Malware detection using convolutional neural network, a deep learning framework: comparative analysis

Abstract

Malware detection is a quintessential task for every security for securing work stations, mobile devices, servers etc. This detection is mainly used for identifying malware that are causing malicious problems. The traditional detection system has a much lesser rate of detection rate and the chances of getting an error is higher as well. As the emerging technology revolutionized day by day, the usage of Deep Learning (DL) is highly influenced in these detection fields. So, this paper brings an effective DL based detection of malware in which the following are the stages: a) Data collection being carried from Malimg dataset, b) Pre-processing carried out to eliminate the unwanted noise from the dataset and passed to c) Feature extraction, where Principal Component Analysis (PCA) used for extracting required features, d) Feature selection where Particle Swarm Optimization (PSO) used for dimensionality reduction and finally passed for e) Classification where Convolutional Neural Network (CNN) used as a classifier for effective classification. These models are evaluated under measures like Accuracy, sensitivity, specificity, precision, recall, f1-score, TPR, FPR and detection rate over models like VGG16, VGG19, Densenet, Alexnent, Ensemble learning. The proposed system (D-WARE) gives much higher performance with a 96% accuracy