Exploring Simulation Methods to Counter Cyber-Attacks on the Steering Systems of the Maritime Autonomous Surface Ship (MASS)

Abstract

This paper presents a simulation-based investigation into control strategies for mitigating the consequences of cyber-assault on the steering systems of the Maritime Autonomous Surface Ships (MASS). The study focuses on two simulation experiments conducted within the Simulink/MATLAB environment, utilizing the catamaran "Nymo" MASS mathematical model to represent vessel dynamics. Cyber-attacks are modeled as external disturbances affecting the rudder control signal, emulating realistic interference scenarios. To assess control resilience, two configurations are compared during a representative turning maneuver to a specified heading: (1) a Proportional–Integral–Derivative (PID) regulator augmented with a Least Mean Squares (LMS) adaptive filter, and (2) a Nonlinear Autoregressive Moving Average with Exogenous Input (NARMA-L2) neural network regulator. The PID and LMS configurations aim to enhance the disturbance rejection capabilities of the classical controller through adaptive filtering, while the NARMA-L2 approach represents a data-driven, nonlinear control alternative. Simulation results indicate that although the PID and LMS setups demonstrate improved performance over standalone PID in the presence of cyber-induced disturbances, the NARMA-L2 controller exhibits superior adaptability, accuracy, and robustness under adversarial conditions. These findings suggest that neural network-based control offers a promising pathway for developing cyber-resilient steering systems for MASS.

1. Introduction

The marine industry is going through a substantial shift, owing to the widespread adoption of smart shipping technologies that combine connectivity, robotics, large-scale data analytics, and computational intelligence [1]. These innovations aim to enhance competitiveness, operational safety, and environmental sustainability. Modern ships are increasingly equipped with a variety of sensors, onboard data processors, and data-driven decision-making systems based on deep learning algorithms. Thanks to high-bandwidth connectivity, vessels can now be remotely monitored and operated, laying the groundwork for greater autonomy at sea. The emergence of MASSs represents a pivotal shift in maritime operations. MASSs are digital–physical structures enabled by the convergence of real-time Information and Operational Technologies (IT/OT). The OT encompasses a broad range of programmable controllers and embedded systems managing onboard physical processes, while the IT handles data management and administrative functions. Both domains are critical to MASS operation, and both are increasingly susceptible to cyber-attacks [3].

MASSs have several advantages: they may be constructed cleaner on fuel, lighter, more affordable to operate, wind-resistant, and aerodynamically efficient. The path to widespread MASS adoption nevertheless remains strewn with technical and regulatory challenges. Steering systems are a particularly attractive target, since disrupting heading control can cause grounding or collision without deep system penetration. This study therefore focuses on the cyber resilience of the steering control system specifically, comparing a classical PID controller, an LMS-augmented PID, and a NARMA-L2 neural controller under identical cyber-induced disturbances, using the "Nymo" catamaran model.

2. Literature Review

The development of unmanned and autonomous naval vehicles marks an important change in marine transportation, research, and defense. Autonomous Underwater Vehicle (AUV) development began in the 1950s, evolving from heavy, inefficient early prototypes to sophisticated platforms capable of navigating depths up to 6000 m with advanced obstacle detection and mapping capabilities [11]. Similarly, MASSs progressed from experimental vessels to commercially operational platforms including cargo ships, research vessels, and military craft [12]. However, despite these advancements, fully autonomous surface and underwater vessels capable of deep and hazardous missions remain a work in progress, with numerous technical, legal, and organizational challenges still unresolved [13]. International regulators like the IMO are actively working on the legal and safety frameworks for safe MASS integration into existing marine traffic. The IMO anticipates finalizing non-mandatory MASS regulations before 2025, laying preparatory measures for a comprehensive mandatory regulatory framework expected post-2030 [14].

Classification societies like Lloyd's Register established autonomy levels from manual operations (AL0) to fully autonomous vessels without human intervention (AL6), providing a standardized taxonomy for assessing unmanned marine systems [15]. In control cybersecurity, prior research showed traditional controllers such as PID perform well under nominal conditions but struggle in nonlinear or high-disturbance environments, while adaptive filtering and adaptive neural control offer promising alternatives. Steering-specific cyber resilience — a heading control system's ability to hold course under direct adversarial interference with the control signal — nevertheless remains understudied in current MASS literature, the gap this study addresses through detailed experimental evaluation in simulation.

3. Methodology

The simulation experiment in this study involves a single MASS, specifically the "Nymo" catamaran (Figure 1 of the original paper), modeled using nonlinear dynamics and differential thrust mechanics. The study investigates the steering control system's cyber-resilience through a structured methodology encompassing mathematical modeling, realistic cyber-attack emulation, and comparative controller performance analysis. The developed model accurately reflects the ship's maneuvering behavior and serves as a foundation for simulating navigation under nominal and adversarial conditions. A dedicated simulation environment was constructed in Simulink/MATLAB to replicate cyber-attack scenarios targeting the rudder control system. These attacks are modeled as Gaussian white noise disturbances superimposed on control inputs, simulating unauthorized interference that may disrupt maneuverability and heading control. Three control strategies were tested under identical conditions: a classical PID controller (baseline); a PID controller augmented with LMS adaptive filters, improving adaptability and disturbance rejection; and a NARMA-L2 neural network controller offering advanced adaptive capabilities. The experimental scenario involves a turning maneuver to a fixed heading angle (e.g., 45◦ North–East–Down (NED)), where each control strategy is evaluated on its ability to maintain the desired course.

3.1. Model of a Catamaran MASS

This section explores the "Nymo" catamaran model (Figure 2 of the original paper). This catamaran watercraft has two distinct hulls with separate propellers, where Fl and Fr are the port-side (left) and starboard-side (right) powers produced by differential thrusts, and d is the lateral hull separation (from hull centerline to MASS centerline). Figure 2 depicts the variable definition structure, with heading angle ψ denoting the orientation of the craft's construction-fixed structure reference to the NED quadrant. For convenience, the vessel's heading angle φ is taken counterclockwise from the horizontal plane, i.e., φ = π/2 − ψ. The catamaran's maneuvering depends on differential thrust: if Fl equals Fr, the vessel travels straight; if they differ, thrust variation between the stern engines creates a turning moment changing course. The model kinematics are [44]: [ẋ, ẏ, φ̇]ᵀ = R(φ)·[u, v, r]ᵀ, (1) where (x,y) are the catamaran center-of-mass coordinates in the earth-fixed system, φ the heading angle, and u, v, r the surge, sway, and yaw speeds respectively. The current study assumes harmful environmental influences are neglected to isolate the cyber-attack effect.

3.2. Parameters of the Catamaran MASS

This section assesses the catamaran parameters for MASS "Nymo". Direct measurements can determine this vehicle's parameters [48]: m = 225 kg, d = 0.405 m, dp = 0.18 m, lx = 2.56 m, ly = 1.08 m, ld = 0.36 m, (6) where lx, ly, ld denote catamaran length, width, and draft respectively. Magnitudes of tP and dNP follow from (3) [46]: tP = 0.25, dNP = 0.25. The parallelepiped's moment of inertia Izz relative to the Z axis is [49]: Izz = m/12·(lx² + ly²), (7) hence from (6) and (7), Izz = 144.7500 kgm². (8) Parameters (6) and (8) link to hull-added masses from (2) [45]: m11 = ldm/2lx, m22 = 2ldm/ly·(1 − ly/2lx), m66 = 2ldIzz/ly·(1 − 1.6ly/lx), (9) giving parameter values in (9): m11 = 15.8203 kg, m22 = 118.3594 kg, m66 = 31.3625 kgm². Parameter ρ in (4) is well known: ρ = 1000 kg/m³. To assess one propeller's F value via Equation (4), the chosen propeller's advance speed coefficient Jp from (4) is [50]: Jp = u/ndp, (10) where u is surge speed, n propeller revolutions, dp propeller diameter. A quadratic polynomial of Jp approximates the propeller thrust coefficient function kt for simulation: kt = a1Jp² + a2. (11)

3.3. PID Control of Course of the MASS

A catamaran model is nonlinear, and designing a simple regulator adhering in every situation to outlined boundaries is challenging. The left propeller thrust Fl can be considered a fixed constant. Allowing Fl and Fr to vary would make the system a multiple-input, multiple-output system, rendering PID and NARMA-L2 control strategies inappropriate. Hence Fl = const. (15) With (15), the right propeller thrust Fr serves as control input regulating heading angle φ, transforming the complex control problem into a single-input one. A PID controller regulates the input variable Fr. PID controllers come in various formats; one possible compensator formula implementation is PID(s) = P(br−y) + I·(1/s)(r−y) + D·(N/(1+N(1/s)))(cr−y), (16) where P, I, D, N, b, c are the proportional, integral, derivative, filter coefficient, proportional setpoint weight, and derivative setpoint weight parameters of the PID controller respectively; r is the targeted reference; y the measured output. Increasing the proportional gain (P) proportionally increases the control signal for the same error level, making the closed-loop system react faster but risking overshoot and instability if overdone.

3.4. Simulation of Cyberattack Using PID Controller

Cyber–physical systems (CPSs) are digital and mechanical devices using sensors and actuators to detect and change their state respectively. CPSs rely on sensor and actuator accuracy and precision to reach a desired state. All CPS parts are prone to malfunctions or errors, and CPSs are vulnerable to deliberate attacks on both their physical and cyber components — attacks on CPSs can drive undesired or dangerous responses. Adversaries can attack the system at any control-system component; the attack propagates through the control system, modifying the control signal value randomly. This malevolent effect can be modeled as overwriting the original control signal with some random value [52,53]. For instance, the desired rudder command can be overwritten with Gaussian white noise to model attack effects on any control system component. Intended output and actual measurement φa are connected; Figure 6 of the original paper shows φd with imposed noise v: φa = φd(τ) + ν(τ). (17) For interference at the system input side as in Figure 6: Fra(τ) = Frd(τ) + w(τ), (18) where building a detector distinguishing adversarial jamming from natural environmental disturbance is difficult, underscoring the need for inherently resilient controllers.

3.5. Simulation of Cyberattack Prevention Using PID Controller and LMS Filters

In this setting, LMS filters are added to the system's input and output, maintaining the PID control system configuration previously discussed (Figure 9 of the original paper). A relatively simple LMS filter combined with a PID controller counters cyber-attacks, improving PID performance under cyber-attacks. The LMS algorithm is a widely used adaptive algorithm, particularly in adaptive filtering and neural networks. It is a stochastic gradient descent method iteratively adjusting filter coefficients to minimize mean squared error between a desired signal and filter output, effectively training the filter to better match desired signal characteristics. In Figure 9, the LMS filter block uses the LMS adaptive algorithm. Filter weights or coefficients must reduce the error ε among output and wanted signals. The output signal is the filtered input signal, i.e., the wanted-signal approximation. The error output port gives the subtraction of the output signal from the aimed signal. Filtered errors for input and output LMS filters are presented in Figures 10 and 11 respectively, the input control signal in Figure 12, and the trajectory with LMS filtering in Figure 13. Here the trend line shows clear tracking improvement over standalone PID, though notable deviations and oscillation around the desired course persist under continued disturbance.

3.6. Simulation of Cyberattack Prevention Using NARMA-L2 Controller

The NARMA-L2 controller is used in the same control system architecture as previously described (see Figure 14 of the original paper). NARMA-L1 and NARMA-L2 are two approximations to the NARMA model proposed in [54]. Practically, the NARMA-L2 model is easier to set than NARMA-L1. Here we grounded our study only on the approximate NARMA-L2 model. For the PID and NARMA controllers under consideration, no cyber-attack signal detection logic is required. The PID controller has no input/output signal restrictions during operation, while NARMA has restrictions corresponding to a given input signal but the output signal is unlimited. After tuning, these controllers operate automatically. The internal window with associated parameters (Figure 15) tunes the NARMA-L2 regulator. Figure 16 depicts the internal organization of the NARMA-L2 regulator block shown in Figure 14, and Figure 17 the neural network structure from Figure 16. An input control signal is given in Figure 18 and the MASS path in Figure 19. Here the catamaran maintains smooth trajectory control with only minor leeway even under simulated cyber threats, clearly outperforming both standalone PID and LMS-augmented PID.

4. Discussion

The present study explored performance differences between conventional PID and NARMA-L2 controllers in the context of cyber-attacks on MASS, applied to the two control strategies under adversarial conditions, offering insights into theoretical and applied aspects of autonomous maritime navigation. From simulations, the traditional PID controller, while effective under nominal conditions, failed to maintain stability and course accuracy under cyber-attacks simulated as Gaussian white noise disturbances. This aligns with prior control literature, where PID controllers are recognized for limitations in nonlinear or high-disturbance environments. Even with LMS filtering to suppress noise, the PID-controlled MASS still exhibited unpredictability and significant deviation from its desired trajectory, posing potential hazards to surrounding maritime traffic. In contrast, the NARMA-L2 controller demonstrated robust performance, retaining smooth trajectory control and only minor leeway even under simulated cyber threats. This superior performance is attributed to the model's innate capacity to learn and adjust to nonlinear system dynamics and disturbance patterns, supporting earlier studies on brain adaptive control in dynamic contexts. These findings highlight the critical importance of employing adaptive, learning-based control architectures in safety-critical autonomous navigation applications.

The results nevertheless raise important deployment questions: NARMA-L2 requires accurate plant identification and greater computational resources than PID, and integrating it into certified marine steering systems may demand re-certification and extensive testing. Its simulation-proven robustness against Gaussian noise may not generalize directly to intelligent adversarial attacks adapting to controller behavior. The study points to a layered defensive architecture as most realistic: a certified PID controller as approved baseline, augmented with LMS-type adaptive filtering as a first disturbance-rejection layer, and an NARMA-L2-type learning layer for adversarial conditions, with continuous safety monitoring enabling safe fallback to conventional control when neural predictions leave safe bounds.

5. Conclusions

In this paper, a mathematical model for the catamaran "Nymo" maneuvering is presented, and PID and NARMA-L2 controllers for desired heading control are applied, with the objective of maintaining the automatic course of a catamaran under cyber-attacks. Simulation results in Simulink are presented. When the obtained results are examined, control using conventional controllers such as PID shows underperformance when subject to cyber-attacks. In contrast, the NARMA-L2 controller, an artificial neural network-based solution, demonstrates strong resilience and high performance under adversarial conditions. The resulting "Nymo" catamaran simulation model makes it possible to avoid difficult-to-implement experiments in rough sea conditions, offering a safe and cost-effective virtual environment for testing advanced control strategies. Comparing adaptive versus neural strategies, this study contributes a detailed, practical evaluation of steering-specific cyber resilience, an area insufficiently addressed in current MASS literature.

However, while the proposed approach shows promise in a simulated setting, real-world feasibility of implementing such neural control systems in operational MASS is only briefly discussed. Practical hardware integration, computational overhead, and robustness challenges across diverse environmental and traffic conditions remain open. Scalability to larger, more complex vessels with different dynamics requires further research. Future work should prioritize physical-platform experimental validation, testing against intelligent adversarial attack patterns beyond Gaussian noise, and developing formal safety assurances for neural controllers before deployment in safety-critical steering systems.

References

Full reference list (54 sources) available in the original PDF.