  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%). This means
that Random Forest algorithm is efficient to be used in malware classification
based on file content and characteristics.
