Enhancing Malware Detection Efficiency through CNN-Based Image Classification in a User-Friendly Web Portal
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Abstract
The proposed CNN-based malware detection web portal classifies images on a unique, self-made dataset to identify malware files as input. There are many different types of malware out there, but no method can detect them all. An anti-virus programme could be created that enforces malware image classification for the aforementioned scenarios as opposed to the traditional signature-based methods used by the majority of anti-virus programmes currently available in the market, which are time-consuming and ineffective because they rely just on signatures of previous malware attacks and need to be updated regularly. The fact that some malware is encrypted and requires a significant amount of computing power to decrypt makes this strategy ineffective for identifying all malware that accesses the network. As a result, fresh malware cannot be detected because this method simulates the behavior of malware samples and matches it to new programs. An online portal with a candid user interface will be used to deploy the proposed Deep-learning based malware detection algorithm. The file to be tested or classified will be uploaded onto the website
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Malware Dataset, Classification, Malware to Image Conversion, Malware Detection Web Portal, Convolutional Neural Network
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