An Efficient Detection of Malware by Naive Bayes Classifier Using GPGPU
This addresses the need for faster malware detection to handle increasing threats, though it is incremental as it applies an existing method with hardware acceleration.
The paper tackles the problem of efficiently detecting malware by parallelizing a Naive Bayes classifier using GPGPU, achieving a speed-up of up to 200x in detection time.
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.