  As a massive number of the Internet of Things (IoT) devices are deployed, the
security and privacy issues in IoT arouse more and more attention. The IoT
attacks are causing tremendous loss to the IoT networks and even threatening
human safety. Compared to traditional networks, IoT networks have unique
characteristics, which make the attack detection more challenging. First, the
heterogeneity of platforms, protocols, software, and hardware exposes various
vulnerabilities. Second, in addition to the traditional high-rate attacks, the
low-rate attacks are also extensively used by IoT attackers to obfuscate the
legitimate and malicious traffic. These low-rate attacks are challenging to
detect and can persist in the networks. Last, the attackers are evolving to be
more intelligent and can dynamically change their attack strategies based on
the environment feedback to avoid being detected, making it more challenging
for the defender to discover a consistent pattern to identify the attack.
  In order to adapt to the new characteristics in IoT attacks, we propose a
reinforcement learning-based attack detection model that can automatically
learn and recognize the transformation of the attack pattern. Therefore, we can
continuously detect IoT attacks with less human intervention. In this paper, we
explore the crucial features of IoT traffics and utilize the entropy-based
metrics to detect both the high-rate and low-rate IoT attacks. Afterward, we
leverage the reinforcement learning technique to continuously adjust the attack
detection threshold based on the detection feedback, which optimizes the
detection and the false alarm rate. We conduct extensive experiments over a
real IoT attack dataset and demonstrate the effectiveness of our IoT attack
detection framework.
