Due to high versatility and widespread adoption, PDF documents are widely exploited for launching attacks by cyber criminals. PDFs have been conventionally utilized as an effective method for spreading malware. Automated detection and classification of PDF malware are essential to accomplish security. Latest developments of artificial intelligence (AI) and deep learning (DL) models pave a way for automated detection of PDF malware. In this view, this article develops an Invasive Weed Optimization with Stacked Long Short Term Memory (IWO-S-LSTM) technique for PDF malware detection and classification. The presented IWO-S-LSTM model focuses on the recognition and classification of different kinds of malware that exist in PDF documents. The proposed IWO-S-LSTM model initially undergoes pre-processing in two stages namely categorical encoding and null value removal. Besides, autoencoder (AE) based outlier detection approach is presented to remove the existence of outliers. In addition, S-LSTM model is utilized to detect and classify PDF malware. Finally, IWO algorithm is applied to fine tune the hyperparameters involved in the S-LSTM model. To determine the enhanced outcomes of the IWO-S-LSTM model, a series of simulations were executed on two benchmark datasets. The experimental outcomes outperformed the promising performance of the IWO-S-LSTM technique on the other approaches.

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Cybercrime has increased considerably in recent times by creating new methods of stealing, changing, and destroying data in daily lives. Portable Document Format (PDF) has been traditionally utilized as a popular way of spreading malware. The recent advances of machine learning (ML) and deep learning (DL) models are utilized to detect and classify malware. With this motivation, this study focuses on the design of mayfly optimization with a deep belief network for PDF malware detection and classification (MFODBN-MDC) technique. The major intention of the MFODBN-MDC technique is for identifying and classifying the presence of malware exist in the PDFs. The proposed MFODBN-MDC method derives a new MFO algorithm for the optimal selection of feature subsets. In addition, Adamax optimizer with the DBN model is used for PDF malware detection and classification. The design of the MFO algorithm to select features and Adamax based hyperparameter tuning for PDF malware detection and classification demonstrates the novelty of the work. For demonstrating the improved outcomes of the MFODBN-MDC model, a wide range of simulations are executed, and the results are assessed in various aspects. The comparison study highlighted the enhanced outcomes of the MFODBN-MDC model over the existing techniques with maximum precision, recall, and F1 score of 97.42%, 97.33%, and 97.33%, respectively.