Privacy-preserving blockchain-based federated learning for traffic flow prediction

Blockchains are distributed systems, in which security is a critical factor for their success. However, despite their increasing popularity and adoption, there is a lack of standardized models that study blockchain-related security threats. To fill this gap, the main focus of our work is to systematize and extend the knowledge about the security and privacy aspects of blockchains and contribute to the standardization of this domain. 
We propose the security reference architecture (SRA) for blockchains, which adopts a stacked model (similar to the ISO/OSI) describing the nature and hierarchy of various security and privacy aspects. The SRA contains four layers: (1) the network layer, (2) the consensus layer, (3) the replicated state machine layer, and (4) the application layer. At each of these layers, we identify known security threats, their origin, and countermeasures, while we also analyze several cross-layer dependencies. Next, to enable better reasoning about security aspects of blockchains by the practitioners, we propose a blockchain-specific version of the threat-risk assessment standard ISO/IEC 15408 by embedding the stacked model into this standard. Finally, we provide designers of blockchain platforms and applications with a design methodology following the model of SRA and its hierarchy.

The Security Reference Architecture for Blockchains: Towards a Standardized Model for Studying Vulnerabilities, Threats, and Defenses

This work investigates the unified coding and identification of smart grid IoT devices, as more and more IoT devices in smart grid need to be managed and controlled. We combine blockchain technology with 5G MEC to realize the connection of massive power IoT devices at the edge of 5G network. Due to blockchain’s distributed storage and credibility, it is used to identify and register IoT devices in smart grid, ensuring the reliability and accuracy of smart grid IoT devices management. In this paper, we propose a hybrid blockchain mechanism based on 5G MEC smart grid, where both public blockchain and private blockchain are deployed on the MEC gateway/server. To facilitate the data searching and extracting, we endeavor to build a blockchain explorer indexed by IoT device identifier. After that, we study the typical consensus algorithms in the blockchain such as PoW, PoS, DPoS, PDFT, and discuss their feasibility in the hybrid blockchain. Finally, we analyzed and compared the performance of different consensus algorithms from the perspective of average computing time and average time to agreement.

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Blockchain-based IoT device identification and management in 5G smart grid

The development of smart network infrastructure of the Internet of Things (IoT) faces the immense threat of sophisticated Distributed Denial-of-Services (DDoS) security attacks. The existing network security solutions of enterprise networks are significantly expensive and unscalable for IoT. The integration of recently developed Software Defined Networking (SDN) reduces a significant amount of computational overhead for IoT network devices and enables additional security measurements. At the prelude stage of SDN-enabled IoT network infrastructure, the sampling based security approach currently results in low accuracy and low DDoS attack detection. In this paper, we propose an Adaptive Machine Learning based SDN-enabled Distributed Denial-of-Services attacks Detection and Mitigation (AMLSDM) framework. The proposed AMLSDM framework develops an SDN-enabled security mechanism for IoT devices with the support of an adaptive machine learning classification model to achieve the successful detection and mitigation of DDoS attacks. The proposed framework utilizes machine learning algorithms in an adaptive multilayered feed-forwarding scheme to successfully detect the DDoS attacks by examining the static features of the inspected network traffic. In the proposed adaptive multilayered feed-forwarding framework, the first layer utilizes Support Vector Machine (SVM), Naive Bayes (NB), Random Forest (RF), k-Nearest Neighbor (kNN), and Logistic Regression (LR) classifiers to build a model for detecting DDoS attacks from the training and testing environment-specific datasets. The output of the first layer passes to an Ensemble Voting (EV) algorithm, which accumulates the performance of the first layer classifiers. In the third layer, the adaptive frameworks measures the real-time live network traffic to detect the DDoS attacks in the network traffic. The proposed framework utilizes a remote SDN controller to mitigate the detected DDoS attacks over Open Flow (OF) switches and reconfigures the network resources for legitimate network hosts. The experimental results show the better performance of the proposed framework as compared to existing state-of-the art solutions in terms of higher accuracy of DDoS detection and low false alarm rate.

/pdf/adaptive-machine-learning-based-distributed-denial-of-e0itceav.pdf

Adaptive Machine Learning Based Distributed Denial-of-Services Attacks Detection and Mitigation System for SDN-Enabled IoT

Blockchains are distributed systems, in which security is a critical factor for their success. However, despite their increasing popularity and adoption, there is a lack of standardized models that study blockchain-related security threats. To fill this gap, the main focus of our work is to systematize and extend the knowledge about the security and privacy aspects of blockchains and contribute to the standardization of this domain.We propose the security reference architecture (SRA) for blockchains, which adopts a stacked model (similar to the ISO/OSI) describing the nature and hierarchy of various security and privacy aspects. The SRA contains four layers: (1) the network layer, (2) the consensus layer, (3) the replicated state machine layer, and (4) the application layer. At each of these layers, we identify known security threats, their origin, and countermeasures, while we also analyze several cross-layer dependencies. Next, to enable better reasoning about security aspects of blockchains by the practitioners, we propose a blockchain-specific version of the threat-risk assessment standard ISO/IEC 15408 by embedding the stacked model into this standard. Finally, we provide designers of blockchain platforms and applications with a design methodology following the model of SRA and its hierarchy.

The Security Reference Architecture for Blockchains: Toward a Standardized Model for Studying Vulnerabilities, Threats, and Defenses

Powered by a number of smart devices distributed throughout the whole network, the Internet of Things (IoT) is supposed to provide services computing for massive data from devices. Fog computing, an extension of cloud-based IoT-oriented solutions, has emerged with requirements for distribution and decentralization. In this respect, the conjunction with Blockchain provides a natural solution for decentralization, as well as potentially helps fog computing overcome some deficiencies such as security and privacy then consequently expand the application scope of IoT. However, one of the key challenges of blockchain's integration with fog computing is scalability. To end this, this work proposes Groupchain, a novel scalable public blockchain of a two-chain structure suitable for fog computing of IoT services computing. Groupchain employs the leader group to collectively commit blocks for higher transaction efficiency and introduces bonus and deposit into the incentive mechanism to supervise behaviors of members in the leader group. Our security analysis shows that Groupchain retains the security of Bitcoin-like blockchain and enhances defense against attacks such as double-spend and selfish mining. We implement a prototype of Groupchain and conducted experiments. The experimental results demonstrate that Groupchain achieves optimization on transaction throughput and confirmation latency which are argued in Bitcoin.

Groupchain: Towards a Scalable Public Blockchain in Fog Computing of IoT Services Computing

Recent advances in artificial intelligence, big data, mobile edge computing and embedded systems have successfully driven the emergence and adoption of smart vehicles and vehicle edge computing which will improve road safety, traffic congestions, and vehicle exhaust emissions. The high-mobility, ad-hoc network topology, and diverse vehicle-to-everything (V2X) have brought substantial challenges in the TCP/IP-based vehicular networking. Given the unique characteristics and strengths in resilient communication in mobile ad hoc networking environments, named data networking (NDN) has become a natural fit for supporting vehicular edge computing (VEC) as the underlying network architecture. However, a variety of security and privacy challenges remain for developing NDN-based VEC networks such as key management, cache poisoning, access control. In this paper, we introduce a novel blockchain-based security architecture in NDN-based VEC networks to systematically tackle these security challenges. More specifically, we design and implement an efficient blockchain system on NDN by adopting lightweight yet robust delegate consensus algorithm, and carry out extensive experiments to evaluate performance efficiency on key management protocols, cache poisoning defense schemes, and access control strategies for NDN-based VEC networks. To the best of our knowledge, this paper is the first effort to systematically devise practical and efficient blockchain-based security architecture to provide key management, cache poisoning security protection, and privacy-aware access control in NDN VEC networks.

Blockchain-Based Cache Poisoning Security Protection and Privacy-Aware Access Control in NDN Vehicular Edge Computing Networks

The current research on the blockchain includes study on network architecture and the incentive. In this paper, an introduction to the architecture of information technology was given and the goal and research status of the incentive layer of blockchain were illustrated with digital economy development as the backdrop. The existing issuance of token was elaborated from computation, storage and transmission, three core facets of network technology. The development of the token allocation and the path was analyzed to confirm its rationality. It discusses the possible directions and challenges of the future research on blockchain incentive.

Survey on Blockchain Incentive Mechanism

The last decade has witnessed the explosive growth of digital tokens and cryptocurrencies including Bitcoin, reward programs and various tokens on Ethereum. However, trading these heterogeneous and non-financial digital assets often encounters substantial liquidity barriers due to the lack of interoperable standards, and faces an equilibrium dilemma in pricing strategies with a risk of market disequilibrium. In this sense, we introduce a novel decentralized equilibrium trading mechanism for assets across value systems with a combination of blockchain and economic theories that optimizes storage, equilibrium, and circulation of assets. To demonstrate the practical feasibility of the proposed mechanism, we design, implement, and evaluate an end-to-end prototype system on Ethereum platform with carefully-crafted smart contracts. Our extensive experimental evaluations and the trading simulation suggest that the proposed mechanism settles exchanges and tradings among different assets with equilibrium prices in a decentralized fashion, providing both practical and theoretical foundation for the autonomous equilibrium of asset trading market without human interventions.

Towards Decentralized Equilibrium Asset Trading Based on Blockchain

Confronted with the explosive growth of data in various cyber systems, decentralized storage has gained popularity thanks to its ubiquitousness, since the devices can share their idle storage space for higher scalability. However, current decentralized storage networks suffer the failure risk due to the insufficient trust among nodes, in addition to the lack of a reliable resource allocation mechanism. In this paper, we present a novel trusted shared storage network, namely TSSN, to achieve efficacious decentralized storage with trust. Designed under a shared storage framework, TSSN introduces blockchain via an on/off-chain collaborative protocol, which realizes the decentralized trust with promising efficiency. In the primary novelty of TSSN, we design an allocation mechanism based on trading and multi-attribute bilateral matching to maintain the stability and fluidity of resources. To verify the practical effectiveness of our solution, we implement a TSSN prototype on our testbed based on IPFS and Ethereum. The experimental results demonstrate the performance of TSSN within on-chain storage overhead, throughput, and latency.

Maoyu Du

Papers

Groupchain: Towards a Scalable Public Blockchain in Fog Computing of IoT Services Computing

Blockchain-Based Cache Poisoning Security Protection and Privacy-Aware Access Control in NDN Vehicular Edge Computing Networks

Survey on Blockchain Incentive Mechanism

Towards Decentralized Equilibrium Asset Trading Based on Blockchain

TSSN: A Trusted Shared Storage Network Based on Blockchain and Bilateral Matching