Nawfal Turki  Obeis, Wesam  Bhaya, 
    Review of Data Mining Techniques for Malicious Detection,
    Research Journal of Applied Sciences,
    Volume 11,Issue 10,
    2016,
    Pages 942-947,
    ISSN 1815-932x,
    rjasci.2016.942.947,
    (https://makhillpublications.co/view-article.php?doi=rjasci.2016.942.947)
    Abstract: Malicious is the term used to illustrate any code in any part of a software system that is expected to bring about undesired impacts, security breaks or harm to a system. Malicious programming is outlined with a hurtful intent. Recently, malicious detectors attempt to distinguish unwanted codes by checking Application Programming Interface (API) calls using data mining techniques and/or different methods. Matching the API call utilizing data mining strategies can be utilized as a part of malicious detection systems, for example, frequent pattern, clustering, etc. In this study, a review of malicious detection system based on API calls and data mining strategies are taking into account. Each malicious sample is represented as a data of API calls to the data mining techniques. After transforming the sample that input as a simplified data based on data mining techniques, data mining matching calculations are utilized to similarity between the data tested sample and malicious API call tested samples placed in a database. In this study, a review of utilization of various data mining methods for the detection of malicious program.
    Keywords: Malicious code;malicious detection;API calls;data mining