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Hyperledger Fabric Blockchain Using CCTV Surveillance Cameras for Pipelines of Oil and Gas Fields

Hyperledger Fabric Blockchain Using CCTV Surveillance Camera...

引用

international conference on Wireless networks and Mobile Communications (WINCOM)

作者： Noor Ali Alshuraify Ali A. Yassin Zaid Ameen Abduljabbar Department of Computer Science Education College for Pure Sciences University of Basrah Basrah Iraq

ISBN: (数字)9798350377866

ISBN: (纸本)9798350377873

The oil and gas industry represents the beating heart of life in the world. These industries suffer from risks of unsustainability, whether they exist in infrastructure such as pipeline oil and gas leaks, vandalism, or information security for example the hacking of surveillance structures resulting in major physical losses amounting to billions of dollars. Various researchers have presented substantial work in this regard. Some of them focus on centralised systems, which struggle with a variety of issues, for instance a single point of failure, lack of privacy, and dependence on intermediaries, and others focus on decentralised systems, which have some limitations, such as delay in data retrieval; high costs; untrustworthy third-party storage; and 51 0/0, Sybil and Sniffing attacks. This paper proposes a decentralised Hyperledger Fabric blockchain based on thermal closed-circuit television (CCTV) surveillance cameras. It provides blockchain-based re-authentication, offering secure external storage by adopting an InterPlanetary File System which provides scalability and offers hash-based integrity of the data stored by utilising ECDSA, which provides low-complexity authentication, balancing between high security and complexity and resisting well-known malicious factors. Our work has been proven by security analysis. Additionally, compared with previous related works, the performance analysis achieves fewer cost results (1.624) ms for the computation cost, (1.056 bits for each administrator and user, 896 bits for CCTV) for the communication cost.

关键词： Resistance Costs distributed ledger Surveillance Oils Pipelines Cameras

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Hyperledger Fabric Platform for Secure and Efficient Data Sharing in Autonomous Vehicles

Hyperledger Fabric Platform for Secure and Efficient Data Sh...

引用

international conference on Wireless networks and Mobile Communications (WINCOM)

作者： Reem Alhabib Poonam Yadav Department of Computer Science University of York York UK

ISBN: (数字)9798350377866

ISBN: (纸本)9798350377873

Advancements in wireless communication, en-compassing cellular and non-terrestrial networks, are empow-ering Autonomous Vehicles (AVs) to revolutionize transportation. The achievement of real-time data exchange and seamless communication with infrastructure promises a future of safer and more efficient travel. However, the significant challenge of efficiently managing the extensive data generated by AVs persists. This data includes sensor readings, information about the surrounding environment, and potentially user data. Consequently, addressing concerns related to data processing, sharing among various stakeholders, privacy, integrity, and security is of utmost importance. This paper tackles the data sharing challenge by proposing and evaluating a platform built on Hyperledger Fabric, a blockchain technology. This platform aims to facilitate secure and efficient data sharing between all parties involved with AVs. Our initial testing reveals that the number of simulated users (virtual user count) and the amount of data processed (data load) can negatively impact the system's performance. This highlights the need for further optimization to ensure the platform can handle large-scale data sharing effectively.

关键词： distributed ledger Wireless networks System performance Transportation Fabrics Real-time systems Stakeholders

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SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain Management 4

SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain ...

引用

4th international conference on Advancements in Computing, ICAC 2022

作者： Amarasinghe, A.A.B.G. Gamagedara, G.M.H.S. Pitigala, K.K. Subawickrama, H.D.A.E. Abeywardena, Kavinga Yapa Yapa, Kanishka Department of Computer Science and Software Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Technology Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka Department of Information Systems Engineering Faculty of Computing Sri Lanka Institute of Information Technology Malabe Sri Lanka

ISBN: (纸本)9798350398090

With the rising competition among pharmaceutical companies, the current drug supply chain market has become more competitive with high-quality product segments. The rapid growth of internet pharmacies has made it more difficult to standardize drug safety throughout in complex distributed supply chain networks. There is a high chance of introducing counterfeit drugs which are almost the same as the original pharmaceuticals. Due to the lack of transparency the possibility of tampering with drugs in the current manual and web-based pharmaceutical systems is extremely high. Throughout the past recent years, these counterfeit drugs were recognized as the one of major worldwide problems. This situation is worse in developing countries. The unavailability of verifying the legitimacy of drug suppliers and poor mechanisms to trace the drugs were identified as critical points that need to be resolved. SUPPLYDECK comes as a solution for the critical scenario mentioned. This solution addresses the above problems using blockchain technology, as a distributed digital ledger that ensures transparency, traceability, and security. This can show promise for solving some global supply chain management problems using smart contracts and user authentication along with IoT technology and machine learning concepts that are critically examined with this potential application. © 2022 IEEE.

关键词： Smart contract

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Blockchain Enabled Secure and Efficient DDoS Attack Detection Mechanisms in Connected Internet of Vehicles Using Federated Learning

Blockchain Enabled Secure and Efficient DDoS Attack Detectio...

引用

Frontiers of Information Technology (FIT)

作者： Muhammad Dilshad Adeel Anjum Madiha Haider Syed Institute of Information Technology Quaid-i-Azam University Islamabad Pakistan

ISBN: (数字)9798331510503

ISBN: (纸本)9798331510510

distributed Denial of Service (DDoS) attacks pose a growing threat to the security and stability of Internet-of- Vehicles (IoV) networks. Traditional detection methods, which often rely on centralized systems, are vulnerable to single points of failure and data manipulation, making them less effective in IoV's distributed environment. In response, we propose a novel, decentralized approach that integrates Federated Learning (FL) with Blockchain Technology to enhance DDoS detection. Using FL, vehicles collaboratively train models on local data, preserving privacy and reducing centralized dependencies. Blockchain further strengthens this setup by providing secure, immutable record-keeping and automating model aggregation with smart contracts, ensuring transparency and tamper resistance. Tested on the CICDDoS2019 dataset, our model achieves a high detection accuracy of 93%, outperforming traditional methods in both security and scalability. This approach offers a resilient solution to IoV cybersecurity challenges and sets a new benchmark for combating modern cyber threats in vehicular networks.

关键词： Resistance Machine learning algorithms Federated learning Scalability Smart contracts Denial-of-service attack Stability analysis Blockchains computer crime Internet of Vehicles

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An Efficient Solution for the Collusion Problem of DECO

An Efficient Solution for the Collusion Problem of DECO

引用

Information security and Cryptology (ISCTURKEY), international conference on

作者： Uğur Şen Murat Osmanoğlu Ali Aydin Selçuk Institute of Applied Mathematics Middle East Technical University Ankara Turkey Department of Computer Engineering Ankara University Ankara Turkey Department of Computer Engineering TOBB University of Economics and Technology Ankara Turkey

ISBN: (数字)9798331533649

ISBN: (纸本)9798331533656

DECO is one of the state-of-the-art TLS evidence protocols that enables privacy-preserving verification without necessitating server-side modifications. It allows applications to extract verifiable data from websites. However, DECO is vulnerable to collusion attacks, where the prover and verifier may secretly conspire, even if DECO itself remains secure. This vulnerability limits DECO's ability to transfer data to smart contracts. To address the collusion issue, decentralized oracle networks have been integrated with DECO, involving multiple executions for the same attestation and relying on majority consensus to determine the correct outcome. While this approach mitigates collusion risk, it is inefficient. In this paper, we address the collusion vulnerability in DECO by proposing an enhanced approach that expands the verifier's role to a set of $n$ verifiers within a single DECO execution, utilizing a distributed Verifiable Random Function (DVRF). This method requires the prover to obtain consensus from all $n$ verifiers, thereby increasing the difficulty of collusion and enhancing the security of the system. Furthermore, the proposed solution is more efficient than executing $n$ separate DECO protocols and provides verifiable features that facilitate integration with slashing and reward mechanisms, thereby improving its applicability in decentralized systems.

关键词： Smart contracts Blockchains security Data mining Interoperability

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Improvement and Application of Blockchain Consensus Algorithm for distributed Power Energy System

Improvement and Application of Blockchain Consensus Algorith...

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IEEE international conference on Big Data Analysis (ICBDA)

作者： Junlong Liang Xiangzhi Liu Xiaoming Wu Ran Chen Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology ( Shandong Academy of Sciences) Jinan China

This article focuses on the application of blockchain consensus algorithms in the power energy trading system and the comparison of these algorithms such as PBFT(Practical Byzantine Fault Tolerance), Raft and IBFT(Istanbul Byzantine Fault Tolerance). The article introduces the concept of blockchain consensus algorithms and its application in power energy trading system. It then provides a brief overview of the three algorithms and their underlying structures. The comparison of the algorithms is done in terms of features such as scalability, reliability and security. The article also provides an overview of the advantages and disadvantages of each algorithm and its application in power energy trading system. We examine the blockchain partition algorithm improved by the integration of Raft consensus and IBFT consensus, with a special focus on the advantages of using a hybrid algorithm in distributed networks. We discuss the need for consensus algorithms that are well-suited to blockchain networks and the improved security provided by a consensus algorithm incorporating both Raft and *** also discuss the implications of incorporating Raft and IBFT into a blockchain partition algorithm and its potential scalability. In conclusion, we provide a synthesis of our research, with potentials for further studies and contributions in this field.

关键词： Fault tolerance Scalability Fault tolerant systems Consensus algorithm Big Data Throughput Hybrid power systems

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Reinforcement Learning for Dynamic Traffic Routing and Optimization

Reinforcement Learning for Dynamic Traffic Routing and Optim...

引用

international conference on Inventive Computation Technologies (ICICT)

作者： Sahar Ebadinezhad Ahmed Conteh Khaldoon Arshed Ali Ibrahim Khalaf Hussein Department of Computer Information Systems Computer Information Systems Research and Technology Center (CISRTC) Near East University Nicosia Cyprus Department of Computer Information Systems Near East University Nicosia Cyprus

ISBN: (数字)9798350359299

ISBN: (纸本)9798350359305

This paper addresses the rapidly evolving and expanding needs of network traffic with new methods, most notably Reinforcement Learning (RL). The RL has shown significant potential in addressing the challenges of dynamic traffic routing and optimization. Despite the complexity of adapting to varying network conditions, techniques such as deep RL and multi-agent systems have been proposed to enhance network efficiency. However, the challenges persist, including the scalability of these algorithms and their adaptability to real-time changes. This research aims to address these challenges by proposing a robust RL framework that not only adapts to dynamic traffic conditions but also scales effectively across large network infrastructures. Our research raises important questions: How effective are RL algorithms? What effect do reward functions have? How do they handle big networks? Are these models flexible enough to adjust? One goal of the research, then is to test all RL methods designed for online traffic direction in real time; examine their merits and problems when applied on large networks; discover whether they could be used under various network conditions or if only certain traffic patterns would work well with them. It should also seek out possible ways RL algorithms might increase overall network performance, as well as look at security issues This study is important because it could revolutionize how we manage network traffic. It means better, stronger and more reliable networks with RL, we can develop routing protocols that are flexible and distributed to overcome changing network circumstances as well as reach different destinations or satisfy various restraints.

关键词： Privacy Heuristic algorithms Telecommunication traffic Reinforcement learning Traffic control Routing Routing protocols

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Machine Learning and Deep Learning for Enhanced DDoS Detection in IoT-Based Intrusion Detection System

Machine Learning and Deep Learning for Enhanced DDoS Detecti...

引用

Innovative and Intelligent Information Technologies (IC3IT), international conference on

作者： Kamir Kharoubi Sarra Cherbal Maroua Akkal LRSD Lab Faculty of Sciences Ferhat Abbas University Setif 1 Setif Algeria

ISBN: (数字)9798331541064

ISBN: (纸本)9798331541071

Securing Internet of Things (IoT) networks is critical due to their increasing prevalence and susceptibility to cyberattacks. Machine Learning (ML) and Deep Learning (DL)powered Intrusion Detection systems (IDS) provide a promising solution to counter these threats. This study focuses on detecting distributed Denial of Service (DDoS) attacks, ensuring IoT device availability and protecting critical services within IoT ecosystems. Five ML and DL models, including XGBoost, were trained and evaluated on the recent CICIoT2023 dataset to classify network traffic as benign or DDoS. Evaluation results show that XGBoost outperformed other models, achieving a 99. 96% accuracy in multi-classifying DDoS attack types with benign traffic and a rapid prediction time of 0.56 microseconds per traffic sample. These results highlight XGBoost's potential for real-time IoT security applications.

关键词： Measurement Deep learning Accuracy Biological system modeling Intrusion detection Denial-of-service attack Real-time systems Internet of Things security computer crime

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Enhance security for the E-payment Application with Blockchain Technology

Enhance Security for the E-payment Application with Blockcha...

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international conference on Integrated Circuits, Design, and Verification (ICDV)

作者： Hung Le-Huy Thuat Nguyen-Khanh Quan Le-Trung Faculty of Computer Networks and Communications University of Information Technology Ho Chi Minh City Viet Nam Vietnam National University Ho Chi Minh City Vietnam

ISBN: (数字)9798350371864

ISBN: (纸本)9798350371871

E-payment systems, despite their practicality, face challenges such as data manipulation, limited bandwidth, and potential denial-of-service attacks. These issues can impact businesses, financial institutions, and individual users conducting transactions, as they struggle to manage large amounts of traffic simultaneously. Addressing these issues is crucial for ensuring secure and efficient transactions. This research presents a system that integrates Hyperledger Fabric with software-defined networking to enhance system throughput without significantly altering the Blockchain’s functionality. It also uses Hyperledger Aries technology for improved client identity recognition efficiency. The proposed system aims to reduce deployment time, maintain secure data, and provide a non-stop payment platform for client identification within a decentralized framework. It supports multiple concurrent users or system attacks with minimal average delay, ensuring efficient communication among components. The system is being researched to find a feasible solution that balances security and performance in real time while minimizing internal platform modifications.

关键词： Integrated circuit synthesis distributed ledger Face recognition Throughput Denial-of-service attack Real-time systems Fabrics

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Taxonomy and Future Threat of Rogue Access Point for Wireless Network 9

Taxonomy and Future Threat of Rogue Access Point for Wireles...

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9th international conference on Computing for Sustainable Global Development, INDIACom 2022

作者： Patel, Kashyap C. Patel, Ajaykumar Ganpat University Department of Computer Science Gujarat India A. M. Patel Institute of Computer Studies Ganpat University Gujarat India

ISBN: (纸本)9789380544441

Wireless network communication is an enormous and vast area of research. Many companies and organizations, related to wireless security have struggled due to the narrow, limited, and restricted access to the legitimate network. In a simplistic sense and most common manner, radio frequency (RF) is used for data communication on a network without wires. By its nature and methodology, wireless communication uses an air interface, and hence, it is vulnerable to attackers or hackers who can leverage these things and will compromise the legitimate network, devices, servers, applications, databases, and connected users. Wireless network attacks can be categorised into various types named distributed Denial of Service (DDoS), Evil-twin, Man-in-the-Middle (MITM), Wireless Fidelity (Wi-Fi) Deauthentication, and Internet of Things (IoT)-based attacks. These types of attacks can compromise and infect the operational technology of large-scale public and private sector organizations' Industrial Control systems (ICS) and Supervisory Control and Data Acquisition (SCADA). A wireless rogue access point has already done harmful things to businesses of all sizes, not just coffee shops, airport terminals, tube stations, or public gardens. This paper classifies rogue access points (RAPs) in wireless communication as a serious threat to the Wireless Local Area Network (WLAN) 802.11 protocol. The aim is to classify various types of wireless attacks and their various techniques, which are especially performed through RAP successfully. This paper also presents the statistical data and literature survey based on demand of next generation Wi-Fi enabled technologies and related cyber threats, which show that RAP is a serious threat to legitimate networks for wireless network and communication. © 2022 Bharati Vidyapeeth, New Delhi.

关键词： Wireless local area networks (WLAN)

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