Inteligência artificial para identificar e tratar ataques de negação de serviço em redes baseadas em software

Abstract

Introduction: The article aims to explore the relationship between Artificial Intelligence (AI) and distributed denial of service (DDoS) attacks in SDN-based networks. The objective is to understand how AI can improve and aggravate cyber threats, developing advanced defense and attack detection strategies to contribute to a safer and more resilient digital environment in the area of Information Systems. Furthermore, the importance of AI in simulating human intelligence and machine learning to deal with large data sets and improve its skills over time is highlighted. Methodology: The study was based on the characteristics of the systematic review, following the PRISMA guidelines. Inclusion and exclusion criteria were used to select the articles, considering the relationship between AI and the detection of DDoS attacks, the mention of keywords in the title, the approach to DDoS attacks and publication between 2019 and 2023. The process The article selection process consisted of screening the title, abstract and full text, resulting in the selection of 11 articles relevant to the research. Results: The global analysis of the study selection process resulted in the identification of 11 articles relevant to the research, highlighting the importance of AI in detecting DDoS attacks today. Furthermore, the need for holistic and adaptive approaches for the effective detection of DDoS attacks is highlighted, considering the specific characteristics of networks and the choice of promising techniques and tools to meet the needs of organizations. Conclusion: The study concludes that there is no determined or "best" method for detecting DDoS attacks, with the effectiveness of detection depending on several factors, such as the type of attack, the type of network and the available resources.