Breaking (and Fixing) Channel-based Cryptographic Key Generation: A Machine Learning Approach

Abstract

Several systems and application domains are under-going disruptive transformations due to the recent breakthroughs in computing paradigms such us Machine Learning and commu-nication technologies such as 5G and beyond. Intelligent trans-portation 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 ve-hicular 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.