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


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.

Cybersecurity Data Science: Minding the Growing Gap - DATAVERSITY


Click to learn more about author Scott Mongeau. Following cybersecurity Data Science best practices can help beleaguered and resource-strapped security teams transform Big Data into smart data for better anomaly detection and enterprise protection. The consequences of ignoring security challenges are rising. According to the Cisco 2018 Annual Cybersecurity Report, over half of cyberattacks resulted in damages of greater than $500K, with nearly 20 percent costing more than $2.5M. Meanwhile regulators, seeking to spur heightened oversight, have become more aggressive in levying fines and holding corporate boards accountable.

23-bit Metaknowledge Template Towards Big Data Knowledge Discovery and Management Artificial Intelligence

The global influence of Big Data is not only growing but seemingly endless. The trend is leaning towards knowledge that is attained easily and quickly from massive pools of Big Data. Today we are living in the technological world that Dr. Usama Fayyad and his distinguished research fellows discussed in the introductory explanations of Knowledge Discovery in Databases (KDD) predicted nearly two decades ago. Indeed, they were precise in their outlook on Big Data analytics. In fact, the continued improvement of the interoperability of machine learning, statistics, database building and querying fused to create this increasingly popular science- Data Mining and Knowledge Discovery. The next generation computational theories are geared towards helping to extract insightful knowledge from even larger volumes of data at higher rates of speed. As the trend increases in popularity, the need for a highly adaptive solution for knowledge discovery will be necessary. In this research paper, we are introducing the investigation and development of 23 bit-questions for a Metaknowledge template for Big Data Processing and clustering purposes. This research aims to demonstrate the construction of this methodology and proves the validity and the beneficial utilization that brings Knowledge Discovery from Big Data.

Credit Card Fraud Detection in e-Commerce: An Outlier Detection Approach Machine Learning

Often the challenge associated with tasks like fraud and spam detection is the lack of all likely patterns needed to train suitable supervised learning models. This problem accentuates when the fraudulent patterns are not only scarce, they also change over time. Change in fraudulent pattern is because fraudsters continue to innovate novel ways to circumvent measures put in place to prevent fraud. Limited data and continuously changing patterns makes learning significantly difficult. We hypothesize that good behavior does not change with time and data points representing good behavior have consistent spatial signature under different groupings. Based on this hypothesis we are proposing an approach that detects outliers in large data sets by assigning a consistency score to each data point using an ensemble of clustering methods. Our main contribution is proposing a novel method that can detect outliers in large datasets and is robust to changing patterns. We also argue that area under the ROC curve, although a commonly used metric to evaluate outlier detection methods is not the right metric. Since outlier detection problems have a skewed distribution of classes, precision-recall curves are better suited because precision compares false positives to true positives (outliers) rather than true negatives (inliers) and therefore is not affected by the problem of class imbalance. We show empirically that area under the precision-recall curve is a better than ROC as an evaluation metric. The proposed approach is tested on the modified version of the Landsat satellite dataset, the modified version of the ann-thyroid dataset and a large real world credit card fraud detection dataset available through Kaggle where we show significant improvement over the baseline methods.

The Hunt for the 'Goldilocks Zone' in Tech and Data Policy - InformationWeek


There is some truth in the notion that the enterprise precedes consumer technology breakthroughs, but there are shared concerns about policy found in both communities. At the CES Unveiled preview event held in New York, the Consumer Technology Association gave its usual glimpse of gadgets expected to be in demand in the coming year however legislative matters colored the conversation. Gary Shapiro, president and CEO of the CTA, spoke about his concerns and observations of the current climate surrounding the entire technology sphere and the effect proposed policies might have. CES Unveiled is the preamble to the annual CES trade conference held in January in Las Vegas. While CES is primarily a showcase for new televisions, smart devices, cloud-based gaming, and tricked out automated cars, there is some crosspollination with elements of enterprise technology with planned discussions on innovation policy, privacy, and transformation across industries.