Author(s) :
R. Arun Kumar, J. B. Shriram
Article Name :
Advanced Malware detection with Inception V3 for Enhanced Computer Security
Abstract :
In the domain of computer security, identifying and categorizing malware is of utmost importance. Malicious software frequently employs sophisticated methods to avoid detection, underscoring the urgency of early identification to protect computer networks and the Internet from extensive harm. This study tackles the obstacles related to malware detection and presents an innovative solution leveraging Convolutional Neural Network (CNN) classification through Inception V3.The input dataset is pre-processed to make the procedure easier. Initial stage of pre-processing is performed on dataset followed by data visualization. These data visualizations help identify the abnormalities and their existence or absence, as well as the structure of data. The categorization process is performed by Inception V3, a CNN-based classification network. The Inception V3 is compared with the most accurate models to assess the effectiveness of proposed system. The implementation of the proposed approach is carried out using the Python programming language. The outcomes of this research showcase the proficiency of the developed system, highlighting its potential in enhancing the accuracy and efficiency of malware detection.
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