TY  - JOUR
T1  - Review of Data Mining Techniques for Malicious Detection
AU - Obeis, Nawfal Turki AU - Bhaya, Wesam 
JO  - Research Journal of Applied Sciences
VL  - 11
IS  - 10
SP  - 942
EP  - 947
PY  - 2016
DA  - 2001/08/19
SN  - 1815-932x
DO  - rjasci.2016.942.947
UR  - https://makhillpublications.co/view-article.php?doi=rjasci.2016.942.947
KW  - Malicious code
KW  -malicious detection
KW  -API calls
KW  -data mining
AB  - 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.
ER  -