Malware Classification using Deep Neural Networks: Performance Evaluation and Applications in Edge Devices

TL;DR

This paper evaluates the effectiveness of deep neural networks for malware classification, demonstrating high accuracy and feasibility of deployment on resource-constrained edge devices to enhance real-time IoT security.

Contribution

It introduces optimized DNN architectures for malware detection and explores their deployment on edge devices, advancing real-time, resource-efficient cybersecurity solutions.

Findings

High accuracy in malware classification across types

Successful deployment of DNNs on edge devices

Enhanced real-time detection in IoT systems

Abstract

With the increasing extent of malware attacks in the present day along with the difficulty in detecting modern malware, it is necessary to evaluate the effectiveness and performance of Deep Neural Networks (DNNs) for malware classification. Multiple DNN architectures can be designed and trained to detect and classify malware binaries. Results demonstrate the potential of DNNs in accurately classifying malware with high accuracy rates observed across different malware types. Additionally, the feasibility of deploying these DNN models on edge devices to enable real-time classification, particularly in resource-constrained scenarios proves to be integral to large IoT systems. By optimizing model architectures and leveraging edge computing capabilities, the proposed methodologies achieve efficient performance even with limited resources. This study contributes to advancing malware detection techniques and emphasizes the significance of integrating cybersecurity measures for the early detection of malware and further preventing the adverse effects caused by such attacks. Optimal considerations regarding the distribution of security tasks to edge devices are addressed to ensure that the integrity and availability of large scale IoT systems are not compromised due to malware attacks, advocating for a more resilient and secure digital ecosystem.