ABSTRACT Computers interact with other systems, often with the support of fast and readily available internet services. But, during communications via computer, security is the primary concern. The malware is a threat that mostly affects computerized devices. Malware identification is a complex issue present in the internet of things (IoT) sector. Implementing a cost‐effective malware protection model to recognize high‐scale malware is significant. Conventional approaches for malware identification suffer from data loss or high‐dimensional feature sets. To combat these difficulties, this work presents a new technique for automatic malware identification by utilizing deep learning. The proposed model introduces an automatic malware detection framework under the Windows platform. At first, the application programming interfaces (API) call sequence data is collected from the available data resource. Further, the temporal features, spatial features, and statistical features are extracted from the input data that become useful information for malware samples. Then, the three sets of extracted features are subjected to the multi‐scale feature fusion‐based 1dimensional convolutional neural network (1DCNN) with a gated recurrent unit (MFF‐1DCGRU) to identify malware detection. An extensive experiment evaluates the proposed automated malware detection approach using two dataset namely malware analysis datasets: API call sequences and API‐call‐sequences. On the malware analysis datasets: API call sequences dataset, the developed model achieved an accuracy of 96.30%. Similarly, when considering the API‐call‐sequences dataset, it outperformed baseline models by achieving improvements of 7.4% over the autoencoder, 5.4% over Bi‐LSTM, 3.2% over 1DCNN, and 1.1% over GRU, respectively. Hence, the research outcome revealed that the recommended method performs better in the automatic recognition of malware in the computer system.
Angusamy et al. (Wed,) studied this question.