  With the increase of IoT devices and technologies coming into service,
Malware has risen as a challenging threat with increased infection rates and
levels of sophistication. Without strong security mechanisms, a huge amount of
sensitive data is exposed to vulnerabilities, and therefore, easily abused by
cybercriminals to perform several illegal activities. Thus, advanced network
security mechanisms that are able of performing a real-time traffic analysis
and mitigation of malicious traffic are required. To address this challenge, we
are proposing a novel IoT malware traffic analysis approach using deep learning
and visual representation for faster detection and classification of new
malware (zero-day malware). The detection of malicious network traffic in the
proposed approach works at the package level, significantly reducing the time
of detection with promising results due to the deep learning technologies used.
To evaluate our proposed method performance, a dataset is constructed which
consists of 1000 pcap files of normal and malware traffic that are collected
from different network traffic sources. The experimental results of Residual
Neural Network (ResNet50) are very promising, providing a 94.50% accuracy rate
for detection of malware traffic.
