Malware Detection Classification using Recurrent Neural Network

Nowadays, increasing numbers of malicious programs are becoming a serious problem, which increases the need for automated detection and categorization of potential threats. These attacks often use undetected malware that is not recognized by the security vendor, making it difficult to protect the endpoints from viruses. Existing methods have been proposed to detect malware. However, as malware variations develop, they can lead to misdiagnosis and are difficult to diagnose accurately. To address this problem, in this work introduces a Recurrent Neural Network (RNN) to identify the malware or benign based on extract features using Information Gain Absolute Feature Selection (IGAFS) technique. First, Malware detection dataset is collected from kaggle repository. Then the proposed pre-process the dataset for removing null and noisy values to prepare the dataset. Next, the proposed Information Gain Absolute Feature Selection (IGAFS) technique is used to select most relevant features for malware from the pre-processed dataset. Selected features are trained into Recurrent Neural Network (RNN) method to classify as malware or not with better accuracy and false rate. The experimental result provides greater performance compared with previous methods.