KEYWORDS: Data modeling, Detection and tracking algorithms, Machine learning, Feature extraction, Blockchain, Random forests, Education and training, Decision trees, Neural networks, Analytical research
With the introduction of blockchain technology and the development of virtual currencies, virtual currencies, which are based on cryptography to ensure the security and anonymity of transactions, have received a lot of attention. As a new payment method that is secure, decentralized and easy to transmit, virtual currencies have also attracted a large number of illegal users and illegal transactions. In order to identify gambling transaction behaviors and gambling-related addresses in the virtual currency market, this paper proposes a gambling transaction feature extraction method based on community detection and network embedding techniques, which obtains a network vector representation of this transaction network structure by discovering a high modularity and highly structured transaction network in gambling address transactions and performing node embedding and averaging calculations based on the node2vec algorithm to complete the extraction of transaction features of gambling addresses and solve the data imbalance problem of the huge gap between the number of historical transactions of different addresses. Finally, based on the feature dataset and several classical machine learning classification algorithms, a binary classification model is trained and evaluated to identify gambling transactions and addresses. Experiments show that all classification models achieve an accuracy rate of 0.72 or higher with high quality of transaction feature data, with the lightGBM model getting the best result of 0.84 accuracy rate, as well as 0.92 and 0.87 recall and F1 scores.
In the vector operation system, each data operation will only increase or decrease the same number for each component of the vector, that is to say, the vector is regarded as a data object on the whole, and each operation is carried out on the whole. For example, translation, scaling and other operations in computer graphics. Such computing systems are currently widely used in graphics, machine learning, mathematical modeling and other fields. When the data in the vector computing system is homomorphic encrypted and uploaded to the cloud server, the problem of low vector coding efficiency and polynomial coefficient expansion caused by a large number of polynomial operations is generated. The existing SIMD coding algorithm encodes an n-dimensional vector integer into a polynomial. The number of calculations increases exponentially with the increase of n, and the coding efficiency of the vector is very low. For typical FHE schemes such as BGV, BFV and CKKS, the main performance bottleneck comes from a large number of polynomial algorithms. Specifically, encrypted data is typically composed of a pair of polynomials with coefficients of hundreds or thousands of bits, requiring expensive multiword arithmetic. In addition, large polynomial lengths increase the computational complexity. Therefore, in this paper, a vector compression coding algorithm based on SIMD(VCABSIMD) is proposed to solve the performance problem when the data in the vector computing system is homomorphically encrypted and uploaded to the cloud server for calculation. The vector coding efficiency and polynomial calculation are deeply optimized.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.