A Large-scale Empirical Analysis of Ransomware Activities in Bitcoin

Exploiting the anonymous mechanism of Bitcoin, ransomware activities demanding ransom in bitcoins have become rampant in recent years. Several existing studies quantify the impact of ransomware activities, mostly focusing on the amount of ransom. However, victims’ reactions in Bitcoin that can well reflect the impact of ransomware activities are somehow largely neglected. Besides, existing studies track ransom transfers at the Bitcoin address level, making it difficult for them to uncover the patterns of ransom transfers from a macro perspective beyond Bitcoin addresses. In this article, we conduct a large-scale analysis of ransom payments, ransom transfers, and victim migrations in Bitcoin from 2012 to 2021. First, we develop a fine-grained address clustering method to cluster Bitcoin addresses into users, which enables us to identify more addresses controlled by ransomware criminals. Second, motivated by the fact that Bitcoin activities and their participants already formed stable industries, such as Darknet and Miner , we train a multi-label classification model to identify the industry identifiers of users. Third, we identify ransom payment transactions and then quantify the amount of ransom and the number of victims in 63 ransomware activities. Finally, after we analyze the trajectories of ransom transferred across different industries and track victims’ migrations across industries, we find out that to obscure the purposes of their transfer trajectories, most ransomware criminals (e.g., operators of Locky and Wannacry) prefer to spread ransom into multiple industries instead of utilizing the services of Bitcoin mixers. Compared with other industries, Investment is highly resilient to ransomware activities in the sense that the number of users in Investment remains relatively stable. Moreover, we also observe that a few victims become active in the Darknet after paying ransom. Our findings in this work can help authorities deeply understand ransomware activities in Bitcoin. While our study focuses on ransomware, our methods are potentially applicable to other cybercriminal activities that have similarly adopted bitcoins as their payments.

Space-time clustering-based method to optimize shareability in real-time ride-sharing

Real-time ride-sharing has become popular in recent years. However, the underlying optimization problem for this service is highly complex. One of the most critical challenges when solving the problem is solution quality and computation time, especially in large-scale problems where the number of received requests is huge. In this paper, we rely on an exact solving method to ensure the quality of the solution, while using AI-based techniques to limit the number of requests that we feed to the solver. More precisely, we propose a clustering method based on a new shareability function to put the most shareable trips inside separate clusters. Previous studies only consider Spatio-temporal dependencies to do clustering on the mobility service requests, which is not efficient in finding the shareable trips. Here, we define the shareability function to consider all the different sharing states for each pair of trips. Each cluster is then managed with a proposed heuristic framework in order to solve the matching problem inside each cluster. As the method favors sharing, we present the number of sharing constraints to allow the service to choose the number of shared trips. To validate our proposal, we employ the proposed method on the network of Lyon city in France, with half-million requests in the morning peak from 6 to 10 AM. The results demonstrate that the algorithm can provide high-quality solutions in a short time for large-scale problems. The proposed clustering method can also be used for different mobility service problems such as car-sharing, bike-sharing, etc.

PENGELOMPOKAN TITIK GEMPA DI PULAU SULAWESI MENGGUNAKAN ALGORITMA ST-DBSCAN (Spatio Temporal-Density Based Spatial Clustering Application with Noise)

Earthquake is a natural disaster which is quite serious in Indonesia, especially on Sulawesi Island. Earthquake is fearful because it can’t be predicted when it will come, where it will come, and how strong the vibration, that often causes fatal damage and casualties. In effort to minimize losses caused by earthquake, it is necessary to divide areas which are easily affected by earthquake. One of the methods that can be used in dividing the area is by using the clustering technique. This research by using a clustering method with the ST-DBSCAN (Spatial Temporal-Density Based Spatial Clustering Application with Noise) algorithm on dataset of earthquake points in Sulawesi Island in 2019. This method by using the spatial distance parameters (Eps1 = 0.45), the temporal distance parameters (Eps2 = 7), and minimum number of cluster members (MinPts = 4), resulting in a total of 60 clusters with 8 large clusters and 216 noises

A Fast Density Peak Clustering Method with Autoselect Cluster Centers

Aiming at density peaks clustering needs to manually select cluster centers, this paper proposes a fast new clustering method with auto-select cluster centers. Firstly, our method groups the data and marks each group as core or boundary groups according to its density. Secondly, it determines clusters by iteratively merging two core groups whose distance is less than the threshold and selects the cluster centers at the densest position in each cluster. Finally, it assigns boundary groups to the cluster corresponding to the nearest cluster center. Our method eliminates the need for the manual selection of cluster centers and improves clustering efficiency with the experimental results.

Detection of Children’s Molars Based on Noise Filter

Pit and fissure sealant is for children aged seven to twelve years to prevent molars from becoming caries. In this paper, we propose a new detection framework to identify whether children need pit and fissure sealing. We divide the framework into two parts: molar detection and molar classification. According to the characteristics of teeth, we propose to use the clustering method to filter the bounding box in the object detection part. In the region divided by clustering, we only keep one detection frame in the same category. In the classification part, we propose a noise filtering layer based on wavelet transform for feature extraction. We map the training samples to another space in the training process based on metric learning to increase the distance between categories and improve the accuracy of classification.