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.
Detection of emerging topics are now receiving renewed interest motivated by the rapid growth of social networks. Conventional term-frequency-based approaches may not be appropriate in this context, because the information exchanged are not only texts but also images, URLs, and videos. We focus on the social aspects of theses networks. That is, the links between users that are generated dynamically intentionally or unintentionally through replies, mentions, and retweets. We propose a probability model of the mentioning behaviour of a social network user, and propose to detect the emergence of a new topic from the anomaly measured through the model. We combine the proposed mention anomaly score with a recently proposed change-point detection technique based on the Sequentially Discounting Normalized Maximum Likelihood (SDNML), or with Kleinberg's burst model. Aggregating anomaly scores from hundreds of users, we show that we can detect emerging topics only based on the reply/mention relationships in social network posts. We demonstrate our technique in a number of real data sets we gathered from Twitter. The experiments show that the proposed mention-anomaly-based approaches can detect new topics at least as early as the conventional term-frequency-based approach, and sometimes much earlier when the keyword is ill-defined.
As big data becomes more of cliche with every passing day, do you feel Internet of Things is the next marketing buzzword to grapple our lives. So what exactly is Internet of Thing (IoT) and why are we going to hear more about it in the coming days. Internet of thing (IoT) today denotes advanced connectivity of devices,systems and services that goes beyond machine to machine communications and covers a wide variety of domains and applications specifically in the manufacturing and power, oil and gas utilities. An application in IoT can be an automobile that has built in sensors to alert the driver when the tyre pressure is low. Built-in sensors on equipment's present in the power plant which transmit real time data and thereby enable to better transmission planning,load balancing.
About NVIDIA NVIDIA (NASDAQ: NVDA) is a computer technology company that has pioneered GPU-accelerated computing. It targets the world's most demanding users -- gamers, designers and scientists -- with products, services and software that power amazing experiences in virtual reality, artificial intelligence, professional visualization and autonomous cars. Certain statements in this press release including, but not limited to, statements as to: the benefits and impact of the NVIDIA Inception Program; NVIDIA's commitment to help companies related to artificial intelligence; and funding through NVIDIA's GPU Ventures Program are subject to risks and uncertainties that could cause results to be materially different than expectations. Important factors that could cause actual results to differ materially include: global economic conditions; our reliance on third parties to manufacture, assemble, package and test our products; the impact of technological development and competition; development of new products and technologies or enhancements to our existing product and technologies; market acceptance of our products or our partners' products; design, manufacturing or software defects; changes in consumer preferences or demands; changes in industry standards and interfaces; unexpected loss of performance of our products or technologies when integrated into systems; as well as other factors detailed from time to time in the reports NVIDIA files with the Securities and Exchange Commission, or SEC, including its Form 10-Q for the fiscal period ended May 1, 2016. Copies of reports filed with the SEC are posted on the company's website and are available from NVIDIA without charge.
Siddiqui, Md Amran (Oregon State University) | Fern, Alan (Oregon State University) | Wright, Ryan (Galois, Inc.) | Theriault, Alec (Galois, Inc.) | Archer, David (Galois, Inc.) | Maxwell, William (Galois, Inc.)
In this paper, we consider the problem of detecting unknown cyberattacks from audit data of system-level events. A key challenge is that different cyberattacks will have different suspicion indicators, which are not known beforehand. To address this we consider a multi-view anomaly detection framework, where multiple expert-designed ``views" of the data are created for capturing features that may serve as potential indicators. Anomaly detectors are then applied to each view and the results are combined to yield an overall suspiciousness ranking of system entities. Unfortunately, there is often a mismatch between what anomaly detection algorithms find and what is actually malicious, which can result in many false positives. This problem is made even worse in the multi-view setting, where only a small subset of the views may be relevant to detecting a particular cyberattack. To help reduce the false positive rate, a key contribution of this paper is to incorporate feedback from security analysts about whether proposed suspicious entities are of interest or likely benign. This feedback is incorporated into subsequent anomaly detection in order to improve the suspiciousness ranking toward entities that are truly of interest to the analyst. For this purpose, we propose an easy to implement variant of the perceptron learning algorithm, which is shown to be quite effective on benchmark datasets. We evaluate our overall approach on real attack data from a DARPA red team exercise, which include multiple attacks on multiple operating systems. The results show that the incorporation of feedback can significantly reduce the time required to identify malicious system entities.