Biblio

Trusted routing is a hot spot in network security. Lots of efforts have been made on trusted routing validation for Interior Gateway Protocols (IGP), e.g., using Public Key Infrastructure (PKI) to enhance the security of protocols, or routing monitoring systems. However, the former is limited by further deployment in the practical Internet, the latter depends on a complete, accurate, and fresh knowledge base-this is still a big challenge (Internet Service Providers (ISPs) are not willing to leak their routing policies). In this paper, inspired by the idea of centrally controlling in Software Defined Network (SDN), we propose a CENtrally Trusted Routing vAlidation framework, named CENTRA, which can automated collect routing information, centrally detect anomaly and deliver secure routing policy. We implement the proposed framework using NETCONF as the communication protocol and YANG as the data model. The experimental results reveal that CENTRA can detect and block anomalous routing in real time. Comparing to existing secure routing mechanism, CENTRA improves the detection efficiency and real-time significantly.

Mobile ad-hoc networks (MANETs) are decentralized and self-organizing communication systems. They have become pervasive in the current technological framework. MANETs have become a vital solution to the services that need flexible establishments, dynamic and wireless connections such as military operations, healthcare systems, vehicular networks, mobile conferences, etc. Hence it is more important to estimate the trustworthiness of moving devices. In this research, we have proposed a model to improve a trusted routing in mobile ad-hoc networks by identifying malicious nodes. The proposed system uses Reinforcement Learning (RL) agent that learns to detect malicious nodes. The work focuses on a MANET with Ad-hoc On-demand Distance Vector (AODV) Protocol. Most of the systems were developed with the assumption of a small network with limited number of neighbours. But with the introduction of reinforcement learning concepts this work tries to minimize those limitations. The main objective of the research is to introduce a new model which has the capability to detect malicious nodes that decrease the performance of a MANET significantly. The malicious behaviour is simulated with black holes that move randomly across the network. After identifying the technology stack and concepts of RL, system design was designed and the implementation was carried out. Then tests were performed and defects and further improvements were identified. The research deliverables concluded that the proposed model arranges for highly accurate and reliable trust improvement by detecting malicious nodes in a dynamic MANET environment.