04639nam a22005895i 4500 978-3-031-53854-4 DE-He213 20251030111432.0 cr nn 008mamaa 240219s2024 sz | s |||| 0|eng d 9783031538544 978-3-031-53854-4 10.1007/978-3-031-53854-4 doi TA345-345.5 UN bicssc COM018000 bisacsh UN thema Korytkowski, Marcin. author. (orcid)0000-0002-6002-2733 https://orcid.org/0000-0002-6002-2733 aut http://id.loc.gov/vocabulary/relators/aut 71324 Advanced Techniques of Artificial Intelligence in IT Security Systems [electronic resource] / by Marcin Korytkowski. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. V, 91 p. 38 illus., 35 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda Studies in Big Data, 2197-6511 ; 146 Artificial neural networks -- Initialization of weights values in neural networks -- Convolution neural networks -- Application of artificial intelligence methods in profiling of computer network users -- Profiling users using the random forest algorithm -- Convolutional networks in user profiling -- User profiling using recurrent-convolutional networks -- Detection of network traffic anomalies using OC-NN. The book explores how modern technologies, including artificial intelligence and neural networks, are being used to enhance cybersecurity. In today's world, the development of the Internet is nothing short of transformative, affecting every aspect of our lives. Ensuring the safety of its users is a paramount concern, and it requires a diverse set of disciplines to address. Researchers from various fields, including IT, mathematics, psychology, and medicine, are collectively working to tackle this interdisciplinary challenge. The significance of this issue has been magnified by the COVID-19 pandemic, which forced many aspects of our lives into the digital realm, from online payments to remote work and education. This shift brought new security challenges, with data privacy and system integrity taking center stage. It delves into the intricacies of Big Data by having to analyze an immense volume of network traffic data that can only be effectively analyzed with specialized tools. Real-time threat detection is critical, and the book sheds light on cutting-edge approaches to achieving this goal. The content of the book covers a broad spectrum of topics related to IT system security, from user and system profiling to preventing data leaks and defending against phishing attacks. Additionally, innovative concepts such as “glial networks” are introduced, offering new ways to interpret knowledge stored in convolutional networks. These solutions are not limited to security alone; they have applications across various domains. The book highlights the advantages of these cutting-edge approaches over existing methods, demonstrating their relevance to large corporations, public institutions, schools, small businesses, and households. In a world where security threats are constantly evolving, this book is a valuable resource for understanding the dynamic landscape of network security and the role of artificial intelligence in safeguarding our digital ecosystems. Engineering Data processing. Computational intelligence. 64052 Data protection. Artificial intelligence. 58441 Big data. 57216 Data Engineering. 66059 Computational Intelligence. 64052 Data and Information Security. 66492 Artificial Intelligence. 58441 Big Data. 57216 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031538537 Printed edition: 9783031538551 Printed edition: 9783031538568 Studies in Big Data, 2197-6511 ; 146 68724 https://doi.org/10.1007/978-3-031-53854-4 ZDB-2-INR ZDB-2-SXIT 57724 57724