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EN
Individual protection of autonomous systems using simple analysis of transmitted messages is unfortunately becoming insufficient. There is a clear need for new solutions using data from multiple sources, integrating various methods, mechanisms and algorithms, including Big Data processing and data classification techniques using artificial intelligence methods. The quantity, quality, reliability and timeliness of data and information about the network situation, as well as the speed of its processing, determine the effectiveness of protection. The paper presents examples of the application of various artificial intelligence techniques for detecting attacks on ICT systems. Attention is focused on the application of deep learning methods for the detection of malicious applications installed on mobile devices. The effectiveness of the presented solutions was confirmed by numerous simulation experiments conducted on real data. Promising results were obtained.
EN
As the threats from cyberspace to IT systems (information technology) and digital OT systems (operational technologies) using ICT technologies (information and communication technologies) grow exponentially, while at the same time the scale of the use of digital data collecting, processing and sharing for the needs of many national economy areas and to support functioning of the state in terms of, for example, defence, health care, education or citizen services, building awareness of the risks and skills to secure networks, systems and digital services against cyber threats becomes crucial. A new type of structures called ISACs (Information Sharing and Analysis Centres) play an important role in this respect. The co-authors, based on the provisions of the law and their own experience in ISAC-Kolej and ISAC-GIG centers, present the ecosystem around ISAC centers, their tasks and challenges.
EN
Biometria jako technika pomiarów istot żywych skupia się na automatycznym rozpoznawaniu jednostek na podstawie ich cech fizycznych. Jedną z najczęściej stosowanych metod biometrycznego uwierzytelniania jest biometria twarzy. Metoda ta jest dość powszechnie stosowana w smartfonach, paszportach oraz innych urządzeniach i systemach służących do weryfikacji tożsamości lub wymagających uwierzytelnienia. Wraz z rosnącą popularnością biometrii twarzy pojawiają się również obawy dotyczące bezpieczeństwa, szczególnie związane z atakami prezentacyjnymi. Celem tych ataków jest oszukanie systemów biometrycznych. Wykorzystuje się do tego celu różne materiały i dostępne dane, m.in.: wydrukowane zdjęcia, nagrania wideo i maski. Wykrywanie tego typu ataków oraz ochrona wymagają stosowania różnego typu środków bezpieczeństwa oraz zaawansowanych algorytmów detekcji. W artykule są omawiane różne metody wykrywania ataków prezentacyjnych, w tym podejścia wykorzystujące interakcję użytkownika z systemem, analizę właściwości obrazu oraz metody sztucznej inteligencji. Szczególna uwaga jest zwrócona na jakość zbiorów danych uczących wykorzystywanych do trenowania algorytmów, w tym ich zrównoważenie i zróżnicowanie zawartych w nich danych, a także na konieczność stałego rozwijania mechanizmów bezpieczeństwa w celu ochrony systemów uwierzytelniania biometrycznego przed ewoluującymi zagrożeniami.
EN
In recent years, there has been a significant increase in threats to children’s safety in cyberspace. The most serious of these include children’s participation in illegal online activities and the production of sexually explicit content involving them. Therefore, it is of fundamental importance to build awareness of cyber threats among our society’s youngest members and teach them skills for the safe use of products and services assigned to cyberspace. A key action for effectively protecting children in this environment is the early detection and reporting to the relevant authorities of illegal behavior and child abuse content. Teams such as Dyżurnet.pl, whose tasks currently include responding to potentially illegal content reported by cyberspace users, and in the near future, possibly also conducting proactive activities in this area, play an important role here. The experience of Dyżurnet.pl clearly shows that effective detection of such content requires automation of activities and appropriate IT tools. This paper presents a novel network monitoring and decision support system using artificial intelligence methods, including deep learning, to automatically detect potentially harmful material, such as Child Sexual Abuse Material (CSAM), erotic content involving children, pornographic content with a created or processed image of a child and pornography involving adults.
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