Simulation methods, techniques and tools of computer systems and networks

Ndih, Eugene David Ngangue; Cherkaoui, Soumaya

doi:10.1016/b978-0-12-800887-4.00017-1

Cited by 6 publications

(9 citation statements)

References 51 publications

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“…We use MNIST data and a widely used software environment PyTorch to perform a digit classification task to evaluate the proposed framework. The simulations were conducted on a laptop with 2.2 GHz Intel i7 processor and 16 GB of memory [29,30]. Simulation parameters are illustrated in Table 1.…”

Section: Discussionmentioning

confidence: 99%

Towards a Secure and Reliable Federated Learning using Blockchain

Moudoud¹,

Cherkaoui²,

Khoukhi³

2022

Preprint

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Federated learning (FL) is a distributed machine learning (ML) technique that enables collaborative training in which devices perform learning using a local dataset while preserving their privacy. This technique ensures privacy, communication efficiency, and resource conservation. Despite these advantages, FL still suffers from several challenges related to reliability (i.e., unreliable participating devices in training), tractability (i.e., a large number of trained models), and anonymity. To address these issues, we propose a secure and trustworthy blockchain framework (SRB-FL) tailored to FL, which uses blockchain features to enable collaborative model training in a fully distributed and trustworthy manner. In particular, we design a secure FL based on the blockchain sharding that ensures data reliability, scalability, and trustworthiness. In addition, we introduce an incentive mechanism to improve the reliability of FL devices using subjective multi-weight logic. The results show that our proposed SRB-FL framework is efficient and scalable, making it a promising and suitable solution for federated learning.

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Section: Discussionmentioning

confidence: 99%

Towards a Secure and Reliable Federated Learning using Blockchain

Moudoud¹,

Cherkaoui²,

Khoukhi³

2022

Preprint

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“…To verify the effectiveness of the proposed 5G enabled IoT security solution to predict and mitigate FDIA and DDoS attacks, we performed extensive experiments. The experiments were meant to evaluate the detection rate and the number of attacks mitigated by the proposed security model [19,20]. We simulated the proposed solution on Intel machine Core™ i7-8550U.…”

Section: Discussionmentioning

confidence: 99%

Prediction and Detection of FDIA and DDoS Attacks in 5G Enabled IoT

2021

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Security in the fifth generation (5G) networks has become one of the prime concerns in the telecommunication industry. 5G security challenges come from the fact that 5G networks involve different stakeholders using different security requirements and measures. Deficiencies in security management between these stakeholders can lead to security attacks. Therefore, security solutions should be conceived for the safe deployment of different 5G verticals (e.g., industry 4.0, Internet of Things (IoT), etc.). The interdependencies among 5G and fully connected systems, such as IoT, entail some standard security requirements, namely integrity, availability, and confidentiality. In this article, we propose a hierarchical architecture for securing 5G enabled IoT networks, and a security model for the prediction and detection of False Data Injection Attacks (FDIA) and Distributed Denial of Service attacks (DDoS). The proposed security model is based on a Markov stochastic process, which is used to observe the behavior of each network device, and employ a range-based behavior sifting policy. Simulation results demonstrate the effectiveness of the proposed architecture and model in detecting and predicting FDIA and DDoS attacks in the context of 5G enabled IoT.

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“…There have been several researchers have tried to address the challenges of using blockchain in an IoT context [7,8]. For instance, [9] and [10] used a distributed time-based consensus algorithm to ensure end-to-end security and ensure trustworthy transactions.…”

Section: Introductionmentioning

confidence: 99%

Towards a Scalable and Trustworthy Blockchain: IoT Use Case

Moudoud,

Cherkaoui,

Khoukhi

2022

Preprint

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Recently, blockchain has gained momentum as a novel technology that gives rise to a plethora of new decentralized applications (e.g., Internet of Things (IoT)). However, its integration with the IoT is still facing several problems (e.g., scalability, flexibility). Provisioning resources to enable a large number of connected IoT devices implies having a scalable and flexible blockchain. To address these issues, we propose a scalable and trustworthy blockchain (STB) architecture that is suitable for the IoT; which uses blockchain sharding and oracles to establish trust among unreliable IoT devices in a fully distributed and trustworthy manner. In particular, we design a Peer-To-Peer oracle network that ensures data reliability, scalability, flexibility, and trustworthiness. Furthermore, we introduce a new lightweight consensus algorithm that scales the blockchain dramatically while ensuring the interoperability among participants of the blockchain. The results show that our proposed STB architecture achieves flexibility, efficiency, and scalability making it a promising solution that is suitable for the IoT context.

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Simulation methods, techniques and tools of computer systems and networks

Cited by 6 publications

References 51 publications

Towards a Secure and Reliable Federated Learning using Blockchain

Towards a Secure and Reliable Federated Learning using Blockchain

Prediction and Detection of FDIA and DDoS Attacks in 5G Enabled IoT

Towards a Scalable and Trustworthy Blockchain: IoT Use Case

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