A Malicious Attack on the Machine Learning Policy of a Robotic System
| Title | A Malicious Attack on the Machine Learning Policy of a Robotic System |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Clark, G., Doran, M., Glisson, W. |
| Conference Name | 2018 17th IEEE International Conference On Trust, Security And Privacy In Computing And Communications/ 12th IEEE International Conference On Big Data Science And Engineering (TrustCom/BigDataSE) |
| Keywords | artificial intelligence, artificial intelligence security, autonomous vehicle, Autonomous vehicles, composability, cyberattack, cybersecurity, Human Behavior, indirect attack, intelligent robots, learning (artificial intelligence), machine learning, machine learning algorithms, machine learning policy, malicious attack, Metrics, mobile robots, pubcrawl, Q learning algorithm, radiofrequency identification, real-time routing control, Resiliency, robot operating systems, Robotic system, robotic vehicle, Robotics, robots, security, security of data |
| Abstract | The field of robotics has matured using artificial intelligence and machine learning such that intelligent robots are being developed in the form of autonomous vehicles. The anticipated widespread use of intelligent robots and their potential to do harm has raised interest in their security. This research evaluates a cyberattack on the machine learning policy of an autonomous vehicle by designing and attacking a robotic vehicle operating in a dynamic environment. The primary contribution of this research is an initial assessment of effective manipulation through an indirect attack on a robotic vehicle using the Q learning algorithm for real-time routing control. Secondly, the research highlights the effectiveness of this attack along with relevant artifact issues. |
| URL | https://ieeexplore.ieee.org/document/8455947 |
| DOI | 10.1109/TrustCom/BigDataSE.2018.00079 |
| Citation Key | clark_malicious_2018 |
- machine learning algorithms
- security of data
- robots
- robotics
- robotic vehicle
- Robotic system
- Resiliency
- real-time routing control
- radiofrequency identification
- Q learning algorithm
- pubcrawl
- mobile robots
- Metrics
- malicious attack
- machine learning policy
- robot operating systems
- machine learning
- learning (artificial intelligence)
- intelligent robots
- indirect attack
- Human behavior
- Cybersecurity
- cyberattack
- composability
- autonomous vehicles
- autonomous vehicle
- artificial intelligence security
- Artificial Intelligence
- security