  In recent years, malware detection has become an active research topic in the
area of Internet of Things (IoT) security. The principle is to exploit
knowledge from large quantities of continuously generated malware. Existing
algorithms practice available malware features for IoT devices and lack
real-time prediction behaviors. More research is thus required on malware
detection to cope with real-time misclassification of the input IoT data.
Motivated by this, in this paper we propose an adversarial self-supervised
architecture for detecting malware in IoT networks, SETTI, considering samples
of IoT network traffic that may not be labeled. In the SETTI architecture, we
design three self-supervised attack techniques, namely Self-MDS, GSelf-MDS and
ASelf-MDS. The Self-MDS method considers the IoT input data and the adversarial
sample generation in real-time. The GSelf-MDS builds a generative adversarial
network model to generate adversarial samples in the self-supervised structure.
Finally, ASelf-MDS utilizes three well-known perturbation sample techniques to
develop adversarial malware and inject it over the self-supervised
architecture. Also, we apply a defence method to mitigate these attacks, namely
adversarial self-supervised training to protect the malware detection
architecture against injecting the malicious samples. To validate the attack
and defence algorithms, we conduct experiments on two recent IoT datasets:
IoT23 and NBIoT. Comparison of the results shows that in the IoT23 dataset, the
Self-MDS method has the most damaging consequences from the attacker's point of
view by reducing the accuracy rate from 98% to 74%. In the NBIoT dataset, the
ASelf-MDS method is the most devastating algorithm that can plunge the accuracy
rate from 98% to 77%.
