New Approach to Malware Detection Using Optimized Convolutional Neural Network

Abstract

Cyber-crimes have become a multi-billion-dollar industry in the recent years. Most cybercrimes/attacks involve deploying some type of malware. Malware that viciously targets every industry, every sector, every enterprise and even individuals has shown its capabilities to take entire business organizations offline and cause significant financial damage in billions of dollars annually.Malware authors are constantly evolving in their attack strategies and sophistication and are developing malware that is difficult to detect and can lay dormant in the background for quite some time in order to evade security controls. Given the above argument, Traditional approaches to malware detection are no longer effective. As a result, deep learning models have become an emerging trend to detect and classify malware. This paper proposes a new convolutional deep learning neural network to accurately and effectively detect malware with high precision. This paper is different than most other papers in the literature in that it uses an expert data science approach by developing a convolutional neural network from scratch to establish a baseline of the performance model first, explores and implements an improvement model from the baseline model, and finally it evaluates the performance of the final model.The baseline model initially achieves 98% accurate rate but after increasing the depth of the CNN model, its accuracy reaches 99.183 which outperforms most of the CNN models in the literature. Finally, to further solidify the effectiveness of this CNN model, we use the improved model to make predictions on new malware samples within our dataset.

Z2F: Heterogeneous graph-based Android malware detection

“…In graph-based Android application detection, Marwan Omar [30] used his own graph convolutional neural network as a baseline, combined with expert data science methods, and finally achieved 99.183% accuracy in Android malware detection by continuously fine-tuning the model. RecepSinan Arslan [31] also uses a graph convolutional neural network, but the novelty is that it converts the data obtained in AndroidManifest.xml into image data and sends it to the neural network for learning, and its final accuracy rate reaches 96.2%.…”