@article{MAKHILLJEAS2017122315289,
    title = {Frequency-Based Fast Algorithm for Anomaly Detection in Big Data},
    journal = {Journal of Engineering and Applied Sciences},
    volume = {12},
    number = {23},
    pages = {7389-7392},
    year = {2017},
    issn = {1816-949x},
    doi = {jeasci.2017.7389.7392},
    url = {https://makhillpublications.co/view-article.php?issn=1816-949x&doi=jeasci.2017.7389.7392},
    author = {Adeel S. and},
    keywords = {Data mining,distributed computing,parallel processing,predictive models,machine learning,tools},
    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.}
    }