BAD: A Blockchain Anomaly Detection Solution

Signorini, Matteo; Pontecorvi, Matteo; Kanoun, Waël; Pietro, Roberto Di

doi:10.1109/access.2020.3025622

Cited by 43 publications

(24 citation statements)

References 38 publications

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“…Another approach was to keep them on a separate blockchain while still being part of the network. Further, an anomaly detection system (ADS) was implemented based on the assumption that a similar kind of attack may occur, but on different nodes in the blockchain network [32]. This system does not discard information about orphans and forks that are usually done by other ADS.…”

Section: A Detectionmentioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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In recent years, the emergence of blockchain technology (BT) has become a unique, most disruptive, and trending technology. The decentralized database in BT emphasizes data security and privacy. Also, the consensus mechanism in it makes sure that data is secured and legitimate. Still, it raises new security issues such as majority attack and double-spending. To handle the aforementioned issues, data analytics is required on blockchain based secure data. Analytics on these data raises the importance of arisen technology Machine Learning (ML). ML involves the rational amount of data to make precise decisions. Data reliability and its sharing are very crucial in ML to improve the accuracy of results. The combination of these two technologies (ML and BT) can provide highly precise results. In this paper, we present a detailed study on ML adoption for making BT-based smart applications more resilient against attacks. There are various traditional ML techniques, for instance, Support Vector Machines (SVM), clustering, bagging, and Deep Learning (DL) algorithms such as Convolutional Neural Network (CNN) and Long short-term memory (LSTM) can be used to analyse the attacks on a blockchain-based network. Further, we include how both the technologies can be applied in several smart applications such as Unmanned Aerial Vehicle (UAV), Smart Grid (SG), healthcare, and smart cities. Then, future research issues and challenges are explored. At last, a case study is presented with a conclusion. INDEX TERMS Blockchain, machine learning, smart grid, data security and privacy, data analytics, smart applications.

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Section: A Detectionmentioning

confidence: 99%

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

et al. 2020

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“…Signorini et al [21] proposed a blockchain-based anomaly detection method (BAD). BAD is a complete framework that relies on several components that utilize its core blockchain metadata to collect potentially malicious activities.…”

Section: Smart Grid Combined With Blockchain-related Workmentioning

confidence: 99%

Smart Grid Nontechnical Loss Detection Based on Power Gateway Consortium Blockchain

Wang

Liu

et al. 2021

Security and Communication Networks

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The rapid development of the smart grid brings convenience to human beings. It enables users to know the real-time power supply capacity, the power quality, and the electricity price fluctuation of the grid. However, there are still some threats in the smart grid, which increase all kinds of expenses in the grid and cause great trouble to energy distribution. Among them, the man-made nontechnical loss (NTL) problem is particularly prominent. Recently, there are also some NTL detection programs. However, most of the schemes need huge amounts of supporting data and high labor costs. As a result, the NTL problem has not been well solved. In order to better avoid these risks, problems such as tampering of smart meter energy data, bypassing the smart meter directly connected to the grid, and imbalance between revenue and expenditure of the smart grid are tackled, and the threat scene of NTL is constructed. A hierarchical grid gateway blockchain is proposed and designed, and a new decentralized management MDMS system is constructed. The intelligent contract combined with the elliptic curve encryption technology is used to detect the storage and the acquisition of power data, and the detection of NTL problems is realized. At the same time, it has a certain ability to resist attacks such as replay, monitoring, and tampering. We tested the time consumption and throughput of this method on Hyperledger Fabric. At the same time, eight indexes of other methods proposed in the literature are compared. This method has a good effect.

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“…NOKIA Bell Labs, proposed the first solution, Blockchain Anomaly Detection (BAD) for detecting anomalies in blockchainbased systems. Much study has been done on blockchainenabled sustainable manufacturing in Industry 4.0 from a technical, commercial, organizational, and operational standpoint [32].…”

Section: Related Workmentioning

confidence: 99%

Minority Resampling Boosted Unsupervised Learning With Hyperdimensional Computing for Threat Detection at the Edge of Internet of Things

et al. 2021

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The Internet of Things (IoT) has rapidly transformed digital environments across a multitude of domains with increased connectivity and pervasive virtualization. The distributed computing paradigm of Edge Computing has been postulated to overcome the concerns of response time, bandwidth, energy consumption, and cybersecurity. In comparison to the other concerns, limited studies have focused on cybersecurity, mainly due to the inherent complexity of threat detection at the Edge. However, the widespread adoption of IoT applications in economic, social, and political contexts is a stringent indication of the significant impact from cyber-attacks. This paper aims to address this challenge by presenting an effective and efficient machine learning approach for threat detection at the Edge of IoT. The novel contributions of this approach are, a new Enhanced Geometric Synthetic Minority Oversampling Technique (EG-SMOTE) algorithm to resolve the imbalanced distribution of data streams at the IoT Edge, an extension to the Growing Self Organizing Map (GSOM) algorithm based on Hyperdimensional Computing for energy efficient machine learning from unlabeled data streams. The proposed EG-SMOTE + GSOM approach has been tested using four open access datasets; three benchmark, KDD99 (F-Score = 0.9360), NSL-KDD (F-Score = 0.9647), CICIDS2017 (F-Score = 0.9999), and one industry-focused botnet IoT traffic dataset, BoT-IoT (F-Score = 0.9445). The EG-SMOTE approach has outperformed SMOTE and G-SMOTE approaches in a vast number of experiments that are tried with different classifiers. The results of these experiments confirm the novelty, efficiency and effectiveness of this approach for cybersecurity at the IoT Edge.

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BAD: A Blockchain Anomaly Detection Solution

Cited by 43 publications

References 38 publications

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Machine Learning Adoption in Blockchain-Based Smart Applications: The Challenges, and a Way Forward

Smart Grid Nontechnical Loss Detection Based on Power Gateway Consortium Blockchain

Minority Resampling Boosted Unsupervised Learning With Hyperdimensional Computing for Threat Detection at the Edge of Internet of Things

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