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Enhancing Security in the Internet of Things: A Trust-Based Protocol for Resilient Communication

SN computer science 2024年第1期5卷 1-8页

作者： Althaf Ali, A. Hussain, Mohammed Muzaffar Subramaneswara Rao, A. Lavanya, S. Feroz Khan, A.B. Department of Computer Applications Madanapalle Institute of Technology & Science (MITS) Andhra Pradesh Madanapalle India Department of Computer Science and Engineering C. Abdul Hakeem College of Engineering and Technology Tamil Nadu Melvisharam India Department of Software Applications Thiruthangal Nadar College Tamil Nadu Chennai India Department of Computer Science Syed Hameedha Arts and Science College Tamil Nadu Kilakarai India

The quick increasing in the Internet of Things (IoT) devices has raised significant security concerns, particularly in the face of reactive jamming attacks. This paper proposes a trust-based protocol named Trust-Based Protocol for Resilient Communication (TBRC) that incorporates a robust key management technique to improve the security of IoT systems and mitigate the impact of reactive jamming attacks. To assess the effectiveness of our protocol, the work is compared with existing schemes such as CKMT, EDDK, and CEMT. Through extensive simulations under realistic conditions, we analyze key performance metrics, including energy consumption, packet loss, computational time, and throughput. The comparative analysis demonstrates that our proposed protocol outperforms CKMT, EDDK, and CEMT in terms of computational efficiency, throughput, and delay, making it a more effective solution for secure IoT communication. By leveraging a robust key management technique, our protocol provides a secure and resilient communication framework for IoT environments, effectively countering reactive jamming attacks. The integration of this technique strengthens the overall security posture of IoT networks, ensuring reliable and secure communication between devices. This work contributes to advancing IoT security by introducing a trust-based protocol with a robust key management technique that effectively mitigates the impact of reactive jamming attacks. The comparative evaluation against existing schemes validates the superiority of our proposed approach in terms of security, efficiency, and reliability. The results of our evaluation highlight the improved performance of our protocol, positioning it as a promising solution for real-world IoT deployments. © 2023, The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd.

关键词： DDoS IoT Security Trusted metrics

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Dynamic generative R-CNN

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Neural Computing and Applications 2025年第10期37卷 7107-7120页

作者： Saffarini, Rasha Khamayseh, Faisal Awwad, Yousef Sabha, Muath Eleyan, Derar IT Engineering Arab American University Jenin Palestine Department of Computer Science Palestine Polytechnic University Hebron Palestine Department of Computer System Engineering Palestine Technical University Tulkarm Palestine Department of Multimedia Technology Arab American University Jenin Palestine Department of Applied Computing Palestine Technical University Tulkarm Palestine

Different techniques have been developed for object detection and recognition. These techniques can be divided into single-shot and two-shot methods. Single-shot methods focus on real-time applications, while two-shot methods are used in applications requiring higher accuracy. However, different versions of the two-shot techniques produce limited results in terms of accuracy and speed, or both. Therefore, this study proposes a novel model called dynamic generative R-CNN (DGR-CNN) that reduces the number of proposed regions using a dynamic programming model that applies the graph similarity method over graph-based image segmentation. Additionally, the proposed model employs DCGAN technique to improve detection performance. DGR-CNN reduces the overall detection and classification time and enhances the detection accuracy. The PASCAL VOC2007 and MS COCO datasets were utilized to evaluate the model. The results showed that DGR-CNN significantly reduces the number of candidate regions compared to the selective search algorithm employed in R-CNN and fast R-CNN. Although fast R-CNN utilizes 2000 regions and faster R-CNN utilizes 300 regions, DGR-CNN reduces the number of regions to approximately 130. The mean average precision of the proposed method was 75.1% on the PASCAL VOC2007, while fast and faster R-CNN scored 66.9% and 69.9%, respectively. Moreover, the DGR-CNN model significantly improved the classification accuracy when tested on the MS COCO dataset, achieving an MAP of 68.76%, compared with 32.64% and 42.3% for fast and faster R-CNN. This increase in accuracy was achieved without significantly compromising the speed compared with faster R-CNN. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2025.

关键词： Image segmentation

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AntiMicrobial Resistance Prediction Using Advanced Image Encoding Techniques 1

AntiMicrobial Resistance Prediction Using Advanced Image Enc...

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1st International Conference on Sustainable Energy Technologies and Computational Intelligence, SETCOM 2025

作者： Ghadekar, Premanand Jaggi, Arjun Swain, Debabrata Ghadekar, Aryan Vishwakarma Institute of Technology Department of Information Technology Pune India HCLTech CA United States Pandit Deendayal Energy University Department of Computer Science and Engineering Gandhinagar India Vishwakarma Institute of Technology Department of Computer Engineering Pune India

ISBN: (纸本)9798331520540

Antimicrobial resistance (AMR) is one of the significant problems in the health sector and requires resources to create and develop accurate forecasting systems capable of recognizing resistant microbes and their treatment approaches. The traditional machine learning models used in AMR prediction usually suffer from poor genomic sequences feature extraction and finding it hard to solve big biological problems with big data. K-mer based methods or other simpler classifiers have been able to provide certain accuracy, but these have strived when dealing with many genomic strains and variants. To address these shortcomings, deep learning models are able to learn data representation at various abstraction levels which understanding complex data of fundamental and high dimensional patterns. In this work we provide a new deep learning approach exploiting CNNs on FCGR images of microbial genomes. The sequence is represented as frequency chaos game representation making it better for the enn structural application as resnet and densenet where features are deeply embedded in classifiers. When it comes to string and image based methods it is clear that they both have their own benefits and shortcomings. String based analysis is a faster and more accurate way to analyze naked sequence data conversion, but it may not be as informative as visual presentation. Our study verified the hypothesis that it is feasible to encode genomic data for further FCGR, so that the data might be utilized as an input for machine learning and deep learning methods. The model is effective with substantial improvements with the accuracy of over 90% in test dataset. This represents a significant improvement in accuracy score with better systems for predicting antibiotic resistance among many types of microbes. © 2025 IEEE.

关键词： AntiMicrobial Resistance Prediction CARD Frequency Chaos Game Representation microbes NCBI

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Pulmonary Nodule Detection in Computed Tomography Images Using Deep Learning 1

Pulmonary Nodule Detection in Computed Tomography Images Usi...

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1st IEEE International Conference on Advances in Electronics, Communication, Computing and Intelligent Information Systems, ICAECIS 2023

作者： Longkumer, Akumlong Walling, Supongmen Kalita, Shobhanjana Icfai University Department of Computer Science Dimapur India National Institute of Technology Department of Computer Science and Engineering Dimapur India Tezpur University Department of Computer Science and Engineering Tezpur India

ISBN: (纸本)9798350348057

the well-being of individuals is seriously threatened by lung cancer, which has one of the highest fatality rates of all cancers. Therefore, to increase patient life expectancies, early diagnosis of lung nodules is essential. For many years, the science of machine vision and medical diagnostics has used convolution neural networks. Early diagnosis can significantly lower the patient's mortality, and computer-aided diagnosis has emerged as a vital tool for medical professionals. computer-aided diagnosis depends on the ability to identify pulmonary nodules in computed tomography images. In the Computed Tomography scans, it is difficult to find the lump inside the lungs. This project uses U-Net to implement automated lung nodule identification. Furthermore, the data sets used for pulmonary nodule recognition from CT scan images are from the freely accessible LIDC/IDRI database. © 2023 IEEE.

关键词： computer aided diagnosis

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Cyber-physical energy system simulation tool: framework for network co-simulation, GridLAB-D and ns-3

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International Journal of Simulation and Process Modelling 2022年第4期18卷 314-328页

作者： Singh, Brijendra Pratap Gore, Manoj Madhava School of Computer Science Engineering and Technology Bennett University Greater Noida India Department of Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad India

A cyber-physical energy system consists of information and communication technology, advanced power electronic devices, energy sources, and smart appliances. A simulator is required for research and to further enhancements in the cyber-physical energy system. The integration of a power system and information and communication technology raises the problem of integration of time-driven activity and event-driven activity. The power system simulators are time-driven, while the network communication simulators are event-driven. The Pacific Northwest National Laboratory, Richland, Washington developed a tool named ‘framework for network co-simulation (FNCS)’ which co-simulates ns-3 (a network simulator) and GridLAB-D (a power distribution system simulator). This work details the enhancement of the FNCS application program (which is integrated into ns-3) to incorporate the functionalities, namely: 1) internal real-time price calculation;2) day-ahead charging schedule of a battery. The enhancement has been used for the development of cyber-physical application programs, namely, internal real-time pricing and battery charging schedules. Copyright © 2022 Inderscience Enterprises Ltd.

关键词： Application programs

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Brain Tumor Detection Using Xception Model

Brain Tumor Detection Using Xception Model

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2024 International Conference on Advances in Computing Research on science engineering and technology, ACROSET 2024

作者： Philip, Joshwa Joy Rani, Ruchi Kumar, Sumit School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Science and Engineering Pune India School of Computer Engineering and Technology Dr Vishwanath Karad Mit World Peace University Department of Computer Engineering and Technology Pune India Department of Electronics and Telecommunication Maharashtra Pune India

ISBN: (纸本)9798350388800

Numerous morphological and histological characteristics of brain tumors make diagnosis and treatment very challenging. Traditional diagnosis methods often depend on the analysis of medical images, which can lead to unclear conclusions and errors. In recent years, machine learning (ML) and deep learning techniques (DL) have shown great scope in improving the detection and classification of brain tumors. These methods extract the features and classify medical imaging data, such as MRI, CT, and PET scans. Convolutional and recurrent neural networks give better performance even though there are challenges in the dataset. In the proposed work, Xception architecture is used which is a type of convolution neural network. This model is pretrained on the ImageNet dataset. The proposed model achieves 99.90% accuracy in brain tumor detection. © 2024 IEEE.

关键词： Convolutional neural networks

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Advances in AI-based genomic data analysis for cancer survival prediction

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Multimedia Tools and Applications 2025年第14期84卷 14139-14166页

作者： Deepali Goel, Neelam Padmavati Khandnor University Institute of Engineering and Technology Panjab University Chandigarh160014 India Chandigarh160012 India Department of Computer Science Guru Nanak College Budhlada151502 India

Problem: Cancer is one of the deadliest diseases prevalent in the world. Survivability, early diagnosis, and accurate prognosis are of utmost importance for the therapeutics and clinical management of cancer patients. To achieve accurate and timely prediction of the survival of cancer patients, several machine-learning models based on genomic data have been proposed but a comprehensive review of recent applications in cancer survival prediction is lacking. This paper represents a review of the most recent application of machine learning and deep learning on cancer survival prediction, with a particular focus on the use of genomic data. It specifically targets the most prominent cancer types such as breast cancer, glioblastoma, lung cancer, renal cell cancer, and oral cancer. Methods: A systematic review approach is employed to analyze recent studies on machine learning techniques applied to cancer survival prediction. Emphasis is placed on methodologies utilizing genomic data due to its effectiveness in predicting survival outcomes. Results: This review highlights the efficacy of different machine/deep learning-based techniques in predicting survival outcomes for different cancer types with genomic data. It also provides a summary of the contributions made by different research groups, critically examines the associated challenges, and suggests potential areas for further investigation. Conclusion: Machine learning and deep learning techniques, especially those utilizing genomic data, hold significant promise for accurate cancer survival prediction across diverse cancer types. Despite advancements, challenges such as data heterogeneity and model interpretability still persist. Further research is warranted to address these challenges and develop a comprehensive framework for cancer survival prediction applicable to various cancer types. This review lays the foundation for future investigations in the area of cancer research. © The Author(s), under exclusive licence

关键词： Diseases

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Parkinson's Disease Detection using Deep Learning 15

Parkinson's Disease Detection using Deep Learning

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15th International Conference on Advances in Computing, Control, and Telecommunication Technologies, ACT 2024

作者： Anandaraj, B. Monisha, P. Krishna Vinaya, M. Laya, V. Mamatha, V. Department of Computer Science and Engineering Madanapalle Institute of Technology and Science Annamayya-Dt Andhra Pradesh Madanapalle India

ISBN: (纸本)9798331300579

Parkinson's disease is a common neurological condition that predominantly im-pacts people who are older than fifty, leading to speech impairments and movement diffi-culties. Timely diagnosis of PD is essential for efficient care and improved patient outcomes, especially considering the global aging population. Our paper introduces a new method for the early identification of Parkinson's disease utilizing machine learning techniques and the Xception architecture. Our focus lies on analyzing spiral and wave drawings, commonly used in clinical diagnosis. We curated a dataset comprising such drawings from individuals with and without PD and processed the data for model training. Leveraging the Xception architecture, our machine learning models achieved promising results, with a training accu-racy of 95.34% for spiral drawings and 93.34% for wave drawings, along with validation accuracies of 93.00% and 86.00%, respectively. Our highlight the capabilities of machine learning methodologies and the Xception architecture in improving the precision and accu-racy of the diagnosis of Parkinson's disease. Implementing our proposed approach could significantly advance early detection efforts, thereby enhancing patient care and quality of life. © Grenze Scientific Society, 2024.

关键词： Neurodegenerative diseases

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Human intelligence-based metaverse for co-learning of students and smart machines

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Journal of Ambient Intelligence and Humanized Computing 2023年第6期14卷 7695-7718页

作者： Lee, Chang-Shing Wang, Mei-Hui Reformat, Marek Huang, Sheng-Hui Department of Computer Science and Information Engineering National University of Tainan Tainan Taiwan Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Information Technology Institute University of Social Sciences Warsaw Poland

This paper proposes a Human Intelligence (HI)-based Computational Intelligence (CI) and Artificial Intelligence (AI) Fuzzy Markup Language (CI&AI-FML) Metaverse as an educational environment for co-learning of students and machines. The HI-based CI&AI-FML Metaverse is based on the spirit of the Heart Sutra that equips the environment with teaching principles and cognitive intelligence of ancient words of wisdom. There are four stages of the Metaverse: preparation and collection of learning data, data preprocessing, data analysis, and data evaluation. During the data preparation stage, the domain experts construct a learning dictionary with fuzzy concept sets describing different terms and concepts related to the course domains. Then, the students and teachers use the developed CI&AI-FML learning tools to interact with machines and learn together. Once the teachers prepare relevant material, students provide their inputs/texts representing their levels of understanding of the learned concepts. A Natural Language Processing (NLP) tool, Chinese Knowledge Information Processing (CKIP), is used to process data/text generated by students. A focus is put on speech tagging, word sense disambiguation, and named entity recognition. Following that, the quantitative and qualitative data analysis is performed. Finally, the students’ learning progress, measured using progress metrics, is evaluated and analyzed. The experimental results reveal that the proposed HI-based CI&AI-FML Metaverse can foster students' motivation to learn and improve their performance. It has been shown in the case of young students studying Software engineering and learning English. © 2023, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Students

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VitaFALL: Advanced Multi-Threshold Based Reliable Fall Detection System

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Recent Advances in computer science and Communications 2022年第1期15卷 32-39页

作者： Patel, Warish D. Patel, Chirag Patel, Monal Department of Computer Science Engineering and Technology Parul University Vadodara India Department of Computer Science BITS Edu Campus Vadodara India

Background: The biggest challenge in our technologically advanced society is the healthy being of aging individuals and differently-abled people in our society. The leading cause for signifi-cant injuries and early death in senior citizens and differently-abled people is due to falling off. The possibility to automatically detect falls has increased demand for such devices, and the high detection rate is achieved using the wearable sensors, this technology has a quite social and monetary impact on society. Therefore, even for the daily activity in the life of aged people, an automatic fall detecting system and vital signs examining system have become a necessity. Objectives: This research work aims at helping aged people and every other necessary human by monitoring their vital signs and fall prediction. A fall detection VitaFALL (Vital Signs and Fall Monitoring) device could analyze the measurement in all three orthogonal directions using a triple-axis accelerometer, and Vital Signs Parameters (Heartrate, Heartbeat, and Temperature monitoring) for the aged and differently-abled people Methods: In comparison with present algorithms, there are various benefits regarding privacy, suc-cess rate, and design of devices upgraded using an implemented algorithm over the ubiquitous algo-rithm. Results: As concluded from the experimental outcomes, the accuracy achieved is up to 94%. ADXL335 is a 3-Axial Accelerometer Module that collects the accelerations of aged people from a VitaFALL device. A guardian can be notified by sending a text message via GSM and GPRS mod-ules so that the aged people can be helped. Conclusion: However, a delay in the time can be noticed while comparing the gradient and minimum value to predetermine the state of the older person. The results of the experiment show the ade-quacy of the proposed approach. © 2022 Bentham science Publishers.

关键词： Fall detection

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