# RESTRAIN: Reinforcement Learning-Based Secure Framework for Trigger-Action IoT Environment

arXiv: https://arxiv.org/abs/2503.09513 · HTML: https://mlsanity.com/paper/2503.09513/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 12 of its 25 references that are in the map.
- First posted on arXiv 12 Mar 2025.

## Builds on (most cited first, 12 of 12)

- [Adam: A Method for Stochastic Optimization](https://mlsanity.com/paper/1412.6980/) (2014)
- [OpenAI Gym](https://mlsanity.com/paper/1606.01540/) (2016)
- [TensorFlow: Large-Scale Machine Learning on Heterogeneous Distributed Systems](https://mlsanity.com/paper/1603.04467/) (2016)
- [Deep Recurrent Q-Learning for Partially Observable MDPs](https://mlsanity.com/paper/1507.06527/) (2015)
- [IoTSan: Fortifying the Safety of IoT Systems](https://mlsanity.com/paper/1810.09551/) (2018)
- [D-ACC: Dynamic Adaptive Cruise Control for Highways with Ramps Based on Deep Q-Learning](https://mlsanity.com/paper/2006.01411/) (2020)
- [RLPG: Reinforcement Learning Approach for Dynamic Intra-Platoon Gap Adaptation for Highway On-Ramp Merging](https://mlsanity.com/paper/2212.03497/) (2022)
- [Program Analysis of Commodity IoT Applications for Security and Privacy: Challenges and Opportunities](https://mlsanity.com/paper/1809.06962/) (2018)
- [A Comprehensive Survey on the State-of-the-art Data Provenance Approaches for Security Enforcement](https://mlsanity.com/paper/2107.01678/) (2021)
- [IoTMonitor: A Hidden Markov Model-based Security System to Identify Crucial Attack Nodes in Trigger-action IoT Platforms](https://mlsanity.com/paper/2202.04620/) (2022)
- [IoTWarden: A Deep Reinforcement Learning Based Real-time Defense System to Mitigate Trigger-action IoT Attacks](https://mlsanity.com/paper/2401.08141/) (2024)
- [Ruledger: Ensuring Execution Integrity in Trigger-Action IoT Platforms](https://mlsanity.com/paper/2402.19011/) (2024)

## Abstract (arXiv)

Internet of Things (IoT) platforms with trigger-action capability allow event conditions to trigger actions in IoT devices autonomously by creating a chain of interactions. Adversaries exploit this chain of interactions to maliciously inject fake event conditions into IoT hubs, triggering unauthorized actions on target IoT devices to implement remote injection attacks. Existing defense mechanisms focus mainly on the verification of event transactions using physical event fingerprints to enforce the security policies to block unsafe event transactions. These approaches are designed to provide offline defense against injection attacks. The state-of-the-art online defense mechanisms offer real-time defense, but extensive reliability on the inference of attack impacts on the IoT network limits the generalization capability of these approaches. In this paper, we propose a platform-independent multi-agent online defense system, namely RESTRAIN, to counter remote injection attacks at runtime. RESTRAIN allows the defense agent to profile attack actions at runtime and leverages reinforcement learning to optimize a defense policy that complies with the security requirements of the IoT network. The experimental results show that the defense agent effectively takes real-time defense actions against complex and dynamic remote injection attacks and maximizes the security gain with minimal computational overhead.