HYBRID DEEP LEARNING EFFECTIVENESS OF IMAGE-BASED MALWARE DETECTION

Abstract

The current high rate of malware variant production each day produces hundreds of thousands of new variants making signature detection methods ineffective. Deep learning patterns succeed at detecting malware through converting malware binaries to gray forms and detecting complex hidden patterns inside these files. CNNs and LSTMs function sub-opportunistically as standalone models to prevent new malware types. This system uses CNNs and LSTMs as components which extract spatial features through spatial extraction followed by temporal pattern processing to maximize malware classification results. The research team conducted testing operations by processing Malign and Blended datasets through images of various resolution settings. The combined network achieves 0.99 F1 score along with 0.96 accuracy that represents superior performance than the isolated individual models based on result measurements. The hybrid approach shows strong performance because it maintains effective adversarial attack resistance while maintaining high malware type recognition capabilities. According to this research the significant hurdles relate to both expensive training expenses needed to handle large datasets and the need for plenty of labeled malware image data. Technical challenges persist in resolving current model update requirements to confront adaptive malware methods in their current format. Future malware detection research goals to enhance performance by integrating three main advancements between transfer learning capabilities and lightweight architecture designs and real-time feedback protocols. Through the research scientists acquire the capability to build stronger defensive measures against intricate malware threats targeting computer systems.