Journal of Cybersecurity and Information Management JCIM 2690-6775 2769-7851 10.54216/JCIM https://www.americaspg.com/journals/show/3736 2019 2019 M. Tech, Dept of CSE, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur, A.P, India Thippireddy Thippireddy Professor, Dept CSE, Koneru Lakshmaiah Educational Foundation, Vaddeswaram, Guntur A.P, India Gera Pradeepini In the last few years, technology has developed so rapidly that many malware applications are available in the software market. Cybercrimes are increasing day by day with the usage of malware applications. Traditional approaches are not as effective in detecting malware. This study introduces a novel method for distinguishing malware from benign software applications using deep learning models like Denoising Autoencoder and Convolutional Neural Network. Initially, we extract binary code from the applications and transform it into grayscale images. Then, utilizing a denoising autoencoder, we improve the quality of the grayscale images by eliminating noise, and the Convolutional Neural Network uses processed images as input. Finally, the Convolutional Neural Network is employed to differentiate between malicious and benign applications. We test this methodology on the dataset that contains 10,810 malware and 1082 benign files. The suggested model obtains an accuracy of 97% and an F1-score of 96% and performs better than some traditional methods. 2025 2025 243 251 10.54216/JCIM.160117 https://www.americaspg.com/articleinfo/2/show/3736