  The Internet of Things (IoT) is an extension of the traditional Internet,
which allows a very large number of smart devices, such as home appliances,
network cameras, sensors and controllers to connect to one another to share
information and improve user experiences. Current IoT devices are typically
micro-computers for domain-specific computations rather than traditional
functionspecific embedded devices. Therefore, many existing attacks, targeted
at traditional computers connected to the Internet, may also be directed at IoT
devices. For example, DDoS attacks have become very common in IoT environments,
as these environments currently lack basic security monitoring and protection
mechanisms, as shown by the recent Mirai and Brickerbot IoT botnets. In this
paper, we propose a novel light-weight approach for detecting DDos malware in
IoT environments.We firstly extract one-channel gray-scale images converted
from binaries, and then utilize a lightweight convolutional neural network for
classifying IoT malware families. The experimental results show that the
proposed system can achieve 94.0% accuracy for the classification of goodware
and DDoS malware, and 81.8% accuracy for the classification of goodware and two
main malware families.
