M. Al-Bakri, A., L. Hussein, H. (2014). Reducing False Notification in Identifying Malicious Application Programming Interface(API) to Detect Malwares Using Artificial Neural Network with Discriminant Analysis. Alustath, 27(3), 556-565.
Abbas M. Al-Bakri; Hussein L. Hussein. "Reducing False Notification in Identifying Malicious Application Programming Interface(API) to Detect Malwares Using Artificial Neural Network with Discriminant Analysis". Alustath, 27, 3, 2014, 556-565.
M. Al-Bakri, A., L. Hussein, H. (2014). 'Reducing False Notification in Identifying Malicious Application Programming Interface(API) to Detect Malwares Using Artificial Neural Network with Discriminant Analysis', Alustath, 27(3), pp. 556-565.
M. Al-Bakri, A., L. Hussein, H. Reducing False Notification in Identifying Malicious Application Programming Interface(API) to Detect Malwares Using Artificial Neural Network with Discriminant Analysis. Alustath, 2014; 27(3): 556-565.

Reducing False Notification in Identifying Malicious Application Programming Interface(API) to Detect Malwares Using Artificial Neural Network with Discriminant Analysis

Ibn Al-Haitham Journal For Pure And Applied Science
Article 1, Volume 27, Issue 3, December 2014, Pages 556-565
Authors
Abbas M. Al-Bakri; Hussein L. Hussein
Abstract
This paper argues the accuracy of behavior based detection systems, in which the
Application Programming Interfaces (API) calls are analyzed and monitored. The work
identifies the problems that affecting the accuracy of such detection models. The work was
extracted (4744) API call through analyzing. The new approach provides an accurate
discriminator and can reveal malicious API in PE malware up to 83.2%. Results of this work
evaluated with Discriminant Analysis.
Keywords
PE Malwares; Malicious API; ANN; DISCRIMINANT ANALYSIS; Mali cious API
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