LAUSR

With the rapid growth of the Internet of Things (IoT) applications in Maritime Transportation Systems (MTS), cyber-attacks and challenges in data safety have also increased extensively. Meanwhile, the IoT devices are resource-constrained and cannot implement the existing security systems, making them susceptible to various types of debilitating cyber-attacks. The dynamics in the attack processes in IoT-enabled MTS networks keep changing, which makes a traditional offline or batch ML-based attack detection systems intractable to apply. This paper provides a novel approach of using an adaptive incremental passive-aggressive machine learning (AI-PAML) method to create a network attack detection system (NADS) to protect the IoT devices in an MTS environment. In this paper, we propose an NADS that utilizes a multi-access edge computing (MEC) platform to provide computational resources to execute the proposed model at a network end. Since online learning models face data saturation problems, we present an improved approximate linear dependence and a modified hybrid forgetting mechanism to filter the inefficient data and keep the detection model up-to-date. The proposed data filtering ensures that the model does not experience a rapid increase in unwarranted data, which affects the model’s attack detection rate. A Markov transition probability is applied to control the MEC selection and data offloading process by the IoT devices. The performance of the NADS is verified using selected benchmark datasets and a realistic IoT environment. Experimental results demonstrate that AI-PAML achieves remarkable performance in the NADS design for an MTS environment.