# AI security and cyber risk in IoT systems

arXiv: https://arxiv.org/abs/2410.09194 · HTML: https://mlsanity.com/paper/2410.09194/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 53 references that are in the map.
- First posted on arXiv 11 Oct 2024.

## Builds on (most cited first, 1 of 1)

- [Using Deep Learning to Detect Digitally Encoded DNA Trigger for Trojan Malware in Bio-Cyber Attacks](https://mlsanity.com/paper/2202.11824/) (2022)

## Built on by (most cited first, 1 of 1)

- [L-XAIDS: A LIME-based eXplainable AI framework for Intrusion Detection Systems](https://mlsanity.com/paper/2508.17244/) (2025)

## Abstract (arXiv)

We present a dependency model tailored to the context of current challenges in data strategies and make recommendations for the cybersecurity community. The model can be used for cyber risk estimation and assessment and generic risk impact assessment.