JoWUA

Abstract

Cybercriminals have become increasingly interested in the spread of critical information, particularly in interpersonal contact and mass distribution of programs and file downloads. This has heightened researchers' awareness of the rampant spread of malware and data breaches. It is anticipated that the number and intensity of malicious software will continue to rise, underscoring the imperative need for strong security architectures. This is especially critical for mobile networks and ubiquitous computing security, given the growing threat posed by hackers. The proposed project involves creating a new dataset using a strictly controlled, shared sample pool to address security threats in wireless mobile networks, leveraging dynamic analysis techniques for malware detection. The dataset aims to enhance the recognition of malicious software by leveraging methods such as encryption and obfuscation. The suggested classification algorithm is CNN-LSTM, a combination of Convolutional Neural Networks (CNNs) and the Long Short-Term Memory (LSTM) model, which excels at learning complex, sequential features. The CNN and LSTM models were tested on a dataset comprising more than 10,000 malware samples and achieved accuracies of 98% and 97%, respectively. These findings demonstrate how deep learning models can be used to enhance the security of mobile networks and provide effective protection against emerging threats in mobile and ubiquitous computing systems, in a highly beneficial way.