Introduction

The development of the Internet—without which it is hard to imagine today’s world—entails the need to ensure the safety of its users. This problem is of an interdisciplinary nature, as researchers in various fields of IT, mathematics, psychology (behavioral analysis) to medicine (telemedicine) are working on it. The importance of this problem has increased significantly with the spread of the use of Internet resources during the COVID-19 pandemic, when the most important aspects of our lives, in particular those related to payments, were transferred to the virtual world. At the same time, the dynamic development of technology brought new challenges for the creators of IT systems, unknown until two decades before, related to ensuring the security of data collected and stored in social networks, which can be used to build user profiles and influence the behavior of their owners based on this knowledge. Another consequence of the COVID pandemic is that it has resulted in a huge increase in the number of nonadvanced Internet users-persons with very little experience and knowledge of the risks involved and with a low level of digital competence. This group includes children and young persons studying, but also elderly persons, who had to switch to performing their work duties remotely from day to day. One of the biggest threats to data security is data theft. It is obvious that data leakage and systems compromising have a very negative impact on the functioning of governments, local governments,private companies, hospitals, etc. Such attacks most often exploit gaps in firewalls or unified threat management (UTM) tools. Unfortunately, detection of such software errors or misconfigurations is possible only after they occur. However, it is worth noting that the amount of information that is transmitted by devices active in networks is impossible to analyze by a human without appropriate tools. Such issues can be attributed to the class of problems from the family of Big Data. This book presents techniques for ensuring the security of IT systems described in the literature and proprietary solutions based on artificial intelligence methods. The topic is extremely broad, so only selected issues will be discussed, in particular on the areas of profiling users and systems of the network, preventing leaks of sensitive (personal) data, crucial for the functioning of entities,and defense against phishing attacks. Furthermore, innovative structures of so-called glial networks will be presented. They might be helpful when we attempt to interpret the knowledge stored in convolutional networks. It is worth emphasizing that all the proposed solutions can be used after a simple adaptation in various areas, not only those related to security. Their advantage over methods described in the current literature will be presented. Today, the problem of information security concerns every field in which computers are used. Large corporations, public institutions such as municipal authorities, schools, and small companies and households need to ensure the security of their system and network resources. The degree of protection depends to a large degree on the available financial resources. As mentioned earlier, a significant factor in this process is the human and their knowledge and skills. State-of-the-art network solutions use artificial intelligence algorithms to support data analysis, for example, through grouping and visualization.