The ISC International Journal of Information Security

Risk-Aware Intrusion Detection via Attack Dependency Modeling in Home Robots

Articles in Press

Document Type : Research Article

Authors

Mohammadreza Shahlaei 1
seyyed mohsen hashemi 1
Ali Movaghar 2

1 Department of Computer Engineering, SR.C., Islamic Azad University, Tehran, Iran.

2 Department of Computer Engineering, Sharif University of Technology, Tehran, Iran

https://doi.org/10.22042/isecure.2026.562673.1282
Abstract
With the growing adoption of home robots, securing them via efficient intrusion detection systems (IDS) is increasingly vital. 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. Meanwhile, a lightweight component on the robot controller enables rapid detection. 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. Rather than relying solely on attack frequency, we enhance detection performance by incorporating self-attention mechanisms to capture dependencies and contextual relationships among attacks. 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.

Keywords

attack dependency
ensemble learning
intrusion detection
home robots
risk-aware
self-attention
The ISC International Journal of Information Security

Articles in Press, Corrected Proof
Available Online from 25 August 2026

Home

AI Policies

Guide for Authors

Submit Manuscript

Contact Us