  Cyber security threats have been growing significantly in both volume and
sophistication over the past decade. This poses great challenges to malware
detection without considerable automation. In this paper, we have proposed a
novel approach by extending our recently suggested artificial neural network
(ANN) based model with feature selection using the principal component analysis
(PCA) technique for malware detection. The effectiveness of the approach has
been successfully demonstrated with the application in PDF malware detection. A
varying number of principal components is examined in the comparative study.
Our evaluation shows that the model with PCA can significantly reduce feature
redundancy and learning time with minimum impact on data information loss, as
confirmed by both training and testing results based on around 105,000
real-world PDF documents. Of the evaluated models using PCA, the model with 32
principal feature components exhibits very similar training accuracy to the
model using the 48 original features, resulting in around 33% dimensionality
reduction and 22% less learning time. The testing results further confirm the
effectiveness and show that the model is able to achieve 93.17% true positive
rate (TPR) while maintaining the same low false positive rate (FPR) of 0.08% as
the case when no feature selection is applied, which significantly outperforms
all evaluated seven well known commercial antivirus (AV) scanners of which the
best scanner only has a TPR of 84.53%.
