# An Efficient Detection of Malware by Naive Bayes Classifier Using GPGPU

## TL;DR

This paper introduces a GPGPU-accelerated Naive Bayes classifier for malware detection, achieving up to 200x speed-up, addressing the need for rapid identification of new malware in growing datasets.

## Contribution

It presents a novel parallelized approach using GPGPU to significantly enhance malware detection speed with Naive Bayes, especially for large feature sets.

## Key findings

- Detection speed-up up to 200x with GPGPU implementation
- Classification time increases with number of features
- Effective detection of unseen malware samples

## Abstract

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.