoor and Mobile Radio Communications, PIMRC 2024

作者： Park, Jaehyoung Kim, Jihye Woo, Seungchan Park, Kyungmin Kim, Jonghyun Lee, Jong-Hyouk Sejong University Dept. of Computer and Information Security & Convergence Engineering for Intelligent Drone Seoul Korea Republic of Sejong University Dept. of Computer and Information Security Seoul Korea Republic of Sejong University Dept. of Computer and Information Security & Convergence Engineering for Metaverse Seoul Korea Republic of Electronics and Telecommunications Research Institute Daejeon Korea Republic of

ISBN: (纸本)9798350362244

As we embrace the dawn of the 5G era, the fusion of Software Defined Network (SDN) and Network Function Virtualization (NFV) technologies has heralded a new epoch in network architecture. This provides indispensable capabilities and virtualized infrastructure to meet the burgeoning demands of modern networks. Yet, with each stride forward in mobile communication technology, security threats loom larger, casting a shadow over the very fabric of our networks. In this context, identifying and addressing these threats is crucial as it plays a key role in enhancing the security of a 5G-Advanced and a next 6G mobile communication technologies. Therefore, this paper undertakes an investigative journey to explore security threats introduced by SDN/NFV technologies in the 5G-Advanced core networks. Through our simulations, we unveil a need for fortified security technologies within the 5G-Advanced core networks, illuminating a path towards the creation of bespoke datasets tailored for the trends of 5G-Advanced security research. © 2024 IEEE.

关键词： 5G mobile communication systems

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Designing of Creator's Information Hub using Machine Learning 2

Designing of Creator's Information Hub using Machine Learnin...

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2nd DMIHER International Conference on Artificial Intelligence in Healthcare, Education and Industry, IDICAIEI 2024

作者： Gabhane, Diksha Gawande, Ujwalla Gaikwad, Sanjana Gajarlewar, Swati Bhawalkar, Rita Yeshwantrao Chavan College of Engineering Dept. of Computer Science & Design Nagpur India Yeshwantrao Chavan College of Engineering Dept. of Information Technology Nagpur India NCPharmacy Wanadongri Nagpur India G H Raisoni College of Engineering and Management Dept. of Cyber Security and Data Science Pune India

ISBN: (纸本)9798331528713

The proliferation of educational content on platforms like YouTube has transformed digital learning, yet it presents challenges in effective creation, dissemination, and impact assessment. This paper proposes a strategic initiative to collaborate with 6,000 active educational YouTubers to enhance global educational outreach. Central to this initiative is the compilation and analysis of a comprehensive dataset containing vital information about content creators and their audiences. Leveraging advanced machine learning algorithms, the analysis aims to identify collaboration opportunities, optimize engagement strategies, enhance content relevance and impact, facilitate knowledge sharing, and measure outcomes. The problem statement highlights fragmentation in educational content, lack of data-driven insights, and suboptimal collaboration opportunities. Addressing these challenges requires a structured approach to connect creators, leverage data analytics, and establish a collaborative ecosystem for enriching educational experiences worldwide. © 2024 IEEE.

关键词： Adversarial machine learning

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IoT-driven automatic vehicle accident rescue system implemented with Proteus 1

IoT-driven automatic vehicle accident rescue system implemen...

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1st International Conference on Optimization Techniques for Learning, ICOTL 2023

作者： Hemalatha, K. Ganga Devi, S.V.S. Deepika, V. Madanapalle Institute of Technology and Science Dept. of Computer Science & Engineering-Artificial Intelligence Andhra-Pradesh Madanapalle India Madanapalle Institute of Technology and Science Dept. of Computer Science & Engineering-Cyber Security Andhra-Pradesh Madanapalle India Sri Venkateswara College of Engineering Dept. of Computer Science & Engineering Andhra-Pradesh India

ISBN: (纸本)9798350328042

IoT-driven automatic vehicle accident rescue system implemented with Proteus is an inventive solution aimed at enhancing emergency response and rescue operations following vehicle accidents. Given the rising vehicle usage alongside population growth, the frequency of accidents has also surged over time, leading to unfortunate loss of life due to delays in ambulance arrival at accident scenes. Swift transportation of accident victims to medical facilities is of paramount importance. Upon occurrence of an accident, the proposed "IoT-driven automatic vehicle accident rescue system implemented with Proteus"immediately transmits accident details to the ambulance service, investigation unit, and designated emergency contacts. Simultaneously, the accident's precise location is shared via a Google Maps link to the users' smart devices equipped with mobile network connectivity. This facilitates efficient location identification for both ambulance services and police departments. Comprising elements such as accelerometers, GPS (Global Positioning System) modules, GSM (Global System for Mobile Communication) modules, motors, buzzers, LEDs, and more, the system seamlessly integrates these components with a central microcontroller unit using Proteus software. The core objective of this initiative revolves around two key goals: rescuing drivers involved in accidents and expediting victim recovery during the critical early stages of mishaps. © 2023 IEEE.

关键词： Global system for mobile communications

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Slotted ALOHA with Code Combining for IoT Networks 3

Slotted ALOHA with Code Combining for IoT Networks

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3rd IEEE International Mediterranean Conference on Communications and Networking, MeditCom 2023

作者： Tyrovolas, Dimitrios Chrysologou, Athanasios Chondrogiannis, Georgios Tegos, Sotiris Mekikis, Prodromos-Vasileios Diamantoulakis, Panagiotis Ioannidis, Sotiris Liaskos, Christos Chatzidiamantis, Nestor Karagiannidis, George Aristotle University of Thessaloniki Dept. of Electrical and Computer Engineering Thessaloniki Greece Technical University of Chania Dept. of Electrical and Computer Engineering Chania Greece University of Ioannina Computer Science Engineering Department Ioannina Greece Cyber Security Systems and Applied Ai Research Center Lebanon

ISBN: (纸本)9798350333732

Internet of Things (IoT) networks are expected to be a key enabler technology for smart cities, due to their ability to offer real-time monitoring. In this work, we propose a novel medium access control (MAC) protocol, inspired by the synergy of slotted ALOHA and hybrid automatic repeat request with code combining (HARQ-CC), termed as code combining slotted ALOHA (CCSA), to improve the performance of IoT networks with multiple randomly deployed sensors. Specifically, we provide analytical closed-form expressions for the network's outage probability as well as for its average throughput when CCSA is applied. Finally, we provide simulation results that reveal the authenticity of the derived expressions and reveal the capabilities of HARQ-CC to significantly improve the network's performance when the sensors' access is based on a random access scheme. © 2023 IEEE.

关键词： Wireless sensor networks

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Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep Reinforcement Learning 100

Traffic Priority-Aware 5G NR-U/Wi-Fi Coexistence with Deep R...

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100th IEEE Vehicular Technology Conference, VTC 2024-Fall

作者： Fasihi, Mohammad Reza Mark, Brian L. George Mason University Dept. of Electrical and Computer Engineering Wireless Cyber Center FairfaxVA United States

ISBN: (纸本)9798331517786

Coexistence of 5G new radio unlicensed (NR-U) and Wi-Fi is highly prone to the collisions among NR-U gNBs (5G base stations) and Wi-Fi APs (access points). To improve performance and fairness for both networks, various collision resolution mechanisms have been proposed to replace the simple listen-before-talk (LBT) scheme used in the current 5G standard. We address two gaps in the literature: first, the lack of a comprehensive performance comparison among the proposed collision resolution mechanisms and second, the impact of multiple traffic priority classes. Through extensive simulations, we compare the performance of several recently proposed collision resolution mechanisms for NR-U/Wi-Fi coexistence. We extend one of these mechanisms to handle multiple traffic priorities. We then develop a traffic-aware multi-objective deep reinforcement learning algorithm for the scenario of coexistence of high-priority traffic gNB user equipment (UE) with multiple lower-priority traffic UEs and Wi-Fi stations. The objective is to ensure low latency for high-priority gNB traffic while increasing the airtime fairness among the NR-U and Wi-Fi networks. Our simulation results show that the proposed algorithm lowers the channel access delay of high-priority traffic while improving the fairness among both networks. © 2024 IEEE.

关键词： Wireless local area networks (WLAN)

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