DETECTION OF MALICIOUS ACTIVITY USING A NEURAL NETWORK FOR CONTINUOUS OPERATION

This article describes the problem of detecting malicious programs in running systems of users of mobile applications. Because users can download any application on their phone, which over time can pull up additional settings, which can store malicious routines for monitoring both personal life and their personal data, such as logins, passwords, bank data. The detection of such routines is based on dynamic analysis and is formulated as a weakly controlled problem. The article contains an analysis of information on the development of researchers who worked on detection models and methods such as: statistical and dynamic intrusion detection methods, anomaly detection model, settings classification methods, machine and deep learning methods. Machine learning, and especially deep learning, has become an extremely useful and interesting topic in cybersecurity over the past few years. In this context, the detection of malicious software has received considerable attention. The article considers the problem of detecting the activity of malicious software of mobile operating systems in the time domain by analyzing behavioral sequences of a large amount of industrial data. When malware exec