Noise injection techniques in cryptographic keys to enhance security of autonomous systems

Cryptographic protocols are widely deployed to enhance the security of sensitive data shared across autonomous networks. Privacy and security issues are exacerbated due to increased vulnerability of the cryptographic key to various types of attack. It is extremely challenging to distribute cryptographic keys in contested areas on a real-time basis, therefore several protocols have been developed that use the fingerprints of electronic devices embedded in cyber physical systems for one-time use key generation. Various autonomous systems, including vehicles, robots, and industrial machines may be protected by these fingerprints, thus allowing the use of onetime encryption keys, thereby reducing the cybersecurity attack surface. To mitigate certain attacks and enhance security we propose methods to inject random noise in these keys, as well as the associated key recovery schemes. The benefit of noise injection is the ability to incrementally confuse the hacker while adding an extra layer of authenticity. The random percentage of noise injected in the key can only be identified and verified by an authorized party. An unexpected increase or decrease in the percentage of noise notifies the server that the autonomous system is potentially under attack. Experimentally, we demonstrate how noise levels up to 30% of the cryptographic keys can be injected, and how the temperature can alter the percentage of noise. We also quantify methods to increase the errors due to the instability of the fingerprints. Additionally, we review different strategies of noise injection and how we may leverage the injected noise for stronger authentication.