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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 4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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Development of a Novel Quality of Service Aware Video Streaming on Demand System for the Internet of Thing Enabled MANET

Development of a Novel Quality of Service Aware Video Stream...

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2024 International Conference on Communication, computer Sciences and engineering, IC3SE 2024

作者： Agrawal, Navin Kumar Kumar, Sandeep Bisht, Girish Singh Srivastav, Akanksha Bansla, Vandna Jain, Arpit Gla University Department Of Computer Engineering And Applications U.P. Mathura India Tula's Institute Department Of Computer Science And Engineering Uttarakhand Dehradun India Shivalik Engineering College Department Of Computer Science And Engineering Uttarakhand Dehradun India Koneru Lakshmaiah Education Foundation Department Of Computer Science And Engineering AP Vaddeswaram India

ISBN: (纸本)9798350366846

A mobile ad hoc network (MANET) uses mobile devices and wireless networking to stream video. Several methods have been developed to address the challenges associated with video streaming over MANETs. This paper designed a new protocol for Mobile Video of Demand (MVOD) system architecture over the MANET environment. The proposed MVOD Model is used to improve the QoS parameters of the video system in wireless ad-hoc networks. The MVOD protocol uses client caching algorithms and a staggered broadcasting method, and it compares its performance with that of an existing traditional protocol for three-tier architecture. When compared to QoS parameters such as service delay and bandwidth utilization, the proposed MVOD model performs better than the traditional approach when compared with various scenarios. Based on the importance and complexity of evaluating the techniques, we conclude that comparability problems exist in research. © 2024 IEEE.

关键词： Quality of service

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Unlocking the Potential of Unlabeled Data: Semi-Supervised Learning for Stratification of Hyperspectral Images 21

Unlocking the Potential of Unlabeled Data: Semi-Supervised L...

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21st OITS International Conference on Information Technology, OCIT 2023

作者： Ranjan, Pallavi Kumar, Rajeev Girdhar, Ashish Delhi Technological University Computer Science and Engineering New Delhi India Kurukshetra University Computer Science and Applications Haryana India

ISBN: (纸本)9798350358230

Hyperspectral imaging, a critical technology for spectral data acquisition, plays a pivotal role in various fields, including agriculture, medicine, defense, remote sensing and environmental monitoring. Nonetheless, an enduring obstacle in the domain of hyperspectral image analysis revolves around the restricted accessibility of annotated data, posing an impediment to the creation of precise classification models. In response to this challenge, we present an innovative semi-supervised framework that harmoniously combines unsupervised feature learning with the employment of graph-based convolutional neural networks (GCNs). Our approach harnesses the latent knowledge hidden within vast pools of unlabeled hyperspectral data using autoencoders, which extract meaningful features. These features are then incorporated into a GCN-based architecture, leveraging spatial relationships among neighboring pixels. The fusion of unsupervised autoencoder based learning and graph-based techniques enables our model to achieve remarkable classification accuracy, even in scenarios with minimal labeled samples. Through extensive experimentation, we demonstrate the superior performance and robustness of our methodology across a spectrum of hyperspectral imaging datasets. This work is a significant step in the realm of semi-supervised hyperspectral image analysis, unlocking the potential of unlabeled data and empowering accurate classification in data-scarce environments. © 2023 IEEE.

关键词： Image classification

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Comprehensive Survey of Event Extraction Methods in Natural Language Processing 5

Comprehensive Survey of Event Extraction Methods in Natural ...

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5th International Conference on Sustainable Communication Networks and Application, ICSCNA 2024

作者： Kulkarni, Amit Dogra, Varun School of Computer Applications Lovely Professional University Punjab Phagwara144411 India School of Computer Science and Engineering Lovely Professional University Punjab Phagwara144411 India

ISBN: (纸本)9798331530013

Event Extraction (Event Extraction) plays an essential role in NLP (Natural Language Processing) by automating the identification and extraction of events from unstructured textual data. This paper provides a comprehensive overview of event extraction, highlighting its significance in various domains such as security, law, and financial analysis. The paper categorizes event extraction into closed-domain and open-domain approaches. It also talks about various techniques like argument identification, event trigger identification, as well as event type classification. It explores methodologies including machine learning, pattern matching, & deep learning, emphasizing their application in different domains. Evaluation metrics to evaluate the effectiveness of EE systems are explored, including precision, recall, as well as the F1 score. Through a systematic review of the literature, the paper underscores the advancements, challenges, and future directions in EE, positioning it as a critical component in extracting actionable knowledge from textualsources. © 2024 IEEE.

关键词： Deep learning

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AI-Driven Security Enhancements in Wireless Sensor Networks 2

AI-Driven Security Enhancements in Wireless Sensor Networks

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2nd IEEE International Conference on Contemporary Computing and Communications, InC4 2024

作者： Singh, Kamred Udham Kumar, Ankit Varshney, Neeraj Raja, Linesh Singh, Teekam Dewangan, Amit Kumar School of Computing Graphic Era Hill University Dehradun India Guru Ghasidas Vishwavidyalaya Department of Information Technology Bilaspur India GLA University Department of Computer Engineering & Applications UP Mathura India Manipal University Jaipur Department of Computer Applications Jaipur India Graphic Era Deemed to be University Department of Computer Science and Engineering Dehradun India

ISBN: (纸本)9798350383652

Wireless sensor networks have garnered significant adoption as a technology for collecting and analyzing data from the environment. Their versatility and wide-ranging applications, encompassing critical military operations, forest fire detection, healthcare systems, and civilian uses, have made them a focal point of extensive research. The potential of WSNs to provide valuable insights and address various real-world challenges has spurred significant interest and exploration in the scientific community. Nevertheless, Wireless Sensor Networks (WSNs) are susceptible to security threats, especially in unattended environments. To address these threats, the popular Low-Energy Adaptive Clustering Hierarchy protocol is deployed with machine learning techniques. This paper makes an analysis of different machine learning algorithms with multiple class of parameters and evaluates it with criterion as such favorable for a WSN. The Random Forest technique, which allows for the classification of assaults such as blackhole attacks, grayhole attacks, TDMA attacks, and flooding attacks, is the most suitable algorithm for intrusion detection. It comprises six extracted characteristics. The accuracy score is 99.12% with prediction accuracy for attacks: 87%, 89%, 92% and 93% respectively. Unlike detecting the anomaly alone, this mechanism when given to a centralized management system become a smart governance model. This prevents the network from different intrusion methodologies and enhance the characteristics such as energy efficiency, network lifetime, throughput, performance and security issue of the wireless sensor network. © 2024 IEEE.

关键词： Random forests

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Knowledge Graph Completion Method Based on Using Multi-Hop Reasoning 10

Knowledge Graph Completion Method Based on Using Multi-Hop R...

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10th International Conference on Information Technology and Nanotechnology, ITNT 2024

作者： Golovin, Aleksei Zhukova, Nataly Kulikov, Igor Saint Petersburg Electrotechnical University 'LETI' Department Of Software Engineering And Computer Applications Saint-Petersburg Russia Saint-Petersburg Russia

ISBN: (纸本)9798350372939

In this paper, the method for knowledge graph completion based on using multi-hop reasoning is proposed. The relevance of the problem is due to the widespread use of large sparse knowledge graphs with incomplete data. Within the research, the method for restoring the structure of a knowledge graph using sequence-to-sequence multi-hop reasoning, based on the transformer architecture, is proposed. To evaluate the effectiveness of the proposed method, the study of its properties was carried out and the results were compared with the results of the existing methods used for predicting relations in the knowledge graphs. The proposed solution almost achieves the same results as methods based on embeddings. The evidential paths generated by the proposed method ensure the interpretability of the results. Our method does not rely on existing relations to create an evidential path and has the flexibility to restore missing relations in the path. © 2024 IEEE.

关键词： Knowledge graph

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Analysis of Skin Cancer Classification using Deep Learning Techniques 1

Analysis of Skin Cancer Classification using Deep Learning T...

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1st International Conference on Innovative engineering Sciences and Technological Research, ICIESTR 2024

作者： Muthuselvi, S. Sumathi, R. Kalasalingam Academy of Research and Education Department of Computer Applications Tamilnadu Krishnankoil India Kalasalingam Academy of Research and Education Department of Computer Science and Engineering Tamilnadu Krishnankoil India

ISBN: (纸本)9798350348637

Skin cancer is defined as abnormal development and proliferation of skin cells. It is in various steps. Malignant skin cancer is classified into different kinds: carcinoma of the basal cell, carcinoma of the squamous, and melanoma or melanocytic carcinoma. Each of these is called by its name because it occurs in specific cells. Cancer tumors can be observed because skin cancer occurs in the upper layer of the skin, the epidermis. Unlike other cancers, these can be detected and treated at an early stage, which is why the death rate from this type of cancer is relatively low. The primary cause of skin cancer is prolonged exposure to ultraviolet rays emitted by radiation. Melanoma along with additional skin cancers are more prevalent in the general population than lung, colon, breast, and prostate tumors. Melanoma is the deadliest form of malignancy of the skin and has a high death rate compared to other skin cancers, although melanoma is less common in the general population. Among the most common skin cancers are non-melanoma skin cancers. Some people have the possibility of developing melanoma later in life because of a large birthmark (large mole) that appears at birth or later due to melanocytes. Therefore, evidence-based automatic identification of cancer of the skin is useful for improving pathologists' precision and competency in their initial phase. In this work, we suggest a model developed by RCNN deep learning for accurately distinguishing between malignant and benign lesions in the skin. First, we implement a filter or kernel programming to remove noise and artifacts;then, we normalize the source pictures and determine characteristics assisting in precise classification. Furthermore, data enrichment builds up the overall count of images, which gets a better classifying rate its precision. The performance of our proposed RCNN model is comparable to other transfer learning approaches like the algorithm AlexNet, ResNet, The VGG model 16, DenseNet, MobileNet,

关键词： Dermatology

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An Approach for Enhancing Data Storage Capacity in Quick Response Code using Zip Compression Technique

An Approach for Enhancing Data Storage Capacity in Quick Res...

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2023 International Conference on Artificial Intelligence and Smart Communication, AISC 2023

作者： Bhardwaj, Cheshtaa Garg, Hitendra G.L Bajaj Group of Institutions Department of Computer Science and Engineering Mathura India Gla University Department of Computer Engineering and Applications Mathura India

ISBN: (纸本)9798350322309

A 2-D matrix barcode known as Quick Response Code stores data in four distinct encoding modes. Nowadays, it can be found on almost every product, billboard, digital transaction, and so on because of its growing popularity. It is now so user- and mobile-friendly that users can seamlessly transit from offline to online environments. It is used to store the information, allowing people to interact in real world using Smartphone and also as an information sharing medium. The primary objective of this work is to use ZIP compression to improve QR code data storage. This paper will provide a brief explanation of how QR code data storage capacity is enhanced by ZIP compression. As ZIP compression is a lossless compression technique, the result demonstrates how more data can be stored in QR codes of different sizes without losing any information in the form of compressed data. Additionally, after scanning the QR code, the compressed data is decompressed to retrieve the original data. © 2023 IEEE.

关键词： Data compression

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Data Analytics for Phishing Attack Detection using Deep Learning Techniques

Data Analytics for Phishing Attack Detection using Deep Lear...

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2023 International Conference on computer Communication and Informatics, ICCCI 2023

作者： Karthikeyan, M. Vijayachitra, S. Pyingkodi, M. Aadhish, T. Dhayananth, P. Goodwin, G. Kongu Engineering College Department of Computer Applications Erode India Kongu Engineering College Department of Electronics and Instrumentation Engineering Erode India

ISBN: (纸本)9798350348217

As the process of digital transformation continues to advance, the challenges to global cyber security have increased. As a result of digitalization, hackers have more alternatives. Phishing efforts are the initial type of cyber-attacks, and they aim to get individual user passwords. Phishing is a common tactic used by hackers to trick people into giving them access to a company's networks and digital assets. Phishing is a type of cyber-attack in which a victim is tricked into divulging sensitive information including account passwords, bank account details, ATM pin numbers, and other personal information. Phishing-style cyber-attacks hurt individuals by deceiving them into disclosing important information. In this paper, the phishing attack dataset will be taught and evaluated to see which deep learning method yields the best results. © 2023 IEEE.

关键词： Recurrent neural networks

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Detection of Credit Card Fraud using Machine Learning Algorithms 11

Detection of Credit Card Fraud using Machine Learning Algori...

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11th International Conference on System Modeling and Advancement in Research Trends, SMART 2022

作者： Singh, Akhilesh Kumar GLA University Computer Engineering and Applications Mathura India

ISBN: (纸本)9781665487320

The use of online transactions in regular life has increased over the past ten years due to continuous development in technology and network connectivity. Online transactions are easy, simple, and user-friendly is the main reason for new users to constantly join the vast population benefitting them from the system. The main demerit of the online transaction system is a misuse of the system is defined as theft or one's credit card information which is used for self-profit without the permission of the cardholder. In this paper we have uses different ML algorithms such as regression, random forest classifier, and decision tree to control the heavily imbalanced dataset. Finally, in this research, we will calculate the accuracy, precision, recall, f1 score, and confusion matrix. © 2022 IEEE.

关键词： Decision trees

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