Android Malware Detection Using Deep Learning Methods *

Artificial intelligence is a popular and rapidly growing branch of computer science. For several years, the number of artificial intelligence applications in various fields has increased. Deep learning is a subcategory of machine learning and involves the use of more complex models in existing tasks. Its use allows the extraction of more features, thanks to which the precision of recognition is greater. Malware is a software, considered intrusive and harmful, which has access to the user's confidential information and subsequent use. Due to the growing popularity of mobile devices, the number of malware for these devices is also increasing. There are many malware detection solutions, mainly based on the signature of the applications, however, due to the development of malware, these methods become less effective. Many publications have proposed the use of artificial intelligence in this field. The work describes the basic concepts of the Android and deep learning algorithms. The work focuses on testing several features of the application and checking several deep learning algorithms. In addition, a solution based on the use of binary file representation and self-organizing maps was proposed.