# AI Security Threats against Pervasive Robotic Systems: A Course for Next Generation Cybersecurity Workforce

arXiv: https://arxiv.org/abs/2302.07953 · HTML: https://mlsanity.com/paper/2302.07953/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 12 of its 65 references that are in the map.
- First posted on arXiv 15 Feb 2023.

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

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Open3D: A Modern Library for 3D Data Processing](https://mlsanity.com/paper/1801.09847/) (2018)
- [Black-box Generation of Adversarial Text Sequences to Evade Deep Learning Classifiers](https://mlsanity.com/paper/1801.04354/) (2018)
- [A new Backdoor Attack in CNNs by training set corruption without label poisoning](https://mlsanity.com/paper/1902.11237/) (2019)
- [Adversarial Examples Are a Natural Consequence of Test Error in Noise](https://mlsanity.com/paper/1901.10513/) (2019)
- [Generating Fake Cyber Threat Intelligence Using Transformer-Based Models](https://mlsanity.com/paper/2102.04351/) (2021)
- [Adversarial Training for Adverse Conditions: Robust Metric Localisation using Appearance Transfer](https://mlsanity.com/paper/1803.03341/) (2018)
- [To Make a Robot Secure: An Experimental Analysis of Cyber Security Threats Against Teleoperated Surgical Robots](https://mlsanity.com/paper/1504.04339/) (2015)
- [Mining Threat Intelligence about Open-Source Projects and Libraries from Code Repository Issues and Bug Reports](https://mlsanity.com/paper/1808.04673/) (2018)
- [Preventing Poisoning Attacks on AI based Threat Intelligence Systems](https://mlsanity.com/paper/1807.07418/) (2018)
- [Developing a Purely Visual Based Obstacle Detection using Inverse Perspective Mapping](https://mlsanity.com/paper/1809.01268/) (2018)
- [alurity, a toolbox for robot cybersecurity](https://mlsanity.com/paper/2010.07759/) (2020)

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

- [Security Considerations in AI-Robotics: A Survey of Current Methods, Challenges, and Opportunities](https://mlsanity.com/paper/2310.08565/) (2023)

## Abstract (arXiv)

Robotics, automation, and related Artificial Intelligence (AI) systems have become pervasive bringing in concerns related to security, safety, accuracy, and trust. With growing dependency on physical robots that work in close proximity to humans, the security of these systems is becoming increasingly important to prevent cyber-attacks that could lead to privacy invasion, critical operations sabotage, and bodily harm. The current shortfall of professionals who can defend such systems demands development and integration of such a curriculum. This course description includes details about seven self-contained and adaptive modules on "AI security threats against pervasive robotic systems". Topics include: 1) Introduction, examples of attacks, and motivation; 2) - Robotic AI attack surfaces and penetration testing; 3) - Attack patterns and security strategies for input sensors; 4) - Training attacks and associated security strategies; 5) - Inference attacks and associated security strategies; 6) - Actuator attacks and associated security strategies; and 7) - Ethics of AI, robotics, and cybersecurity.