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The popularity of Windows attracts the attention of hackers/cyber-attackers,making Windows devices the primary target of malware attacks in recent years.Several sophisticated malware variants and anti-detection methods have beensignificantly enhanced and as a result, traditional malware detectiontechniques have become less effective. This work presents MalBehavD-V1, a newbehavioural dataset of Windows Application Programming Interface (API) callsextracted from benign and malware executable files using the dynamic analysisapproach. In addition, we present MalDetConV, a new automated behaviour-basedframework for detecting both existing and zero-day malware attacks. MalDetConvuses a text processing-based encoder to transform features of API calls into asuitable format supported by deep learning models. It then uses a hybrid ofconvolutional neural network (CNN) and bidirectional gated recurrent unit(CNN-BiGRU) automatic feature extractor to select high-level features of theAPI Calls which are then fed to a fully connected neural network module formalware classification. MalDetConv also uses an explainable component thatreveals features that contributed to the final classification outcome, helpingthe decision-making process for security analysts. The performance of theproposed framework is evaluated using our MalBehavD-V1 dataset and otherbenchmark datasets. The detection results demonstrate the effectiveness ofMalDetConv over the state-of-the-art techniques with detection accuracy of96.10%, 95.73%, 98.18%, and 99.93% achieved while detecting unseen malware fromMalBehavD-V1, Allan and John, Brazilian, and Ki-D datasets, respectively. Theexperimental results show that MalDetConv is highly accurate in detecting bothknown and zero-day malware attacks on Windows devices.

MalDetConv: Automated Behaviour-based Malware Detection Framework Based on Natural Language Processing and Deep Learning Techniques