Scalable Malware Detection System Using Distributed Deep Learning

Abstract

The number of complex and novel malware attacks is increasing exponentially in the cyberworld. Malware detection systems are facing new challenges due to the volume, velocity, and complexity of malware. The current malware detection system relies on a time-consuming, resource-intensive, and knowledge-intensive classification approach. Most of the existing malware detection system is ineffective in detecting novel malware attacks. A deep learning approach can be used to build a malware detection system that can effectively detect novel malware attacks without much human intervention. The current circumstance necessitates not just a malware system with excellent accuracy, but also one that can serve a large volume of demand in near real-time. A scalable malware detection system capable of detecting complex attacks is the need of time. This article discusses a scalable and distributed deep learning approach for malware detection using convolutional neural network and bidirectional long short-term memory (CNN-BiLSTM). The deep learning approach has been used to make the system learn and make predictive decisions without human intervention. The performance of the deep learning approach depends on various parameters and training data sets. Hence, different combinations of deep learning algorithms have been used to design and test the models to achieve the desired result. The experimental results show that the double layer of CNN and BiLSTM has better performance than single-layer CNN.