Nawfal Turki  Obeis, Wesam  Bhaya, 
    A Survey on Association Rule Mining Approaches for Malicious Detection,
    Journal of Engineering and Applied Sciences,
    Volume 12,Issue 21,
    2017,
    Pages 5394-5398,
    ISSN 1816-949x,
    jeasci.2017.5394.5398,
    (https://makhillpublications.co/view-article.php?doi=jeasci.2017.5394.5398)
    Abstract: The quality of malicious detector is determined by the technique it uses. The extracting of interest
and useful knowledge from huge data called data mining. It uses with many aspects of clustering, classification,
association rule mining, frequent pattern mining, etc. Association rule mining is a significant technique to finds
interesting relationships among items in various datasets. Recently, association rule discovery has turned to
important topics in data mining with malicious detection. It attracts extracares because of its varied usability.
The association rule mining is normally worked by generating of frequent itemsets and rules in which many
researchers provided many effective algorithms. To discover these rules, it needs to find frequent itemsets.
Based on these frequent itemsets, it can build blocks of association rules with a given support and confidence
factors. Here in this study, a survey on association rule algorithms will be present. At the beginning, we present
the concepts of association rules and some of the related research works which done on it. Then, a discussion
of the limitations and advantages of association rule algorithms will provide.
    Keywords: Association rule;malicious detection;frequent pattern mining;data mining;survey;itemsets