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| Type | Master's Thesis |
| Scope | Discipline-based scholarship |
| Title | Federated Reinforcement Learning for Private and Collaborative Selection of Moving Target Defense Mechanisms for IoT Device Security |
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| Institution | University of Zurich |
| Faculty | Faculty of Business, Economics and Informatics |
| Date | 2023 |
| Abstract Text | The Internet of Things (IoT) has grown exponentially in recent years and it is predicted that the number of devices will double again to 30 billion by 2030 [24]. At the same time, the number of unpatched, vulnerable and infected devices connected to the Internet is increasing exponentially as well. Famous malware incidents from the past like Mirai have painfully illustrated how vulnerable IoT devices are on a broad scale. This work examines how Moving Target Defense (MTD) can be used in a collaborative framework for defense in depth and to thwart cyberattacks. For this purpose, a system prototype has been implemented that is capable of autonomously learning to defend a set of IoT devices (more specifically Radio Frequency Spectrum Sensors belonging to ElectroSense) from a specific set of malware by selecting and deploying Moving Target Defenses (MTDs). In scientific literature, usually individual MTDs optimized against specific attacks are presented, but no collaborative framework that combines and orchestrates a set of MTDs. In the prototypical implementation, an individual local agent is deployed on a set of simulated device, monitoring the behavior of its host, according to 100 system parameters. In case an attack is detected, the local agent is invoked in order to select from a set of MTD to ward off the attack. If the post-MTD device behavior can be considered normal again, the local agent receives a reward, which is used to update the local policy. Thanks to the use of FL, all local agents contribute to learning one global defense policy together. The project shows that a good attack mitigation probability can be achieved in non-federated as well as federated learning setting. Furthermore, the system also proves to be somewhat robust against locally and globally skewed sample distribution. Under certain assumptions it can also be assumed that collaborative learning of an MTD selection policy is faster and more robust than centralized learning. The findings on how FRL can be used in IT security to collaboratively learn an MTD selection policy contribute to the state of the art on MTD. |
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