SRI Blog

-By Joana Lorenz , Maria Inês Silva, David Aparício, João Tiago Ascensão, Pedro Bizarro Feedzai Paper link                                     Git hub link Abstract Every year, criminals launder billions of dollars acquired from serious felonies (e.g., terrorism, drug smuggling, or human trafficking) harming countless people and economies. Cryptocurrencies, in particular, have developed as a haven for money laundering activity. Machine Learning can be used to detect these illicit patterns. However, labels are so scarce that traditional supervised algorithms are inapplicable. Here, we address money laundering detection assuming minimal access to labels. First, we show that existing state-of-the-art solutions using unsupervised anomaly detection methods are inadequate to detect the illicit patterns in a real Bitcoin transaction dataset. Image courtesy: BitCoin Magazine  ...

Angle based detection By  Hans-Peter Kriegel, Matthias Schubert, Arthur Zimek  Ludwig-Maximilians-Universität München  Oettingenstr. 67, 80538 München, Germany Ref Link PyOD By  Yue Zhao   Zain Nasrullah   Department of Computer Science, University of Toronto, Toronto, ON M5S 2E4, Canada  Zheng Li jk  Northeastern University Toronto, Toronto, ON M5X 1E2, Canada I am combining two papers to summarize Anomaly detection. First one is Angle Based Outlier Detection (ABOD) and other one is python module that  uses ABOD along with over 20 other apis (PyOD) . This is third part in the series of Anomaly detection. First article exhibits survey that covered length and breadth of subject, Second article highlighted on data preparation and pre-processing.  Angle Based Outlier Detection. Angles are more stable than distances in high dimensional spaces for example the popularity of cosine-based sim...

By - Siyang Lu, Xiang Wei, Yandong Li, Liqiang Wang Department of Computer Science, University of Central Florida, Orlando, FL, USA School of Software Engineering, Beijing Jiaotong University, China Paper Link Previous week we did a scan of the survey of Anomaly detection   , that covers breath of the topic (Types of Anomalies, Types of Models and their applications). In this summary paper, we look in to the core Heavy lifting part of Detecting Anomaly in Big Data System Logs Using Convolutional Neural Network. Abstract Nowadays, big data systems are being widely adopted by many domains for offering effective data solutions, such as manufacturing, healthcare, education, and media. Big data systems produce tons of unstructured logs that contain buried valuable information. However, it is a daunting task to manually unearth the information and detect system anomalies. A few automatic methods have been developed, where the cutting-edge machine lear...