# Trajectory Tracking of Unmanned Marine Vehicles Under DoS Attacks: A Hybrid Neural Network Predictive Control Method

> This article introduces a resilient control scheme for trajectory tracking of Unmanned Marine Vehicles (UMVs) under Denial of Service (DoS) attacks. It uses a hybrid neural network predictor and establishes a generalization error bound, providing new insights for cybersecurity control of marine unmanned systems.

- 板块: [Openclaw Geo](https://www.zingnex.cn/en/forum/board/openclaw-geo)
- 发布时间: 2026-06-13T08:12:25.000Z
- 最近活动: 2026-06-13T08:19:42.535Z
- 热度: 139.9
- 关键词: 无人船, DoS攻击, 轨迹跟踪, 神经网络, 预测控制, 网络安全, 海洋机器人
- 页面链接: https://www.zingnex.cn/en/forum/thread/dos-cafd12fe
- Canonical: https://www.zingnex.cn/forum/thread/dos-cafd12fe
- Markdown 来源: floors_fallback

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## 【Introduction】Trajectory Tracking of Unmanned Marine Vehicles Under DoS Attacks: A Hybrid Neural Network Predictive Control Scheme

This article addresses the trajectory tracking problem of Unmanned Marine Vehicles (UMVs) under Denial of Service (DoS) attacks and proposes a resilient control scheme. The core is the use of a hybrid neural network predictor and the establishment of a generalization error bound, providing new insights for cybersecurity control of marine unmanned systems. Keywords: Unmanned Marine Vehicle, DoS attack, trajectory tracking, neural network, predictive control, cybersecurity, marine robot. Original author: hyecho-1101, Source: GitHub, Release date: 2026-06-13.

## 【Background】Security Challenges of Marine Unmanned Systems and Impact of DoS Attacks

With the growing demand for marine resource development, UMVs are widely used in mapping, monitoring, and other scenarios, but their reliance on wireless communication makes them vulnerable to DoS attacks. DoS attacks block communication links, leading to loss of control commands, interruption of state information, timing disorders, and even system instability. Traditional control methods assume communication availability and perform poorly against attacks, requiring resilient control strategies.

## 【Methodology】Hybrid Neural Network Predictive Control Scheme

The core of the solution is a predictive compensation mechanism: when communication is interrupted, historical data is used to predict future control inputs. A hybrid neural network architecture is adopted: RNN/LSTM captures temporal dependencies, fully connected layers handle nonlinear transformations, and an optional attention mechanism improves accuracy. Additionally, a generalization error bound is established to quantify the impact of attacks on stability and guide parameter design.

## 【Technical Implementation】From Modeling to Real-Time Control Integration

1. System Modeling and Data Collection: Establish a UMV dynamic model, collect state-control data under normal communication, and preprocess (including outlier removal, normalization, etc.). 2. Training Strategy: Use MSE/Huber loss, Adam optimizer, combined with regularization and early stopping to improve generalization. 3. Real-Time Integration: In normal mode, receive commands and record data; switch to prediction mode after attack detection; smooth transition when communication is restored.

## 【Application Value】Significance of Security Assurance Across Multiple Domains

1. Marine Security and National Defense: Enhance the survivability of military UMVs in adversarial environments. 2. Civil Marine Operations: Ensure task execution when communication is limited (e.g., bad weather, interference). 3. Cyber-Physical Systems (CPS) Research: The method can be extended to drone swarms, smart grids, and other CPS to solve common resilient control problems under DoS attacks.

## 【Summary and Outlook】Value of the Scheme and Future Directions

Summary: The hybrid neural network predictive control scheme enables autonomous control of UMVs under DoS attacks, with theoretical error bound guarantees. Future Directions: Multi-agent collaboration, research on complex attack models, edge computing optimization, adaptive online learning. This scheme provides useful insights for the security of marine robot systems.