Abstract
The latest advancements in Internet of Things (IoT) have revolutionized the productivity of global shipping industry in the recent years. It also led to the emergence of IoT-enabled Maritime Transportation Systems (MTS). These approaches detect the malware in network before the execution process. Various machine learning (ML) models have been proposed and designed in literature for effective malware detection. However, the existence of numerous features in the data bring dimensionality problem which can be only resolved by the use of feature selection approaches. Therefore, the current research work presents Intelligent Metaheuristics-based Feature Selection model with Optimal ML approach for Malware Detection (IMFSOML-MD) on IoT-enabled MTS. Primarily, IMFSOML-MD technique involves the design of Quantum Invasive Weed Optimization Algorithm-based Feature Selection technique to optimally choose a subset of features. Moreover, an Optimal Wavelet Neural Network (OWNN) model is employed to perform classification process. The initial parameters of WNN model are optimally tuned with the help of Colliding Bodies Optimization algorithm thereby improving the detection performance. The proposed IMFSOML-MD technique was experimentally validated using publicly-available CICMalDroid2020 dataset. The results from extensive comparative analysis demonstrated the superiority of the proposed IMFSOML-MD technique over other compared methods in terms of detection performance with maximum accuracy of 98.96%.