Real‐Time Detection and Prevention of DoS Attack in Unmanned Marine Vehicles Using Machine Learning

Unmanned marine vehicles (UMVs) are one of the most crucial components of the underwater communication and surveillance system in the marine and oceanographic environment. This research introduces a novel approach to stabilize the connection in an unstable underwater environment by inheriting features of communication velocities. The innovative proposed technique effectively divides the entire communication underwater by distributing the burden and ensuring trouble‐free and efficient communication and surveillance. In existing approaches, troubleshooting communication issues often results in delays or slow performance. However, the proposed method focuses on establishing an efficient communication link to increase the throughput of the deployed underwater network. To achieve this, we have implemented two different communication channels, acoustic and wireless optical, that provide redundant pathways for communication, which enhances reliability and resilience. In the event of a denial‐of‐service attack or failure in one communication channel, the other channel becomes the primary means of communication, ensuring uninterrupted data transmission. We have used an attack prediction machine‐learning model to further improve system security. Random forest (RF), naïve Bayes classifiers, and machine learning methods like support vector machines (SVM), multilayer perceptrons (MLP), and k‐nearest neighbors (KNN) are used to forecast the likelihood of an attack. To stop unwanted access or malicious activity, the associated communication channel is shut when a model shows a high probability of an attack. The efficiency of our recommended control strategy is confirmed by a simulation study of the proposed networked UMVs system. The suggested system delivers improved robustness and secure communication for real‐time situations in the underwater environment by using machine learning‐based attack prediction, redundant communication channels, and varied velocities to stabilize the connection. SVM achieves high accuracy at 98%. MLP is not far behind, with a 96% accuracy rate. The higher accuracy of SVM and MLP in predicting DoS assaults in UMVs is highlighted by the lesser accuracies of 66.3.