Research Journal of Applied Sciences

Year: 2016
Volume: 11
Issue: 10
Page No. 942 - 947

Review of Data Mining Techniques for Malicious Detection

Authors : Nawfal Turki Obeis and Wesam Bhaya

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.

How to cite this article:

Nawfal Turki Obeis and Wesam Bhaya, 2016. Review of Data Mining Techniques for Malicious Detection. Research Journal of Applied Sciences, 11: 942-947.

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