  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.
