Adeel S.  Hashmi, Tanvir  Ahmad, 
    Frequency-Based Fast Algorithm for Anomaly Detection in Big Data,
    Journal of Engineering and Applied Sciences,
    Volume 12,Issue 23,
    2017,
    Pages 7389-7392,
    ISSN 1816-949x,
    jeasci.2017.7389.7392,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2017.7389.7392)
    Abstract: Anomaly/outlier detection is an important area of machine learning which finds its application in
intrusion-detection, fraud-detection, etc. In recent times, the focus of data analytics has shifted to big data
analytics, i.e., analytics on large-scale data and fast-moving data streams. The traditional data processing tools
and algorithms are not able to handle big data, so, there is a need of algorithms to be implemented in a parallel
model like MapReduce to solve this problem. In this study, the researchers implement frequency-based
algorithm on Spark MapReduce as a scalable and accurate solution for anomaly detection on large-scale as well
as streaming datasets.
    Keywords: Data mining;distributed computing;parallel processing;predictive models;machine learning;tools