Invasive weed optimization with stacked long short term memory for PDF malware detection and classification

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