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Visual Analytics of Anomalous User Behaviors: A Survey

arXiv.org Machine Learning

The increasing accessibility of data provides substantial opportunities for understanding user behaviors. Unearthing anomalies in user behaviors is of particular importance as it helps signal harmful incidents such as network intrusions, terrorist activities, and financial frauds. Many visual analytics methods have been proposed to help understand user behavior-related data in various application domains. In this work, we survey the state of art in visual analytics of anomalous user behaviors and classify them into four categories including social interaction, travel, network communication, and transaction. We further examine the research works in each category in terms of data types, anomaly detection techniques, and visualization techniques, and interaction methods. Finally, we discuss the findings and potential research directions.


A 20-Year Community Roadmap for Artificial Intelligence Research in the US

arXiv.org Artificial Intelligence

Decades of research in artificial intelligence (AI) have produced formidable technologies that are providing immense benefit to industry, government, and society. AI systems can now translate across multiple languages, identify objects in images and video, streamline manufacturing processes, and control cars. The deployment of AI systems has not only created a trillion-dollar industry that is projected to quadruple in three years, but has also exposed the need to make AI systems fair, explainable, trustworthy, and secure. Future AI systems will rightfully be expected to reason effectively about the world in which they (and people) operate, handling complex tasks and responsibilities effectively and ethically, engaging in meaningful communication, and improving their awareness through experience. Achieving the full potential of AI technologies poses research challenges that require a radical transformation of the AI research enterprise, facilitated by significant and sustained investment. These are the major recommendations of a recent community effort coordinated by the Computing Community Consortium and the Association for the Advancement of Artificial Intelligence to formulate a Roadmap for AI research and development over the next two decades.


Deep learning: the next frontier for money laundering detection

#artificialintelligence

Monitoring transactions for suspicious ones can be more efficient. Up to $2 trillion dollars representing 5% of global GDP – that's the estimated amount of money laundered worldwide each year according to the United Nations Office on Drugs and Crime. The fight against money laundering is one of top priorities of financial institutions – but it also poses a significant challenge for them. To combat the phenomenon, one needs to have a large number of human and technology resources at hand. And even then, the good guys have a hard time winning.


Argyle Data Points to Innovations in Machine Learning to Solve New Waves of Telco Fraud - insideBIGDATA

#artificialintelligence

Argyle Data, a leader in big data/machine learning analytics for mobile providers, has highlighted the role of supervised and unsupervised machine learning in detecting and preventing anomalous mobile traffic. The move comes as Argyle Data and Carnegie Mellon University (CMU) Silicon Valley's Department of Electrical and Computer Engineering prepare to publish a new research paper on anomaly detection, which will be presented at academic conferences during the first half of 2017. Global mobile fraud levels cost the industry an estimated U.S. 38 billion 2015 according to the latest CFCA survey. Most major attacks today are'fraud cocktails': unpredictable mixtures of several fraud types. The chief reason that operators are unable to detect complex new fraud is that approaches currently used to detect fraud in communications networks typically rely on static rules with pre-set thresholds, and can only detect known fraud types.


Global Big Data Conference

#artificialintelligence

In their work to unearth evidence of fraudulent activities, forensic accounting investigators dig through diverse data looking for anomalies that suggest something is just not right. But as the massive volumes of data collected by companies balloon, this task has become increasingly arduous, time-consuming and humanly impossible. The regrettable consequence is the greater chance of a well-thought-out scam slipping through the cracks. A case in point is healthcare fraud, which has been estimated to cost the United States tens of billions of dollars annually. For forensic accounting investigators, unearthing these crimes manually is an uphill climb.