Android Malware Detection Using Multi-Layer Perceptron Approach for Cybersecurity

Malware development is a persistent problem for devices that operate on Android, making it a severe danger to cybersecurity and forcing the search for ways to counter it. Many new algorithms have come up in the last few years, machine learning approaches can be viewed as effective tools for automation of the detection process. The focus of this paper is the survey of the Multi-Layer Perceptron (MLP), a form of artificial neural network, used in the determination of Android malware. The review sums the current literature on MLP-based detection systems in terms of feature extraction method, types of datasets used, guidelines for measurement, and issues. Also, it analyses the advantages and disadvantages of MLP in this regard and offers ideas for further research to improve the effectiveness of Android malicious application detection for computer security.