Banca de DEFESA: NILTON FLÁVIO SOUSA SEIXAS

Uma banca de DEFESA de MESTRADO foi cadastrada pelo programa.
DISCENTE : NILTON FLÁVIO SOUSA SEIXAS
DATA : 30/05/2019
HORA: 14:00
LOCAL: Instituto de Matemática e Estatística
TÍTULO:

PRIMO: An ICN Model for Preventing Denial of Service Attacks by Flooding of False Interests

PALAVRAS-CHAVES:

icn; primo; ddos

PÁGINAS: 100
GRANDE ÁREA: Ciências Exatas e da Terra
ÁREA: Ciência da Computação
RESUMO:

The current Internet architecture is designed to share resources available to communities. However, the way the Internet is used today has made the architecture incompatible with the explosive demand for distribution and data collection. Network users are interested in the content and not in their host. The Named Data Networking (NDN) is an Internet architecture that adapts to this new demand, reducing network traffic caused by the need to obtain the same content, and changes the orientation of data security , which currently secures the connections by which they will be transported, into the data itself, making the architecture resilient to many recurring problems and difficult to treat in the current architecture, such as the denial of service attack. However, these attacks have been adapted to this architecture and it is necessary to mitigate them so that the architecture is considered safe. In this context, PRIMO is proposed, an NDN-based model that forges a router-producer collaboration to detect, mitigate and prevent Denial of Service attacks by Flood of Interests (IFA). The components of the PRIMO model cooperate to: (i) detect an ongoing IFA attack, (ii) mitigate the attack by distinguishing between legitimate and false interests, (iii) prevent attack from reaching the core of the network after mitigation, and the network of new instances of attacks with prefixes already used. The experimental results show that PRIMO is effective in: detecting the attack, mitigating it by distinguishing between legitimate and false interests, preserving network performance and preventing further occurrences of an ongoing attack. The contributions of this dissertation are: i) a model with collaborative mechanisms for the detection, mitigation and prevention of IFA and ii) a mechanism for identifying false interests.

MEMBROS DA BANCA:
Externo à Instituição - ANTONIO AUGUSTO DE ARAGÃO ROCHA - UFF
Interno - 1764465 - LEOBINO NASCIMENTO SAMPAIO
Interno - 1850683 - MAYCON LEONE MACIEL PEIXOTO