Biblio

Anomaly detection is one of the research hotspots in Bitcoin transaction data analysis. In view of the existing research that only considers the transaction as an isolated node when extracting features, but has not yet used the network structure to dig deep into the node information, a bitcoin abnormal transaction detection method that combines the node’s own features and the neighborhood features is proposed. Based on the formation mechanism of the interactive relationship in the transaction network, first of all, according to a certain path selection probability, the features of the neighbohood nodes are extracted by way of random walk, and then the node’s own features and the neighboring features are fused to use the network structure to mine potential node information. Finally, an unsupervised detection algorithm is used to rank the transaction points on the constructed feature set to find abnormal transactions. Experimental results show that, compared with the existing feature extraction methods, feature fusion improves the ability to detect abnormal transactions.

In this thesis, an modified algorithm for grey wolf optimization in swarm intelligence optimization algorithm is proposed, which is called an adaptive grey wolf algorithm (AdGWO) based on population system and bacterial foraging optimization algorithm (BFO). In view of the disadvantages of premature convergence and local optimization in solving complex optimization problems, the AdGWO algorithm uses a three-stage nonlinear change function to simulate the decreasing change of the convergence factor, and at the same time integrates the half elimination mechanism of the BFO. These improvements are more in line with the actual situation of natural wolves. The algorithm is based on 23 famous test functions and compared with GWO. Experimental results demonstrate that this algorithm is able to avoid sinking into the local optimum, has good accuracy and stability, is a more competitive algorithm.