Browsing by Author "Nawarathna, C"

Now showing 1 - 3 of 3

Embargo
Enhancing the security of OLSR protocol using reinforcement learning
(IEEE, 2017-09-14) Priyadarshani, H; Jayasekara, N; Chathuranga, L; Kesavan, K; Nawarathna, C; Sampath, K. K; Liyanapathirana, C; Rupasinghe, L
Mobile ad-hoc networks are used in various institutions such as the military, hospitals, and various businesses. Due to their dynamic mobile structure-free and self-adaptive nature, they are ideal to be used in emergency situations where the resources available are limited. The wireless range of the devices in the MANET is narrow. In order to communicate with the desired device often times it is necessary to use intermediate devices between the source and the destination. Therefore, it is important to secure sensitive information sent through intermediate devices. OLSR is a widely used MANET routing protocol. Although OLSR protocol has excelled in performance and reliability, it is rather poor in security. In this context, we attempt to improve the security of OLSR protocol with the aid of Q-Learning by selecting trustworthy nodes to forward messages. Behavior of the nodes is used to determine the trust of the nodes.
Embargo
Improving trusted routing by identifying malicious nodes in a MANET using reinforcement learning
(IEEE, 2017-09-06) Mayadunna, H; De Silva, S. L; Wedage, L; Pabasara, S; Rupasinghe, L; Liyanapathirana, C; Kesavan, K; Nawarathna, C; Sampath, K. K
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.
Embargo
Trustworthy MANET routing ESTAODV implementation
(IEEE, 2018-12-21) Rupasinghe, L; Nawarathna, C; Niroshan, M. A. J; Kodithuwakku, K. A. H; Kularathna, M. A. S. H; Liyanage, S. C. G
Mobile Ad hoc networks (MANETs) are a collection of mobile nodes which can move randomly and communicate with each other wirelessly with a dynamic topology. Frequent topological changes are happened in the network because of this dynamic nature. MANETs are popularly used for unmanned systems and networks where telecommunication is absent. Providing security in Mobile Ad hoc Network has become a crucial problem due to the unpredictable motions of misbehaving network nodes. Therefore, a security mechanism is required to distinguish between trustworthy and malicious nodes. Secured routing has become the most active research area in the MANET field due to these challenges and the significances. In this article, a trust-based MANET routing model is proposed to detect misbehaving nodes over Ad hoc On-Demand Distance Vector (AODV) routing protocol. Trust is calculated based on the previous individual experiences and the recommendations of other neighbor nodes. Trust Recommendation Request (TRR) protocol which allows nodes to exchange recommendations regarding their neighbors is introduced in this proposed model. In order to identify malicious nodes, nodes are categorized into trust levels. After detecting malicious nodes, they will be isolated from the network and broadcasted to their neighbor nodes. As a result, network nodes will be able to use the most secure route for packet transmission instead of the shortest path.