Breaking (and fixing) channel-based cryptographic key generation: a machine learning approach

Several systems and application domains are under-going disruptive transformations due to the recent breakthroughs in computing paradigms such us Machine Learning and communication technologies such as 5G and beyond. Intelligent transportation systems is one of the flagship domains that witnessed drastic transformations through the development of ML-based environment perception along with Vehicle-to-Vehicle (V2V) and Vehicle-to-Infrastructure (V2I) communication protocols. Such connected, intelligent and collaborative transportation systems represent a promising trend towards smart roads and cities. However, the safety-critical aspect of these cyber-physical systems requires a systematic study of their security and privacy. In fact, security-sensitive information could be transmitted between vehicles, or between vehicles and the infrastructure such as security alerts, payment, etc. Since asymmetric cryptography is heavy to implement on embedded time-critical devices, in addition to the complexity of PKI-based solutions, symmetric cryptography offers confidentiality along with high performance. However, cryptographic key generation and establishment in symmetric cryptosystems is a great challenge. Recent work proposed a key generation and establishment protocol for vehicular communication that is based on the reciprocity and high spatial and temporal variation properties of the vehicular communication channel. This paper investigates the limitations of such channel-based key generation protocols. Based on a channel model with a machine learning approach, we show the possibility for a passive eavesdropper to compromise the secret key in a practical manner, thereby undermining the security of such key establishment technique. Moreover, we propose a defense based on adversarial machine learning to overcome this limit.