  Due to continuous increase in the number of malware (according to AV-Test
institute total ~8 x 10^8 malware are already known, and every day they
register ~2.5 x 10^4 malware) and files in the computational devices, it is
very important to design a system which not only effectively but can also
efficiently detect the new or previously unseen malware to prevent/minimize the
damages. Therefore, this paper presents a novel group-wise approach for the
efficient detection of malware by parallelizing the classification using the
power of GPGPU and shown that by using the Naive Bayes classifier the detection
speed-up can be boosted up to 200x. The investigation also shows that the
classification time increases significantly with the number of features.
