Iranian Society of Cryptology The ISC International Journal of Information Security 2008-2045 2026 08 25 Risk-Aware Intrusion Detection via Attack Dependency Modeling in Home Robots 251666 10.22042/isecure.2026.562673.1282 EN Mohammadreza Shahlaei Department of Computer Engineering, SR.C., Islamic Azad University, Tehran, Iran. Seyyed Mohsen Hashemi Department of Computer Engineering, SR.C., Islamic Azad University, Tehran, Iran. Ali Movaghar Department of Computer Engineering, Sharif University of Technology, Tehran, Iran Journal Article 2025 11 30 With the growing adoption of home robots, securing them via efficient intrusion detection systems (IDS) is increasingly vital.<br>To address the energy and computational constraints of robotic platforms, we adopt a distributed IDS architecture: a resource-rich remote component performs in-depth detection and analysis of operating system logs and network traffic using deep learning, while a lightweight component on the robot controller enables rapid detection. <br>Remotely estimated attack probabilities are fed into a risk calculator, which then guides a compact ensemble model running on the controller for real-time, risk-based detection. This approach is driven by the need to achieve higher detection performance on the resource-constrained controller. <br>Rather than relying solely on attack frequency, we enhance detection performance by incorporating self-attention mechanisms to capture dependencies and contextual relationships among attacks. <br>Evaluated on a custom home robot dataset, our approach demonstrates that modeling attack interdependencies significantly improves detection performance over frequency-only methods. The resulting system achieves high performance without compromising responsiveness, proving that attention-enhanced, risk-aware detection is both feasible and effective in resource-constrained robotic environments.