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TreeChain: A High throughput and Efficient Search based Secure application for Internet of Vehicles 23

TreeChain: A High Throughput and Efficient Search based Secu...

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24th International conference on Distributed Computing and Networking (ICDCN)

作者： Kumar, Amritesh Das, Debasis IIT Jodhpur Rajasthan India

ISBN: (纸本)9781450397964

the Internet of Vehicles (IoV) rapidly expands due to lower bandwidth, enhanced security, and privacy for high-quality information sharing with minimal latency. To properly execute all these services, IoV must first overcome several challenges created by the centralized architecture, notably those related to throughput and data storage. We offer TreeChain, a high throughput and efficient information-searching technique based on a novel balance tree structure that also addresses the most extended chain sequence problem. Following that, TreeChain compares security strength in terms of several types of security attacks to other state-of-the-art methodologies, as well as performance results in terms of throughput and data storage. According to the results, TreeChain outperforms state-of-the-art solutions in terms of security and performance.

关键词： Internet of Vehicles (IoV) Balance Tree Blockchain throughput

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Investigation of the Internet of things (IoT) security and privacy Issues 5

Investigation of the Internet of Things (IoT) Security and P...

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5th International conference on Inventive Research in Computing applications, ICIRCA 2023

作者： Gautam, Krishan Kant Singh Kumar, Rajendra Yadav, Rakesh Sharma, Preeti Jamia Millia Islamia Central University Department of Computer Science New Delhi110025 India Shivaji College University of Delhi Department of Computer Science New Delhi110027 India

ISBN: (纸本)9798350321425

Connecting diverse devices and facilitating unhindered connection and data exchange, the IoT has dramatically altered the ways in which humans engage with their surroundings. However, there are major privacy and safety risks that come along with this connectedness. With the goal of determining the most common vulnerabilities and their effects on IoT systems, this study explores these concerns. Assessment of risks, algorithms for data encryption, and security measures are all part of the study technique. Weak authentication, unsecured firmware, unencrypted communication, and a lack of security upgrades are all highlighted as significant via this research of vulnerability frequency. By encouraging manufacturers to priorities security measures, pushing the adoption of standards and laws, and improving user awareness and education, these results provide a basis for driving practical improvements in the IoT security environment. the findings of this study inform academics, industry experts, policymakers, and end-users of IoT systems, while also providing a foundation for future studies on this topic. A more secure and trustworthy IoT ecosystem may be built with the use of sophisticated threat modelling, security frameworks, privacy-preserving technology, security automation, and ethical considerations. © 2023 IEEE.

关键词： Authentication

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Federated Learning for Predictive Maintenance: Model Comparisons and privacy Advantages 5

Federated Learning for Predictive Maintenance: Model Compari...

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5th International conference on IoT Based Control Networks and Intelligent Systems, ICICNIS 2024

作者： Singh, Ashita Sampath, Raghav Raj, Shiv Akash Dhinakar Mary, Gerardine Immaculate Aarthi, G. Rajesh Kumar, M. School of Electronics Engineering Vellore Institute of Technology Vellore India

ISBN: (纸本)9798331518097

Predictive maintenance has evolved into an essential component of modern industrial systems, enabling early detection of equipment defects and reducing downtime. Traditional methodologies have frequently relied on centralized machine learning models to collect large amounts of sensitive data, presenting significant privacy and security concerns in the Big data era. this work presents a federated learning architecture developed exclusively for predictive maintenance, allowing multiple clients to train machine learning models simultaneously while keeping their data decentralized. Several machine learning algorithms were used, including Logistic Regression, Random Forest, Ridge Classifier, AdaBoost, Decision Tree, and Gradient Boosting, as well as advanced techniques such as the Synthetic Minority Over-sampling Technique (SMOTE) to address class imbalances, Principal Component Analysis (PCA) to reduce dimensionality, and Particle Swarm Optimization (PSO) to tune hyperparameters. Empirical evaluations on the Gearbox Fault Diagnosis and Machine Predictive Maintenance Classification datasets demonstrate that the proposed federated learning framework achieves performance comparable to traditional centralized approaches while preserving data privacy. In the Machine Predictive Maintenance Classification dataset, Random Forest achieved the highest performance, attaining an impressive accuracy of 93.17% in the federated learning approach. For the Gearbox Fault Diagnosis dataset, the accuracy of all models was remarkably similar in both the traditional and federated learning approaches, which is a noteworthy outcome. these results indicate that federated learning can effectively balance the demands of privacy and performance in predictive maintenance applications, making it a viable alternative for industries that prioritize data confidentiality. © 2024 IEEE.

关键词： Federated learning

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Local Methods for privacy Protection and Impact on Fairness 23

Local Methods for Privacy Protection and Impact on Fairness

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13th acm conference on data and application security and privacy (CODASPY)

作者： Palamidessi, Catuscia Inria Saclay Palaiseau France Inst Polytech Paris Palaiseau France

ISBN: (纸本)9798400700675

the increasingly pervasive use of big data and machine learning is raising various ethical issues, in particular privacy and fairness. In this talk, I will discuss some frameworks to understand and mitigate the issues, focusing on iterative methods coming from information theory and statistics. In the area of privacy protection, differential privacy (DP) and its variants are the most successful approaches to date. One of the fundamental issues of DP is how to reconcile the loss of information that it implies with the need to preserve the utility of the data. In this regard, a useful tool to recover utility is the iterative Bayesian update (IBU), an instance of the expectation-maximization method from statistics. I will show that the IBU, combined with a version of DP called d-privacy (also known as metric differential privacy), outperforms the state-of-the-art, which is based on algebraic methods combined with the randomized response mechanism, widely adopted by the Big Tech industry (Google, Apple, Amazon, ... ). then, I will discuss the issue of biased predictions in machine learning, and how DP can affect the level of fairness and accuracy of the trained model. Finally, I will show that the IBU can be applied also in this domain to ensure fairer treatment of disadvantaged groups and reconcile fairness and accuracy.

关键词： Differential privacy metric differential privacy iterative Bayesian update fairness

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5th International conference on security and privacy in New Computing Environments, SPNCE 2022

5th International Conference on Security and Privacy in New ...

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5th International conference on security and privacy in New Computing Environments, SPNCE 2022

ISBN: (纸本)9783031306228

the proceedings contain 12 papers. the special focus in this conference is on security and privacy in New Computing Environments. the topics include: P-TECS: An Energy Balance Algorithm for Opportunistic Networks Integrating Multiple Node Attributes;network Situation Awareness Model Based on Incomplete Information Game;an Improved Authenticated Key Agreement Protocol for IoT and Cloud Server;efficient Two-Party Authentication Key Agreement Protocol Using Reconciliation Mechanism from Lattice;anonymous and Practical Multi-factor Authentication for Mobile Devices Using Two-Server Architecture;cross-Chain data Auditing for Medical IoT data Sharing;Outsourced privacy-Preserving SVM Classifier Model over Encrypted data in IoT;a Scheme of Anti Gradient Leakage of Federated Learning Based on Blockchain;Analysis of a New Improved AES S-Box Structure;social Internet of Tings Trust Management Based on Implicit Social Relationship.

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Geometry of Sensitivity: Twice Sampling and Hybrid Clipping in Differential privacy with Optimal Gaussian Noise and application to Deep Learning 23

Geometry of Sensitivity: Twice Sampling and Hybrid Clipping ...

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30th acm SIGSAC conference on Computer and Communications security (acm CCS)

作者： Xiao, Hanshen Wan, Jun Devadas, Srinivas MIT Cambridge MA 02139 USA

ISBN: (纸本)9798400700507

We study the fundamental problem of the construction of optimal randomization in Differential privacy (DP). Depending on the clipping strategy or additional properties of the processing function, the corresponding sensitivity set theoretically determines the necessary randomization to produce the required security parameters. Towards the optimal utility-privacy tradeoff, finding the minimal perturbation for properly-selected sensitivity sets stands as a central problem in DP research. In practice, l(2)/l(1)-norm clippings with Gaussian/Laplace noise mechanisms are among the most common setups. However, they also suffer from the curse of dimensionality. For more generic clipping strategies, the understanding of the optimal noise for a high-dimensional sensitivity set remains limited. this raises challenges in mitigating the worst-case dimension dependence in privacy-preserving randomization, especially for deep learning applications. In this paper, we revisit the geometry of high-dimensional sensitivity sets and present a series of results to characterize the non-asymptotically optimal Gaussian noise for Renyi DP (RDP). Our results are both negative and positive: on one hand, we show the curse of dimensionality is tight for a broad class of sensitivity sets satisfying certain symmetry properties;but if, fortunately, the representation of the sensitivity set is asymmetric on some group of orthogonal bases, we show the optimal noise bounds need not be explicitly dependent on either dimension or rank. We also revisit sampling in the high-dimensional scenario, which is the key for both privacy amplification and computation efficiency in large-scale data processing. We propose a novel method, termed twice sampling, which implements both sample-wise and coordinate-wise sampling, to enable Gaussian noises to fit the sensitivity geometry more closely. With closed-form RDP analysis, we prove twice sampling produces asymptotic improvement of the privacy amplification given an

关键词： Differential privacy Sensitivity Geometry Renyi DP Clipping Twice Sampling privacy Amplification Deep Learning

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Edge-Based security Measures for a Robust Internet of things Ecosystem 5

Edge-Based Security Measures for a Robust Internet of Things...

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5th International conference on Electronics and Sustainable Communication Systems, ICESC 2024

作者： Niranjana, R. Ravi, A. Arvind, S. Vignesh, M. Vishaal, S. Sri Krishna College of Engineering and Technology Department of Ece Coimbatore India Francis Xavier Engineering College Department of Eee Tirunelveli India

ISBN: (纸本)9798350379945

Amidst the fourth technological revolution, characterized by the expanding reach of the internet and its applications, smart devices gain increasing acceptance. A proposition is presented for remotely managing household appliances via the internet, supported by local data processing to make it more secure. Utilizing the ESP32 microcontroller and Raspberry Pi 5 microprocessor enables remote appliance control from any internet-enabled location. Leveraging a local server enhances security, while Flutter serves as the development framework for the IoT application. Additionally, the local Flutter app contributes to enhancing project security measures. © 2024 IEEE.

关键词： Domestic appliances

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Comparative Analysis of Blowfish and Twofish Cryptographic Protocol 5

Comparative Analysis of Blowfish and Twofish Cryptographic P...

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5th International conference on Sustainable Communication Networks and application, ICSCNA 2024

作者： Rawat, Bhupender Singh Singh, Anushree Sharma, Hemendra Shankar Saxena, Megha Verma, Pooja College of Smart Computing COER University Roorkee India Invertis University Department of Computer Applications Bareilly India Uttaranchal Institute of Technology Uttaranchal University Uttarakhand Dehradun248007 India

ISBN: (纸本)9798331530013

In today's interconnected world showcasing technical advancement, the exchange of information is conducted through the network as a communication medium at the click of a button. Still, the problem is that the medium we use as communication channel for transferring personal or confidential data is secure and original. So, the confidentiality and integrity of the data transferred over the network are ensured by Cryptography. this paper is conducted to assess the strengths and weakness of Blowfish and Twofish algorithm in different scenarios. the main objective is to provide decision-making as well as comprehensive insight into the application and suitability of both algorithms. the finding of this research is to understand the dependency of security on Cryptography, facilitating informed choice in cryptographic protocol design and deployment. © 2024 IEEE.

关键词： data transfer

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Literature Review on Packet Sniffing: Essential for Cybersecurity & Network security 5

Literature Review on Packet Sniffing: Essential for Cybersec...

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5th International conference on Intelligent Communication Technologies and Virtual Mobile Networks, ICICV 2024

作者： Shaw, Ranju Parveen, Suraiya Jamia Hamdard University Dept. of Computer Science Engineering with Specialization in Cyber Forensic Information Security Delhi India Faculty at Jamia Hamdard University Sest Department of Computer Science Engineering Delhi India

ISBN: (纸本)9798350385649

In the realm of cybersecurity and network security, the significance of monitoring and analyzing network traffic cannot be overstated Network packet sniffing, a technique employed to capture and inspect data packets traversing a network, has emerged as an imperative tool for understanding network behavior, identifying vulnerabilities, and enhancing overall security. this study analyzes packet sniffing by exploring its role in modern cybersecurity practices. By analyzing packet sniffing mechanisms, including its methodologies and potential applications, this res earch sheds light on its effectiveness as a proactive security solution. Furthermore, the study examines the ethical considerations surrounding packet sniffing, addressing concerns related to user privacy and data protection. It also investigates the legal aspects that govern the use of packet sniffing techniques, ensuring compliance with regulations while maximizing its benefits. through case studies and real-world examples, this research illustrates how packet sniffing can be leveraged to detect network anomalies, thwart malicious activities, and fortify network infrastructures. By presenting a comprehensive overview of packet sniffing's capabilities and limitations, this study equips cybersecurity practitioners with the knowledge needed to make informed decisions about its integration into their defensive strategies. © 2024 IEEE.

关键词： Cybersecurity

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Real-Time Adaptive Intrusion Detection System [RTPIDS] for Internet of things Using Federated Learning and Blockchain 5

Real-Time Adaptive Intrusion Detection System [RTPIDS] for I...

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5th International conference on data Intelligence and Cognitive Informatics, ICDICI 2024

作者： Sunil Raj, Y. Parimala, E. Helen Bosco, V. S. Jayakumar Paul Adarsh Samuel Raj, J. Kumar, P.J. Rosario Vasantha Kolluru, Vinothkumar Department of Data Science Palayamkottai627 002 India Gitam Deemed to be University Department of Computer Science Bangalore India Department of Bca Tirupattur635601 India San Jose State University San JoseCA United States Palayamkottai627002 India Stevens Institute of Technology Department of Data Science 1 Castle Point Terrace HobokenNJ07030 United States

ISBN: (纸本)9798350389609

In the growing field of the Internet of things (IoT), ensuring security and privacy has become a critical concern, particularly in sensitive domains such as healthcare. this paper presents a Real-Time Adaptive Intrusion Detection System (IDS) specifically designed for Internet of Medical things (IoMT) networks. the proposed system leverages Federated Learning (FL) and Blockchain technologies to address key challenges, including handling non-independent and identically distributed (non-IID) data, ensuring secure model updates, and providing real-time detection capabilities. By employing FL, the system enables IoMT devices to collaboratively train models without sharing sensitive data, thereby preserving privacy and improving scalability. Blockchain integration further enhances the system by ensuring the integrity and security of model updates, mitigating risks of tampering. the system demonstrates strong performance in detecting simpler intrusions in real-time, though additional enhancements are required to manage more complex attacks and large-scale IoT deployments. this work contributes a robust and scalable IDS framework, particularly valuable for protecting critical IoT environments such as healthcare networks. © 2024 IEEE.

关键词： Federated learning

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