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

Abstract

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.