Temporal assessment of malicious behaviors: application to turnout field data monitoring

TL;DR

This paper presents a method for detecting cyberattacks on railway turnout systems by analyzing the temporal evolution of their behavior and comparing predictions to real-time data, enhancing security monitoring.

Contribution

It introduces a novel approach for cyberattack detection in railway turnouts using temporal behavior prediction and discrepancy analysis, validated on real-world data.

Findings

Effective detection of cyberattacks demonstrated on real data

Temporal analysis improves reliability of turnout monitoring

Discrepancy detection identifies malicious behavior

Abstract

Monitored data collected from railway turnouts are vulnerable to cyberattacks: attackers may either conceal failures or trigger unnecessary maintenance actions. To address this issue, a cyberattack investigation method is proposed based on predictions made from the temporal evolution of the turnout behavior. These predictions are then compared to the field acquired data to detect any discrepancy. This method is illustrated on a collection of real-life data.