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Title A Hybrid DL-Based Framework to Classify Malware using Mexican Hat Wavelet Function

Author(s) Afrasiab Younis

Abstract Detecting and categorizing malware represents a substantial and demanding undertaking within the realm of information security and various other computer-related domains. Millions of malicious files are detected annually. The high volume is largely due to malware authors using mutations to evade detection, Malware variants are constantly evolving through the use of advanced obfuscation and packing methods, making detection and classification increasingly difficult. In order to efficiently examine and categorize a substantial volume of files, it becomes imperative to group them and ascertain their behavioral characteristics to classify them effectively. In recent, most malware classification techniques have been based on machine learning or deep learning models. These models work with the train and test. The models are trained with the features, for instance, opcode sequence, API calls, signature, etc. Recently, many deep learning techniques have been proposed for Alex Net Network, Resnet-50 Network, and Hybrid (AlexNet-Resnet-50). These models work well in terms of accuracy, Sensitivity, and so forth. However, these models are complex in nature and need high computational power. In order to adequately confront the difficulty presented by emerging malware variations, it becomes essential to employ alternative approaches, as conventional artificial intelligence and machine learning algorithms are no longer capable of identifying all intricate and constantly changing variants. A promising solution is deep learning, which differs from traditional machine learning. This study proposes a Mexican hat wavelet function that classifies malware variants through a hybrid deep learning model in this approach, malware samples undergo conversion into grayscale images before being fed into the DL system. Following the image acquisition section, the proposed method employs the convolution layers of the hybrid architecture to extract high-level malware features from the malware images with cloud-based architecture to decrease the computational intricacy, and neural network complexity to achieve higher accuracy. Upon subjecting the proposed method to testing using the MALIMG dataset, an accuracy of 99% was achieved. Similarly, when applied to the MALEVIS dataset, an accuracy of 97.12% was attained, outperforming the majority of machine learning-based methods employed for malware detection

Type Thesis/Dissertation MS

Faculty Engineering and Computer Science

Department Computer Science

Language English

Publication Date 2024-01-08

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