Block-chain abnormal transaction detection method based on adaptive multi-feature fusion - Huijuan ZHU; Jinfu CHEN; Zhiyuan LI; Shangnan YIN (Tongxin xuebao)
Aiming at the problem that the performance of intelligent detection models was limited by the representation ability of original data (features), a residual network structure ResNet-32 was designed to automatically mine the intricate association relationship between original features, so as to actively learn the high-level abstract features with rich semantic information.Low-level features were more transaction content descriptive, although their distinguishing ability was weaker than that of the high-level features.How to integrate them together to obtain complementary advantages was the key to improve the detection performance.Therefore, multi feature fusion methods were proposed to bridge the gap between the two kinds of features.Moreover, these fusion methods can automatically remove the noise and redundant information from the integrated features and further absorb the cross information, to acquire the most distinctive features.Finally, block-chain abnormal transaction detection model (BATDet) was proposed based on the above presented methods, and its effectiveness in the abnormal transaction detection is verified.