  Internet of Things (IoT) is becoming more frequently used in more
applications as the number of connected devices is in a rapid increase. More
connected devices result in bigger challenges in terms of scalability,
maintainability and most importantly security especially when it comes to 5G
networks. The security aspect of IoT devices is an infant field, which is why
it is our focus in this paper. Multiple IoT device manufacturers do not
consider securing the devices they produce for different reasons like cost
reduction or to avoid using energy-harvesting components. Such potentially
malicious devices might be exploited by the adversary to do multiple harmful
attacks. Therefore, we developed a system that can recognize malicious behavior
of a specific IoT node on the network. Through convolutional neural network and
monitoring, we were able to provide malware detection for IoT using a central
node that can be installed within the network. The achievement shows how such
models can be generalized and applied easily to any network while clearing out
any stigma regarding deep learning techniques.
