Year: 2025
Type: article
Abstract: One item that almost everyone has in their everyday life is a mobile phone, and Android-based phones dominate the market. Numerous apps can be found on the internet, and individuals frequently download applications from unidentified sites that may harm their mobile devices. Therefore, these dangerous apps may risk their money, privacy, and data security. In order to prevent consumers from installing harmful applications and protect their devices, a machine learning model can be used to detect such apps before installation. Given the complexity and dynamic nature of the malwares, traditional security approaches alone may not be suitable enough to protect users effectively. Machine learning (ML) techniques offer a promising avenue for enhancing malware detection and mitigation efforts. We have been motivated to implement various well known ML Algorithms such as Support Vector Machine, Random Forest decision trees, and Naive Bayes. Futher we will also implement Artificial Neural Networks (ANNs) or simple Neural Networks to find the efficacy of the Malware Detection.
Language: English
Subfield: Signal Processing
Field: Computer Science
Domain: Physical Sciences
Open Access status: bronze
OpenAlex ID: W4410097820