Scalable anomaly detection in blockchain using graphics processing unit

Morishima, Shin

doi:10.1016/j.compeleceng.2021.107087

Cited by 20 publications

(3 citation statements)

References 5 publications

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“…Weili Chen et al [12] propose a graph-based cascade feature extraction method based on transaction records and a lightGBM-based Dual-sampling Ensemble algorithm to build the identification model. Shin Morishima [27] propose a structure of the subgraph is suitable for graphics processing units (GPUs) to accelerate detection for anomaly blockchain by using parallel processing.…”

Section: A Smart Contract Code Based Approach To Illegal Transaction ...mentioning

confidence: 99%

Transformer-based comparative multi-view illegal transaction detection

et al. 2023

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In recent years, as the Ether platform has grown by leaps and bounds. Numerous unscrupulous individuals have used illegal transaction to defraud large sums of money, causing billions of dollars of losses to investors worldwide. Facing the endless stream of the illegal transaction based on Ether smart contracts problems, such as illegal transaction, money laundering, financial fraud, phishing. Currently, illegal transaction are only detected by a single view of the smart contract’s contract code view feature and account transaction view feature, which is not only incomplete, but also not fully representative of the smart contract’s features. More importantly, the single view detection model cannot accurately capture the global structure and semantic features between the Tokens of the view features. In this case, it is particularly important that all view features are shared among themselves. In this paper, we investigate a Transformer-based model for contrasting illegal transaction detection networks under multiple views (TranMulti-View Net). The model in this paper is based on Transformer to learn a multi-view fusion representation, which aims to maximise the fusion of the interaction information of different view features under the same condition. In this model we first use the Transformer model to learn global structure and semantic features from a sequence of Tokens tokenised by a view, capturing the remote dependencies of Tokens in the view features, and then we share the contract code view features and the account transaction view features across all views to learn important semantic information between views from each other. In addition, we find that the approach of semi-supervised training of multi-view features using contrast learning outperforms the scheme of prediction based on direct fusion of different view features, resulting in stronger correlation between view features. As a result, the underlying semantic information can be captured more accurately, leading to more accurate predictions of illegal transaction. The experimental results show that our proposed TranMulti-View Net obtains good detection results with a Precision score of 98%.

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Section: A Smart Contract Code Based Approach To Illegal Transaction ...mentioning

confidence: 99%

Transformer-based comparative multi-view illegal transaction detection

et al. 2023

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“…Blockchain is the technology behind promoting bitcoin as a cryptocurrency, but this has caused the blockchain technology to face several attacks including double-spending and mining pool [110]. Another use of applying blockchain technology is in developing anomaly detection systems for various types of projects including electrical vehicle and bitcoin transactions, as mentioned by Signorini et al [111], Jin et al [112], and Morishima [113].…”

Section: Security Of Blockchainsmentioning

confidence: 99%

AIBPSF-IoMT: Artificial Intelligence and Blockchain-Based Predictive Security Framework for IoMT Technologies

Alshammari

2023

Electronics

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The latest advancements in artificial intelligence (AI) technologies, including machine and deep learning models, in prediction, recommending, and automating processes have greatly impacted IoT devices in general, and protect them from cyberattacks in particular. Blockchain also has features that assist in creating more secure IoT devices due to its abilities of traceability, acceptability, and trust. This paper studies the current advancements in the IoT and blockchain, their architectures, and their effect on security. The paper proposes a novel framework that takes into consideration the advantages and benefits of machine/deep learning models and blockchain in order to provide a solution that makes IoT devices more secure. This framework is based on the IoT four-layer architecture, and it aims to enhance the way IoT devices detect and recognise cyberattacks using blockchain and machine/deep learning algorithms. Machine and deep learning algorithms are responsible for detecting security attacks in the IoT, based on their patterns. The blockchain platform is responsible for verifying whether a specific request is secure, and it also uses cryptography to sign all new requests in order to recognise them in future requests. The MQTTset dataset, which is contains data associated with intrusion detection cases, has been used to implement a case study that aims to prove the validity of this framework. Various machine and deep learning algorithms have been used in this case study which have all achieved high results with regard to precision, recall, accuracy, and F1 performance measurements. Such results have proven the validity and reliability of the proposed framework to detect and predict new attacks before their requests are processed within a particular IoT system.

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“…Chen et al found that there was serious market manipulation in the Mt.Gox exchange, illustrating that the regulation of cryptocurrency exchanges needs to be strengthened. In addition, address transaction records are also used for abnormal behavior monitoring [5,7,31,32,41,57], such as money flow detection and identity tracing.…”

Section: Related Workmentioning

confidence: 99%

Who is gambling? Finding cryptocurrency gamblers using multi-modal retrieval methods

Huang

Liu

et al. 2022

Int J Multimed Info Retr

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With the popularity of cryptocurrencies and the remarkable development of blockchain technology, decentralized applications emerged as a revolutionary force for the Internet. Meanwhile, decentralized applications have also attracted intense attention from the online gambling community, with more and more decentralized gambling platforms created through the help of smart contracts. Compared with conventional gambling platforms, decentralized gambling have transparent rules and a low participation threshold, attracting a substantial number of gamblers. In order to discover gambling behaviors and identify the contracts and addresses involved in gambling, we propose a tool termed ETHGamDet. The tool is able to automatically detect the smart contracts and addresses involved in gambling by scrutinizing the smart contract code and address transaction records. Interestingly, we present a novel LightGBM model with memory components, which possesses the ability to learn from its own misclassifications. As a side contribution, we construct and release a largescale gambling dataset at https://github.com/AwesomeHuang/Bitcoin-Gambling-Dataset to facilitate future research in this field. Empirically, ETHGamDet achieves a F1-score of 0.72 and 0.89 in address classification and contract classification respectively, and offers novel and interesting insights.

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Scalable anomaly detection in blockchain using graphics processing unit

Cited by 20 publications

References 5 publications

Transformer-based comparative multi-view illegal transaction detection

Transformer-based comparative multi-view illegal transaction detection

AIBPSF-IoMT: Artificial Intelligence and Blockchain-Based Predictive Security Framework for IoMT Technologies

Who is gambling? Finding cryptocurrency gamblers using multi-modal retrieval methods

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