DataVisor's CEO Yinglian Xie is interviewed by vpnMentor to discuss how DataVisor uses Multi-Dimensional Algorithms to detect Fraudulent User Accounts before they launch malicious attacks.
The DataVisor report provides the latest insights into trends in global fraud activity and attack techniques. Last quarter, we uncovered a total of 900 million malicious activities and transactions. The report reveals dramatic growth in fraud infrastructure, with fraudulent accounts growing by 50% from Q4 2017 to Q1 2018.
DataVisor's Yuhao Zheng and Boduo Li share advanced techniques for managing thousands of spark workers to analyze billions of events at a time, including clustering workers and automated, optimized management of DataVisor's spark infrastructure.
DataVisor's Ting-Fang Yen and Arthur Meng present a novel deep learning technique for scalable online fraud detection among billions of users.
DataVisor is honored to be named as a Top 25 Tech Company to Watch 2018 by the Wall Street Journal for it's machine-learning, big-data fraud detection solution.
Does the fact that UML doesn’t require labels mean that there is no benefit at all to labels? If label data exists already, how can it be used to improve UML detection results? In this article we discuss how labels can be effectively used in UML detection, even if they are not required.
Unsupervised Machine Learning (UML) is a topic that we get a lot of questions about here at DataVisor, because UML is at the core of our detection platform. In this 5-minute primer on UML, we start by defining the overarching field of Artificial Intelligence, then we drill down to the sub-field of Machine Learning, and lastly we discuss the various machine learning techniques, including UML, and when each ML technique is most effective.
With online criminals getting highly skilled at attacking enterprises every day, it’s getting difficult for businesses to tell the difference between legitimate and fraudulent activity. Learn how UML can detect threats before they are known.