Analysing Cyber-resiliency of a Marine Navigation System using Behavioural Relations

Abstract

Marine vessels need trustworthy navigation data for safe manoeuvring, but threats exist for external manipulation of signals and on-board systems. This paper employs analysis of behaviours to cross-validate that instruments provide correct information. Deviations from normal behaviour could be effects of malicious cyber-attack or instrument malfunction. Independent of the root cause, faulty information need be disregarded for navigation. This paper shows how instruments' violation of correct behaviour can be detected and isolated during near-coast navigation. The approach is to analyse topology of information flow and information processing, also referred to as structural analysis. The paper addresses the diagnosis potential for isolation of erroneous information about state of own ship and of surrounding objects. The analysis includes position, ship speed, and heading, which could lead to errors in navigation, to collision or grounding. The paper addresses required sensors, according to the International Maritime Organizations (IMO) Safety of Life at Sea (SOLAS) [20], and also presents potential gains by inclusion of computer vision. Showing that all single and several cases of simultaneous defects are discovered, for own ship and in surroundings, the results demonstrate that resilience of navigation information can be obtained for vessels sailing in coastal waters.

Cyber-resilience for marine navigation by information fusion and change detection

“…This approach abides with Safety of Life at Sea (SOLAS) regulations for knowledge of position, velocity and course of own ship at all times and it performs integrity monitoring of the vessel's position, heading and speed. Earlier results by the authors (Blanke and Staroswiecki, 2006;Blanke and Nguyen, 2018) demonstrated that modelling using normal behaviours and analysis based on system structure provided a framework to isolate defect components, and (Nissov et al, 2021) extended this to navigation in coastal waters using information from proprioceptive sensors (GNSS, compass, speed log) and exteroceptive sensors and information systems (ENC, ARPA radar, AIS). However, Nissov et al (2021) did not show how the monitoring action could be achieved by unsupervised algorithms.…”

Buoy Light Pattern Classification for Autonomous Ship Navigation Using Recurrent Neural Networks

“…Marine vessels rely to a large extent on global navigation satellite systems (GNSS) for navigation, but spoofing and jamming attempts have become increasingly frequent [10]. Automatic buoy classification can help combat spoofing if supplemented by a comparison between environment and sea charts, similar to landmark recognition [11], [12]. The mapping from international regulations to required features of autonomous navigation architecture and algorithms are discussed in [13].…”