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A Study on the Evaluation of Cyber Security Vulnerabilities in Medical Software

A Study on the Evaluation of Cyber Security Vulnerabilities ...

引用

2025 International Conference on Electronics, Information, and Communication, ICEIC 2025

作者： Woo, Jeong-Hyun Koo, Jee-Hyun Seo, Byung-Suk Son, Sun-Young Ko, Kwang-Man Sang-Ji University Dept. of Computer Engineering Korea Republic of Sang-Ji University Dept. of Bigdata Science Korea Republic of Sang-Ji University Dept. of Electrical and Electronic Engineering Korea Republic of

ISBN: (纸本)9798331510756

In this paper, we examine the cybersecurity vulnerability assessment method of medical software. Medical software processes patient sensitive data and is linked to various medical devices and systems in real time. Due to these characteristics, medical software is highly likely to be exposed to various cybersecurity threats such as ransomware, data leakage, and medical device hacking. Based on the international standard IEC TS 60601-4-5, we propose threat modeling, vulnerability scanning, and penetration testing as a methodology for assessing the security vulnerabilities of medical software. Through this, we can identify security vulnerabilities in advance and prepare measures to respond quickly. We can prevent security threats and improve the safety of medical software through response strategies such as security patches and updates, network separation, data encryption, and security education. In conclusion, strengthening the security of medical software is essential to maintain patient safety and the reliability of the medical system, and systematic security assessment and continuous response are required. © 2025 IEEE.

关键词： Cyber attacks

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Delay Minimization in UAV-Enabled MEC Networks Using Q-Learning 10

Delay Minimization in UAV-Enabled MEC Networks Using Q-Learn...

引用

10th IEEE World Forum on Internet of Things, WF-IoT 2024

作者： Moiz, Syed Khwaja, Ahmed S. Alnoman, Ali Anpalagan, Alagan Jaseemuddin, Muhammad Toronto Metropolitan University Dept. of Electrical and Computer Engineering Toronto Canada American University of Ras Al Khaimah Dept. of Computer Science and Engineering Ras al Khaimah United Arab Emirates

ISBN: (纸本)9798350373011

Mobile edge computing (MEC) is a promising solution for the ever-increasing computational demands of the ubiquitous Internet of things (IoT) applications. However, efficient resource management algorithms are needed to satisfy the quality-of-service (QoS) requirements of delay-sensitive IoT systems. In this paper, we propose a new Q-learning-based load-balancing resource management (QLLB) algorithm to reduce the computational delay in a MEC system. Unlike existing algorithms, the proposed algorithm optimally assigns tasks to unmanned aerial vehicles (UAVs) considering load balancing, and employs the Q-learning algorithm to achieve efficient CPU usage management at the MEC server level. Results show that the QLLB algorithm exhibits a considerable improvement in the overall performance compared to the baseline and round-robin algorithms. Specifically, under heavy load conditions, we observe a 68% reduction in the total delay and a 75% reduction in the overall algorithm execution time with respect to the baseline algorithm. Furthermore, we observe a 59% improvement in the combined UAV selection and task data transmission delay. © 2024 IEEE.

关键词： Quality of service

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Less is More: Deep Learning Framework for Digital Forensics in Resource-Constrained Environments 11

Less is More: Deep Learning Framework for Digital Forensics ...

引用

11th International Symposium on Digital Forensics and Security, ISDFS 2023

作者： Rath, Suman Das, Tapadhir Astaburuaga, Ignacio Sengupta, Shamik University of Nevada Reno Dept. of Computer Science & Engineering Reno United States University of the Pacific Dept. of Computer Science Stockton United States

ISBN: (纸本)9798350336986

Machine learning (ML)-enabled tools and techniques are an unavoidable necessity for modern digital forensics. However, the performance of such tools often requires extensive training using powerful computing environments that may expend thousands of dollars and several training hours for a single model. Further, this process necessitates using high-volume datasets that are generally unavailable due to court-mandated restrictions. To overcome these problems, this paper proposes using a two-layered strategy that fuses several features of transfer learning and knowledge distillation-based techniques to build lightweight neural networks that can be trained using low-volume datasets to achieve high accuracy. Experimental results highlight we achieved an accuracy of 98.5% and 97.5% during training and testing of the heavier ML model, and an accuracy of 97.08% and 97.2% on the lightweight ML model (during training and testing respectively), therefore demonstrating the robustness and validity of the proposed technique. © 2023 IEEE.

关键词： Digital forensics

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Atlas-Based Automatic Internal Auditory Canal Localization with a Weakly-Supervised 3D U-Net

Atlas-Based Automatic Internal Auditory Canal Localization w...

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Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Mason, Hannah G. Noble, Jack H. Dept. of Computer Science Vanderbilt University United States Dept. of Electrical and Computer Engineering Vanderbilt University United States

ISBN: (纸本)9781510660373

Cochlear implants (CIs) are neural prosthetics which use an array of implanted electrodes to improve hearing in patients with severe-to-profound hearing loss. After implantation, the CI is programmed by audiologists who adjust various parameters to optimize hearing performance for the patient. Without knowing which auditory nerve fibers (ANFs) are being stimulated by each electrode, this process can require dozens of programming sessions and often does not lead to optimal programming. The internal auditory canal (IAC) houses the ANFs as they travel from the implantation site, the cochlea, to the brain. In this paper, we present a method for localizing the IAC in a CT image by deforming an atlas IAC mesh to a CT image using a 3D U-Net. Our results suggest this method is more accurate than an active shape model-based method when tested on a test set of 20 images with ground truth. This IAC segmentation can be used to infer the position of the invisible ANFs to assist with patient-specific CI programming. © 2023 SPIE.

关键词： Cochlear implants

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An Effective Prediction of Rainfall Using Machine Learning Technique 5

An Effective Prediction of Rainfall Using Machine Learning T...

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5th IEEE International Conference on Electrical, computer and Communication Technologies, ICECCT 2023

作者： Rana, Nisha Guru Prasad, M. Sharma, Ashutosh Pratap, M.S. Anand Kumar, M. Aditya Pai, H. Dept. of Computer Science and Engineering Rishikesh India Graphic Era Deemed University Dept. Computer Science and Engineering Dehradun India Rishikesh India School of Computing and Information Technology Reva University Bengaluru India Dept. of Computer Application Dehradun India Dept. of Computer Science and Engg. Dehradun India

ISBN: (纸本)9781665493604

Climatic information such as temperature, humidity, and precipitation are useful for agriculturists, businesses, researchers, and the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. In this study, factual approaches and machine learning methods are used to estimate and forecast meteorological parameters for the government. Estimating precipitation is a major factor that affects the environment and is one of the most important aspects of meteorological science. Approaches are used to forecast and estimate precipitation. Daily observations were used as part of the experiment. Validation of results using real-world data is used to check the accuracy of forecasting model experimentation. The experimental results show that distribution, correlation, line-plots, and neural networks perform well for forecasting meteorological characteristics and have the best classification accuracy when compared to other types of machine learning methods for precipitation forecasting. © 2023 IEEE.

关键词： Machine learning

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Self-supervised Registration and Segmentation of the Ossicles with A Single Ground Truth Label

Self-supervised Registration and Segmentation of the Ossicle...

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Medical Imaging 2023: Image-Guided Procedures, Robotic Interventions, and Modeling

作者： Zhang, Yike Noble, Jack H. Dept. of Computer Science Vanderbilt University United States Dept. of Electrical and Computer Engineering Vanderbilt University United States

ISBN: (纸本)9781510660373

AI-assisted surgeries have drawn the attention of the medical image research community due to their real-world impact on improving surgery success rates. For image-guided surgeries, such as Cochlear Implants (CIs), accurate object segmentation can provide useful information for surgeons before an operation. Recently published image segmentation methods that leverage machine learning usually rely on a large number of manually annotated ground truth labels. However, it is a laborious and time-consuming task to prepare the dataset. This paper presents a novel technique using a self-supervised 3D-UNet that produces a dense deformation field between an atlas and a target image that can be used for atlas-based segmentation of the ossicles. Our results show that our method outperforms traditional image segmentation methods and generates a more accurate boundary around the ossicles based on Dice similarity coefficient and point-to-point error comparisons. The mean Dice coefficient is improved by 8.51% with our proposed method. © 2023 SPIE.

关键词： Image segmentation

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Brain-computer Interfaces: A Key to Neural Communication's Limitless Possibilities 1

Brain-Computer Interfaces: A Key to Neural Communication's L...

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1st International Conference on Cognitive, Green and Ubiquitous Computing, IC-CGU 2024

作者： Saxena, Shilpi Ranjan, Mritunjay Kr. Sattar, Arif Md. School of Computer Science and Engineering Sandip University Nashik India A.M. College Dept. of Computer Science & Information Technology Gaya India

ISBN: (纸本)9798350361780

Brain-computer Interfaces (BCIs) have revolutionized human-machine interaction by establishing a direct communication pathway between the brain and an external machine. These systems rely on neural signals, acquired invasively through implanted electrodes or non-invasively using scalp sensors. Signal processing algorithms extract meaningful information from these signals, such as motor intentions or cognitive states. BCIs have applications in various domains, including healthcare, neurorehabilitation, gaming, virtual reality, and augmented reality. However, BCI development faces challenges such as improving signal acquisition and decoding techniques, enhancing reliability and robustness, and ensuring user safety and privacy. The translation of BCI technology from the lab to real-world applications requires addressing ethical, legal, and societal considerations. Standardized evaluation metrics and protocols are needed to compare and validate different BCI approaches effectively. In conclusion, BCIs have the potential to revolutionize human-machine interaction across various domains, but challenges remain in technology refinement, user acceptance, and ethical considerations. Future research directions include enhancing spatial and temporal resolution of neural signals, exploring novel electrode materials, and investigating long-term implantation methods. © 2024 IEEE.

关键词： Augmented reality

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Facial Emotion Analysis using Deep Learning for Seamless Human-computer Interaction 2

Facial Emotion Analysis using Deep Learning for Seamless Hum...

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2nd IEEE International Conference on Networks, Multimedia and Information Technology, NMITCON 2024

作者： Sangeetha, S. Saini, Poonam Banupriya, R. Srimathi, S. Kiruthiga, S. Aarthi, R. Srm Arts and Science College Department of computer Applications and Technology Kattankulathur Changelpattu603203 India Sir Padampat Singhania University Dept. of Computer Science & Engineering Vil. Bhatewar Teh. Vallabhnagar Rajasthan Udaipur India Pgp College Of Engineering and Technology Dept. of Electrical and Electronics Engineering India Saveetha School of Engineering Simats Department of Bio Technology Tamilnadu Chennai India Vel Tech Rangarajan Dr Sagunthala R & D Institute of Science & Technology School of Computing Department of Computer Science & Engineering Chennai India R.M.D Engineering College Department of Ece India

ISBN: (纸本)9798350372892

HCII, or human-computer intelligent interaction, is a new academic subject that tries to make computer use more intuitive. Some say machines need human-level social abilities before interacting with people. These skills include reading emotions. Due to its exponential expansion, facial emotion recognition is becoming essential to face recognition. According on photographs, machine learning can recognise expressions. This paper discusses expression recognition model training datasets. Deep learning for Facial Emotion Recognition (FER) is used in this research to teach robots to recognise and adapt to human emotions. An extensive review of deep learning models, including CNNs and CNNR, emphasises their ability to extract spatial and temporal data needed for emotion classification. FER pipeline components like data collection, preprocessing, and model architecture are examined in the paper. Transfer learning, pre-trained models, and data augmentation are studied to address low-labeled dataset concerns. Detailed model training, validation, and evaluation metrics emphasise performance assessment. This study uses the enlarged CohnKanade (CK+) dataset from a face emotion recognition experiment. The system averaged 92.81% accuracy. Eight emotions were successfully detected by the algorithm. The proposed method for emotion recognition has proven useful. © 2024 IEEE.

关键词： Contrastive Learning

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An Enhanced Deadlock Detection Methodology Using Tarjan's Algorithm 8

An Enhanced Deadlock Detection Methodology Using Tarjan's Al...

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8th IEEE International Conference on Computational System and Information Technology for Sustainable Solutions, CSITSS 2024

作者： Neeraja, Adhi Begum, Chimakurthy Mounika Reddy, Kamalapuram Vigneswara Koushal, Rebbavarapu Henry Divya, K.V. Amrita School of Computing Amrita Vishwa Vidyapeetham Dept. of Computer Science Engineering Bengaluru India

ISBN: (纸本)9798331505462

The reliability and performance of concurrency are severely affected by deadlocks in concurrent systems. An efficient deadlock detection algorithm is needed to ensure that any deadlock cases can be pre-empted. The paper introduces a novel algorithm based on Tarjan's method for detecting deadlocks. The algorithm identifies strongly connected components in a directed graph and accurately tracks dependencies among processes using a wait-for graph, enabling precise deadlock detection in concurrent systems. It's implementation is described hereby and its performance is evaluated through extensive experiments on various scenarios. Therefore, this re- search has shown how important it is to detect deadlocks in these concurrent systems which improves their stability as well as reliability. The results demonstrates that the proposed deadlock detection algorithm performs its task efficiently, which is crucial for enhancing the consistency and stability of concurrent systems. This work makes a significant contribution to the development of deadlock detection techniques and offers valuable insights into their application. It is aimed at preventing deadlocks in real-world scenarios. © 2024 IEEE.

关键词： Graph algorithms

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Fault Detection in Solar Panel Images using a novel feed-forward based CNN model 15

Fault Detection in Solar Panel Images using a novel feed-for...

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15th International Conference on Computing Communication and Networking Technologies, ICCCNT 2024

作者： Karuppasamy, ArunaDevi Venugopal, Dhanalakshmi Balaji, P.S. Selvakumar, Karthik Ramachandran, Brintha Eliyan, Greeshma Cherian, Anju Mathew Pillai, Indu Govinda Gulf College Dept. of Computing Sciences Muscat Oman Sultan Qaboos University Dept. of Electrical and Computer Engineering Muscat Oman National Institute of Technology Dept. of Mechanical Engineering Rourkela India Pondicherry University Dept of Green Energy Technology Pondicherry India Saradha Gangadharan College Dept of Computer Applications Pondicherry India

ISBN: (纸本)9798350370249

The increasing dependence on renewable energy highlights the critical need for efficient and reliable maintenance strategies for solar panel installations. A key aspect of maintenance is early detection of faults in individual solar panels, which results in reduced energy production and increased maintenance costs. In this paper, we proposed a novel approach for fault detection in solar panel images. The proposed novel Feed-forward based CNN architecture utilizes the ability to automatically extract features from images to accurately identify faults in solar panels. The proposed CNN model trained on a small, annotated dataset of solar panel images, which includes clean and faulty panels. The Experimental results show the superiority of Feed-forward based CNN model in fault-detection compared to traditional CNN methods, achieving higher precision and recall rate in a wide range of fault types. The proposed approach not only significantly reduces the time and resources required for manual inspection, but also enhances the effectiveness of identifying faults within the solar energy systems. In conclusion, this paper introduces a novel Feed-forward based CNN for fault detection in solar panel images, which provides a solution to enhance the efficiency of solar power installation. © 2024 IEEE.

关键词： Convolutional Neural Network Fault Detection Filter Learning Logistic Regression Solar-Panel Images

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