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Comments on "Two Authentication and Key Agreement Protocols in WSN Environments" 5th

Comments on "Two Authentication and Key Agreement Protocol...

引用

5th international conference on Smart Vehicular Technology, Transportation, Communication and Applications, VTCA 2022

作者： Kong, Fangfang Kumari, Saru Wu, Tsu-Yang College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Department of Mathematics Chaudhary Charan Singh University Uttar Pradesh Meerut250004 India

ISBN: (纸本)9789819908479

Wireless sensor network (WSN) is a self-organizing network composed of distributed sensor nodes. Its appearance has dramatically changed people’s lives and is now widely used in various fields. Due to the openness of wireless channel, malicious attackers can eavesdrop, intercept or tamper with data in the channel. In the communication process, the user’s privacy is easy to leak, which leads to the user’s inability to communicate securely with the sensor node. Hence, it is necessary to design secure authentication and key agreement (AKA) protocols to enhance the communication security of users in the WSN environment. Recently, Jawad et al. proposed an anonymous three-factor authentication protocol based on symmetric encryption in WSN. Polai et al. proposed an authentication protocol using lightweight primitives in wireless body area networks. In this paper, we analyze Jawad et al.’s and Polai et al.’s protocol and find that both protocols have security vulnerabilities. We prove their protocols cannot resist sensor node capture attacks, known temporary information disclosure attacks, and hub node stolen database attacks. Finally, we put forward suggestions for the improvement of these two protocols. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Authentication

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Federated Ensemble for Lung Pneumonia Detection

Federated Ensemble for Lung Pneumonia Detection

引用

international conference on Computing and Networking Technology (ICCNT)

作者： Vishwash Sharma VenkataHemant Kumar Reddy Challa Pasupuleti Pranavi Tripty Singh Rekha R Nair Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Bengaluru India Department of Computer Science and Enginnering Alliance School of Advanced Computing Alliance University Bengaluru India

ISBN: (数字)9798350370249

ISBN: (纸本)9798350370270

In the realm of medical imaging, the detection of lung pneumonia through automated methods has garnered significant attention due to its potential to augment diagnostic processes. This study proposes a novel approach, termed Federated CNN Ensemble, aiming to enhance the accuracy and robustness of pneumonia detection from chest X -ray images through federated learning and ensemble techniques. The proposed method leverages convolutional neural networks trained locally at distributed medical institutions, thereby preserving data privacy and security while harnessing collectiveintelligence. By aggregating the learned models through an ensemble strategy, our approach achieves superior performance in pneumonia detection compared to standalone CNN models. This research contributes to the advancement of automated medical diagnosis systems, offering a promising avenue for scalable and collaborative disease detection in healthcare settings.

关键词： Pneumonia Accuracy Federated learning Computational modeling Lung Robustness Convolutional neural networks security Medical diagnosis Medical diagnostic imaging

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Decentralized Metaverse: Towards a Secure, Autonomous, and Inclusive Virtual World

Decentralized Metaverse: Towards a Secure, Autonomous, and I...

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Electrical, computer and Energy Technologies (ICECET), international conference on

作者： Saeed Hamood Alsamhi Ammar Hawbani Santosh Kumar Lukasz Porwol Edward Curry Insight Centre for Data Analytics University of Galway Galway Ireland School of Computer Science Shenyang Aerospace University Shenyang China Computer Science and Engineering IIIT Naya Raipur Chhattisgarh India

The metaverse is a universal and immersive virtual world, which are components of cyber-physical-social systems (CPSS). The traditional centralized approach to building a metaverse poses risks to user privacy, security, and autonomy. A growing movement towards a decentralized metaverse, built on blockchain and distributed networks, has emerged as a response. This paper presents a conceptual framework for building a de-centralized metaverse emphasising security, user autonomy, and inclusivity within situation-aware CPSS. Drawing on literature from blockchain, virtual worlds, and decentralized systems, we explore the technical and regulatory challenges that must be addressed to create a more secure, autonomous, and inclusive virtual world. Our framework highlights the importance of community-driven governance and transparent data management for building a decentralized metaverse that empowers users and promotes collaboration.

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Enhancing Building Management systems with distributed Intelligence

Enhancing Building Management Systems with Distributed Intel...

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international conference on Communication systems and Network Technologies (CSNT)

作者： B Rajalakshmi V Venkat Reddy Bhupendra Malviya H Pal Thethi Praveen Myasar Mundher Adnan Department of Computer Science New Horizon College of Engineering Bangalore India Institute of Aeronautical Engineering Hyderabad Department of Computer Science & Engineering IES College of Technology Bhopal M. P. India Lovely Professional University Phagwara India Lloyd Institute of Engineering & Technology Greater Noida Uttar Pradesh The Islamic university Najaf Iraq

ISBN: (数字)9798350305463

ISBN: (纸本)9798350305470

There has been a significant change in the management and operation of buildings as a result of the incorporation of distributed Intelligence into Building Management systems. This paper analyzes the revolutionary potential of this integration, concentrating on the suggested technique, which amalgamates Decentralized Sensor networks, Federated Learning, and Predictive Maintenance algorithms. The approach uses real-time data, machine learning, and decentralized decision-making to improve building operations. The new technique is shown to perform better on all of the most important performance criteria when compared to the six original approaches. The suggested technique greatly improves upon conventional methods with regards to energy efficiency, occupant comfort, system responsiveness, maintenance cost reduction, security effectiveness, and flexibility. It is much easier to understand the comparative analysis and how the suggested solution outperforms the separate methods by using visual representations.

关键词： Visualization Costs Smart cities Decision making Energy efficiency Real-time systems Maintenance

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Blockchain - Enhanced Secure and Efficient IPFS - based EHR Management: A Decentralized Approach

Blockchain - Enhanced Secure and Efficient IPFS - based EHR ...

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international conference on Emerging Trends in networks and computer Communications (ETNCC)

作者： Santhi V Diyaa Santhosh Samyuktha N Sachitha C Department of Computer Science and Engineering PSG College of Technology Coimbatore

ISBN: (数字)9798350353266

ISBN: (纸本)9798350353273

This paper introduces a novel healthcare system based on blockchain and IPFS technologies, designed to securely store and facilitate the sharing of patient health records. The system is structured into three layers: blockchain,IPFS, and the application. Within the blockchain layer, the primary function is to store and safeguard patient health records. This is achieved through the utilization of distributed ledger technology, ensuring the records' immutability and resistance to tampering. Meanwhile, the IPFS layer offers the infrastructure necessary for secure storage and retrieval of patient health records. Lastly, the application layer provides the user interface for accessing and managing patient health records, including functions like addition, updates, and deletions. This innovative system boasts numerous advantages compared to traditional healthcare systems. It offers enhanced security, as patient health records are stored in an encrypted format across a distributed network. Furthermore, it provides a more efficient means of securely sharing patient health records among healthcare providers. The system also improves transparency by recording all network accesses as transactions. While this system is still in its early developmental stages, its potential to transform the storage and sharing of healthcare records is promising.

关键词： Smart contracts Memory Medical services Transforms User interfaces Market research Blockchains Recording Secure storage Testing

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Prediction of Human Age and Gender Using Deep Learning For Smart security systems

Prediction of Human Age and Gender Using Deep Learning For S...

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distributed Computing and Electrical Circuits and Electronics (ICDCECE), IEEE international conference on

作者： Durga Bhavani Kinthadi Abhishekar Burugu Anish Rumandla Sujana S Dept. of ECE Vardhaman College of Engineering Hyderabad Telangana Dept. of ECE Vardhaman College of Engineering Hyderbad Telangana

The demand for accurate identification and verification of a person has increased as the number of smart security systems has grown. In recent years, data from a human face has been used in numerous real-world applications, including social networking, security monitoring, advertising, and entertainment. computer vision researchers have long been interested in this topic because automatic age and gender prediction from facial images is crucial for interpersonal communication. This work predicts that the gender will be either ‘Male’ or ‘Female,’ and that the age will be one of the following ranges: (0-5), (6-10), (11-17), (18-25), (25-32), (33-45), (46-55), (55-70). In the proposed system the images are preprocessed and then the convolutional neural networks are used to extract age and gender-related features and classified the images using the appropriate classifiers. The face images are taken from the UTK dataset and the proposed method achieved a training accuracy of 92.5% and a validation accuracy of 90%.

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Optimized Local Secret Sharing Techniques for distributed Blockchain networks

Optimized Local Secret Sharing Techniques for Distributed Bl...

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international conference on Science Technology Engineering and Management (ICONSTEM)

作者： M.S. Roobini Chappidi Sindhuja Chanda Shravan Manju C Nair M. Gowri Department of Computer Science and Engineering Sathyabama Institute of Science and Technology Chennai India

ISBN: (数字)9798350365092

ISBN: (纸本)9798350365108

In blockchain systems, transaction details are stored across a distributed ledger, and each peer bears the responsibility of upkeeping an identical replica of this ledger. This project explains the challenges faced by the high storage costs occurred in the traditional blockchain systems, where each peer maintains an identical copy of transaction data. A DSB system tries to tackle the high storage cost issue by incorporating cryptographic techniques. The distributed Storage Blockchain (DSB) systems, however face communication costs during peer failures, which are often caused by denial-of-service attacks. To overcome these challenges, the DSB approach has been using a LSS approach. This method utilizes a hierarchical privacy scheme comprising a primary global secret node and multiple subordinate local secret nodes. The suggested distributed Storage Blockchain (DSB) approach, incorporating Local Secret Sharing (LSS), enhances both storage efficiency and reduces recovery communication costs. This approach aims enhancing both the effectiveness of storage and reducing communication costs. During recovery processes. In the case of peer failures and security threats the DSB with LSS approach provides a solution for creating more resilient and resource-efficient blockchain systems.

关键词： Privacy Costs Electric breakdown Denial-of-service attack Mathematics Encoding Blockchains

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Modified LSTM Based Intrusion Detection System for networks

Modified LSTM Based Intrusion Detection System for Networks

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international conference on Parallel distributed and Grid Computing (PDGC)

作者： R Allen Joseph Abhishek K Anna Sanjay Kumar T Department of Information Technology Velammal College of Engineering and Technology Madurai India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

The proliferation of wireless networks in the past decade is seriously coupled with security threats and intrusion incidents, raising greater demands for robust and efficient intrusion detection systems. A new approach is present in this paper, to intrusion detection in wireless networks based on the computational paradigm Echo State networks. ESN is one type of reservoir computing model. The inherent advantages of training efficiency and dynamic responses for temporal patterns make ESN especially fit for the task of detecting network traffic anomalies. In the following, we design our proposed ESN-based IDS to identify and classify various types of network intrusions with high accuracy and low computational overhead. This system will be robust and adaptive against different attack scenarios by training with the entire dataset of normal and malicious network activities. We show the efficiency of our technique through extensive simulations and real experimentation for a wide range of intrusion detection such as exploit as well as unauthorized access attempts. The results explicitly show that ESN-based IDS outperforms detection accuracy, false positive rate, and time response by traditional machine learning methods.

关键词： Training Accuracy Wireless networks Computational modeling Intrusion detection Reservoir computing Echo state networks Threat assessment Time factors computer crime

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The Application of Deep Learning Techniques in computer security for Big Data Anomaly Detection

The Application of Deep Learning Techniques in Computer Secu...

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Integrated Intelligence and Communication systems (ICIICS), international conference on

作者： Zhijuan Duan Wuhan University of Engineering Science Wuhan Hubei China

ISBN: (数字)9798331504960

ISBN: (纸本)9798331504977

Today, with the rapid development of the Internet, the requirements for network security are increasing day by day. This article explores the application of deep learning technology, especially convolutional neural networks (CNN), in anomaly detection in big data environments. The study designed a CNN-based anomaly detection framework to simulate the way the human brain processes visual information, achieving automatic extraction of complex features in data and recognition of abnormal patterns. Research methods include building multi-layer CNN models, data preprocessing, feature extraction and anomaly scoring, as well as comparative analysis of experimental designs. Experimental results show that CNN performs well in different network attack scenarios, with an average recall rate of 94.9%, a precision rate of 92.6%, and an average accuracy rate of 93.75%, which is significantly better than the local anomaly factor (LOF) and principal component analysis (PCA). ) and kernel density estimation (KDE) and other traditional methods. Additionally, CNN has lower response times when detecting advanced persistent threats, ransomware attacks, and distributed denial-of-service attacks. As an emerging abnormal behavior identification method, CNN can quickly and accurately find targets for users, which is of great significance to information security in today's Internet environment.

关键词： Deep learning Visualization Accuracy Big Data Feature extraction Data models Internet Convolutional neural networks Anomaly detection Principal component analysis

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Comparing the Supersonic Cloud Computing Model to Enhance the Networking and security in Traditional Data Centers

Comparing the Supersonic Cloud Computing Model to Enhance th...

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Disruptive Technologies (ICDT), international conference on

作者： Ranadeep Reddy Palle Vinay Mallikarjunaradhya Haritha Yennapusa K. Suganyadevi Nidhi Gupta Department of Computer Science University of Houston Austin Texas USA Legal Tech Platform Thomson Reuters Toronto Canada Department of Computer Science University of central Missouri Austin Texas USA Department of ECE Sri Eshwar College of Engineering Coimbatore Tamil Nadu India Department of Computer Science and Engineering G.L. Bajaj Institute of Technology and Management Greater Noida India

The Comparing the supersonic cloud computing model to enhance the networking and security in Traditional Data Centers study aimed to analyse the differences between traditional data centers and cloud computing models. The researchers' findings demonstrate that the supersonic cloud model consists of a number of services and functions that can improve network and security performance across all levels of the data center. Furthermore, the supersonic model offers more options for load balancing through routing protocols and more secure network access. In addition, the supersonic model allows for more flexibility in the resources used, such as virtual servers and storage systems. Lastly, the supersonic model allows for greater scalability through the use of distributed simultaneous networks. These benefits pave the way to the eventual goal of improving existing data center operation and security. The objective of this study was to assess the potential of supersonic cloud computing model (SCC) to improve the networking and security in traditional data centers. The findings indicated that SCC improved the network efficiency by 10.4%, reduced network latency by 11 %, and improved packet delivery rates by 8.6%. The security was also improved, with overall firewall rules increased by 12 %, and 5% fewer security violations. Overall, SCC showed promising performance in improving the networking and security of traditional data centers.

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