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Simulation of distributed denial of service attack against ethereum smart contract on the blockchain

Simulation of distributed denial of service attack against e...

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international conference on computer Vision and Internet of Things 2023 (ICCVIoT'23)

作者： N. Jeyakkannan G. Subathra R. Karthika A. Lavanya N. Kirupanithi Rathinam Technical Campus Coimbatore India Sathyabama Institute of Science and Technology Chennai India

The perception and functional infrastructure of smart networks are changing as a result of the advent of blockchain technology and the proliferation of IoT-driven devices. Deploying Ethereum's smart contracts, the blockchain allows secure administration, authentication, and access to IoT equipment. Blockchain-IoT systems are creating a high amount of sensitive data due to increased demand and growth. DDoS assaults pose the most serious danger to smart contracts in blockchain-IoT systems, according to a new report. Smart contracts in blockchain-IoT systems currently lack any kind of security mechanism. Artificial intelligence approaches for threat categorization utilizing Adaptive Weighted MLP ("Multi-Layer Perceptron") are used first in the proposed security architecture to provide complete autonomy in making decisions. The primary job of the transaction handling engine is to use adaptive weighted MLP to categorize transactions into regular and attack kinds. If the transaction is legitimate, the smart contract will carry out its business logic and add it to the blockchain network in the cloud, where it will be included to future transactions. SHA-256 hash value calculation is used to generate the certificate key. Ethereum Blockchain-based proof of authority is used for data aggregation and transaction addition in the Consensus blockchain mechanism. Homomorphic cryptography schemes are also applied to enhance security. To avoid intrusions, the security of the system is improved. The proposed framework's performance is measured against a real-world IoT dataset, called BoT-IoT20,21, which comprises current DoS ("Denial-of-Service"), DDoS (distributed Denial-of-Service Attack"), and other IoT-based attack samples. We used a variety of assessment measures to determine how effective the suggested distributed system is in finding DDoS attacks. It's also been likened to some of the most modern approaches in the field.

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Quantum Assisted Scheduling Algorithm for Federated Learning in distributed networks

Quantum Assisted Scheduling Algorithm for Federated Learning...

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international conference on computer Communications and networks (ICCCN)

作者： Xinliang Wei Lei Fan Yuanxiong Guo Yanmin Gong Zhu Han Yu Wang Department of Computer and Information Sciences Temple University Philadelphia PA USA Department of Engineering Technology University of Houston Houston TX USA Department of Information Systems and Cyber Security University of Texas at San Antonio San Antonio TX USA Department of Electrical and Computer Engineering University of Texas at San Antonio San Antonio TX USA Department of Electrical and Computer Engineering University of Houston Houston TX USA

The scheduling problem for federated learning (FL) with multiple models in a distributed network is challenging, as it involves NP-hard mixed-integer nonlinear programming. Moreover, it requires optimal participant selection and learning rate determination among multiple FL models to avoid high training costs and resource competition. To overcome those chal-lenges, in literature the Benders' decomposition algorithm (BD) can deal with mixed integer problems, however, it still suffers from limited scalability. To address this issue, in this paper, we present the Hybrid Quantum-Classical Benders' Decomposition (HQCBD) algorithm, which combines the power of quantum and classical computing to solve the joint participant selection and learning scheduling problem in multi-model FL. HQCBD decomposes the optimization problem into a master problem with binary variables and small subproblems with continuous variables. This collaboration maximizes the potential of both quantum and classical computing, and optimizes the complex joint optimization problem. Simulation on the commercial D-Wave quantum annealing machine demonstrates the effectiveness and robustness of the proposed method, with up to 18% improvement of iterations and 81% improvement of computation time over BD algorithm on classical CPUs even at small scales.

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AI-Powered Real-Time Obstacle Detection for UAVs using Blockchain

AI-Powered Real-Time Obstacle Detection for UAVs using Block...

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international conference on Parallel distributed and Grid Computing (PDGC)

作者： Aparna Kumari Patel Ansh Alkeshbhai Shah Akshat Nimesh Safaa Najah Saud Al-Humairi Prasun Kumar Sunil Kumar Prakriti Lohumi Department of Computer Science and Engineering Institute of Technology Nirma University Ahmedabad India Department of Computer Science and Engineering Faculty of Engineering Science and Technology Adani University Ahmedabad India Faculty of Information Sciences and Engineering Management and Science University Malaysia Department of Biotechnology SSET Sharda University Greater Noida U.P. India Department of AI and Data Science Guru Jambheshwar University of Science & Technology Hisar Haryana India Department of Artificial Intelligence and Data Science Bhagwan Parshuram Institute of Technology New Delhi Delhi India

ISBN: (数字)9798331521349

ISBN: (纸本)9798331521356

AI integration with UAVs has brought significant advancements in aerial control and operational safety, particularly in real-time obstacle detection—an essential aspect for navigating unknown environments. This work introduces an innovative AI-based solution for obstacle detection in UAVs, leveraging deep learning techniques to enhance precision and environmental awareness. The system’s architecture involves multiple layers, where UAVs first capture high-resolution images that undergo a processing pipeline including data pre-processing, augmentation, and labeling. A key element of this process is the use of Convolutional Neural networks (CNNs) to train models capable of identifying obstacles across various terrains. To ensure the integrity and security of the data, especially in complex multi-UAV systems, blockchain technology is integrated. Utilizing distributed Hash Tables (DHTs) and the Interplanetary File System (IPFS), this decentralized system creates a content-addressable database to store and authenticate unalterable records. Experimental analysis demonstrates that this system offers high accuracy in real-time obstacle detection, minimizing false positives and improving UAV safety.

关键词： Deep learning Autonomous aerial vehicles Real-time systems Blockchains Safety Reliability InterPlanetary File System Convolutional neural networks Artificial intelligence Drones

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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...

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

Hyperledger Fabric Blockchain Using CCTV Surveillance Camera...

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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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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...

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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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SUPPLYDECK - A Blockchain-Based Pharmaceutical Supply Chain Management 4

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

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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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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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An Efficient Solution for the Collusion Problem of DECO

An Efficient Solution for the Collusion Problem of DECO

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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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Reinforcement Learning for Dynamic Traffic Routing and Optimization

Reinforcement Learning for Dynamic Traffic Routing and Optim...

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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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