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.

Can fear of failure predict academic procrastination? A study of Indonesian university students

This study aims to determine the ability of fear of failure in predicting academic procrastination in students in Indonesia by involving 428 respondents (male = 52.63%). The instruments used are the academic procrastination scale and the fear of failure scale. Analysis of this research data uses a simple linear regression technique. This study showed that fear of failure affected academic procrastination significantly with a contribution value of 38.6% positive direction, while 61.4% was influenced by other factors not studied. Research confirms that students with high levels of fear of failure tend to have high levels of academic procrastination and vice versa

Morphodynamic limits to environmental signal propagation across landscapes and into strata

The sedimentary record contains unique information about landscape response to environmental forcing at timescales that far exceed landscape observations over human timescales. However, stochastic processes can overprint and shred evidence of environmental signals, such as sediment flux signals, and so inhibit their transfer to strata. Our community currently lacks a quantitative framework to differentiate between environmental signals and autogenic signals in field-scale analysis of strata. Here we develop a framework and workflow to estimate autogenic thresholds for ancient sediment routing systems. Crucially these thresholds can be approximated using measurements that are readily attainable from field systems, circumventing the low temporal resolution offered by strata. This work demonstrates how short-term system dynamics can be accessed from ancient sediment routing systems to place morphodynamic limits on environmental signal propagation across ancient landscapes and into strata.

A large scale study of reader interactions with images on Wikipedia

Wikipedia is the largest source of free encyclopedic knowledge and one of the most visited sites on the Web. To increase reader understanding of the article, Wikipedia editors add images within the text of the article’s body. However, despite their widespread usage on web platforms and the huge volume of visual content on Wikipedia, little is known about the importance of images in the context of free knowledge environments. To bridge this gap, we collect data about English Wikipedia reader interactions with images during one month and perform the first large-scale analysis of how interactions with images happen on Wikipedia. First, we quantify the overall engagement with images, finding that one in 29 pageviews results in a click on at least one image, one order of magnitude higher than interactions with other types of article content. Second, we study what factors associate with image engagement and observe that clicks on images occur more often in shorter articles and articles about visual arts or transports and biographies of less well-known people. Third, we look at interactions with Wikipedia article previews and find that images help support reader information need when navigating through the site, especially for more popular pages. The findings in this study deepen our understanding of the role of images for free knowledge and provide a guide for Wikipedia editors and web user communities to enrich the world’s largest source of encyclopedic knowledge.