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Cybercriminals are moving towards zero-day attacks affectingresource-constrained devices such as single-board computers (SBC). Assumingthat perfect security is unrealistic, Moving Target Defense (MTD) is apromising approach to mitigate attacks by dynamically altering target attacksurfaces. Still, selecting suitable MTD techniques for zero-day attacks is anopen challenge. Reinforcement Learning (RL) could be an effective approach tooptimize the MTD selection through trial and error, but the literature failswhen i) evaluating the performance of RL and MTD solutions in real-worldscenarios, ii) studying whether behavioral fingerprinting is suitable forrepresenting SBC's states, and iii) calculating the consumption of resources inSBC. To improve these limitations, the work at hand proposes an online RL-basedframework to learn the correct MTD mechanisms mitigating heterogeneous zero-dayattacks in SBC. The framework considers behavioral fingerprinting to representSBCs' states and RL to learn MTD techniques that mitigate each malicious state.It has been deployed on a real IoT crowdsensing scenario with a Raspberry Piacting as a spectrum sensor. More in detail, the Raspberry Pi has been infectedwith different samples of command and control malware, rootkits, and ransomwareto later select between four existing MTD techniques. A set of experimentsdemonstrated the suitability of the framework to learn proper MTD techniquesmitigating all attacks (except a harmfulness rootkit) while consuming <1 MB ofstorage and utilizing <55% CPU and <80% RAM.

RL and Fingerprinting to Select Moving Target Defense Mechanisms for Zero-day Attacks in IoT