Classification of malware based on file content and characteristics
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In general, the industry of malware has come to be a market which brings on loads of money by investing and implementing high end technology to escape traditional detection while vendors of anti-malware spend thousands if not millions of dollars to stop the malware breach since it not only causes financial losses but also emotional ones. This paper study the classification of malware based on file content and characteristics, this was done through use of Clamp Integrated dataset that includes 5210 instances. There are different algorithms were applied using Weka software, which are; ZeroR, bayesNet, SMO, KNN, J48, as well as Random Forest. The obtained results showed that Random Forest that achieved the highest overall accuracy of (99.0979 that Random Forest algorithm is efficient to be used in malware classification based on file content and characteristics.
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