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Enhancing Malware Detection using Innate Immunization


Mohamed Ahmed Mohamed Ali, Mohd Aizaini Maarof


Vol. 13  No. 10  pp. 74-77


The massive amount of malware created everyday made the process of malware detection is a significant process to protect data and systems. The methods used are varying from signature based to behavior based, and from static to dynamic detection. Detection accuracy is the main obstacles facing the researchers in this field. Artificial immune system is one of the methods used frequently these days because of its ability to simulate the human immune system and take advantage of its strength in the detection of diseases. In this paper we introduce a dynamic hybrid signature-behavior base model by applying the innate immune system to enhance the detection accuracy. The proposed model is using the portable executable (PE) file representation and API call logs extracted from windows environment because of the wide spread of this type of files in different platforms. The results show that the proposed model accomplishes a better performance in detection of known malware, new unknown malware and polymorphic malware.


Malware detection; artificial immune system; innate immune system