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Intrusion detection system in software-defined networks

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Resumo:Software-Defined Networking technologies represent a recent cutting-edge paradigm in network management, offering unprecedented flexibility and scalability. As the adoption of SDN continues to grow, so does the urgency of studying methods to enhance its security. It is the critical importance of understanding and fortifying SDN security, given its pivotal role in the modern digital ecosystem. With the ever-evolving threat landscape, research into innovative security measures is essential to ensure the integrity, confidentiality, and availability of network resources in this dynamic and transformative technology, ultimately safeguarding the reliability and functionality of our interconnected world. This research presents a novel approach to enhancing security in Software-Defined Networking through the development of an initial Intrusion Detection System. The IDS offers a scalable solution, facilitating the transmission and storage of network traffic with robust support for failure recovery across multiple nodes. Additionally, an innovative analysis module incorporates artificial intelligence (AI) to predict the nature of network traffic, effectively distinguishing between malicious and benign data. The system integrates a diverse range of technologies and tools, enabling the processing and analysis of network traffic data from PCAP files, thus contributing to the reinforcement of SDN security.
Autores principais:Leite, Vinicius Lopes
Assunto:Software defined network IDS Cybersecurity
Ano:2023
País:Portugal
Tipo de documento:dissertação de mestrado
Tipo de acesso:acesso aberto
Instituição associada:Instituto Politécnico de Bragança
Idioma:inglês
Origem:Biblioteca Digital do IPB

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