AutoSec: Multidimensional Timing-Based Anomaly Detection for Automotive Cybersecurity

Nowadays, autonomous driving and driver assistance applications are being developed at an accelerated pace. This rapid growth is primarily driven by the potential of such smart applications to significantly improve safety on public roads and offer new possibilities for modern transportation concepts. Such indispensable applications typically require wireless connectivity between the vehicles and their surroundings, i.e. roadside infrastructure and cloud services. Nevertheless, such connectivity to external networks exposes the internal systems of individual vehicles to threats from remotely-launched attacks. In this realm, it is highly crucial to identify any misbehavior of the software components which might occur owing to either these threats or even software/hardware malfunctioning. In this paper, we introduce AutoSec, a host-based anomaly detection algorithm which relies on observing four timing parameters of the executed software components to accurately detect malicious behavior on the operating system level. To this end, AutoSec formulates the task of detecting anomalistic executions as a clustering problem. Specifically, AutoSec devises a hybrid clustering algorithm for grouping a set of collected timing traces resulted from executing the legitimate code. During the runtime, AutoSec simply classifies a certain execution as an anomaly, if its timing parameters are distant enough from the boundaries of the predefined clusters. To show the effectiveness of AutoSec, we collected timing traces from a testbed composed of a set of real and virtual control units communicating over a CAN bus. We show that using our proposed AutoSec, compared to baseline methods, we can identify up to 21% less false positives and 18% less false negatives.