MALWARE ANALYSIS ON MOBILE PHONE

Reducing physical distances within cyberspace allowed information to travel almost instantly while the international cybercrime phenomenon has risen almost inevitably. Most of the slips in cybersecurity come into being due to a poor management of the online environment, due to a lack of knowledge within the technological features in current use and due to the improper way to work with data. This issue leads to the necessity of outlining a cybersecurity culture along with respecting the rights related to online privacy. There is not yet a universally valid solution to the complex problem of cybersecurity, the field being very dynamic, the technological progress - very fast, while the individual cannot find enough self protection against organizations specialized in fraud, fake news dissemination, data retrieval, storage and destruction. The attitude towards cybersecurity does not represent a special case, it only completes and supports individual’s attitude towards society. We must yet mention that these alarming scenarios are backed up by the business environment which looks at selling security solutions in growing numbers. Malware protection programs represent one of the most important measures to ensure cybersecurity measures. The dynamics of the internet and the frequency of attacks impose the use of efficient methods that identify suspicious behaviour. The situations in which signature detection fails, machine learning use can lead to acceptable results. This type of computational learning (supervised learning) implies using a set of data that make the ins and outs to be known, while the resulting model determines the connections and makes predictions over a new set of data. The present paper showcases the research done so far within The Integrated Software Platform for the mobile malware analysis.