Browsing by Author "Sampath, K. K"

Now showing 1 - 4 of 4

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
Policies based container migration using cross-cloud management platform
(IEEE, 2018-12-21) Janarthanan, K; Peramune, P. R. L. C; Ranaweera, A. T; Krishnamohan, T; Rupasinghe, L; Sampath, K. K; Liyanapathirana, C
Over the last decade, cloud computing has helped in variety of ways to humanity. Mainly in the ways of, achieving Disaster Recovery (DR) and in protecting the end users' data and Anywhere, Any device, Anytime access to the users' data. This research further helps people and organization to overcome common problems related to clouds such as, vendor-lock in and legal regulation. In today's world, more and more organizations are adopting the cloud services mainly because of the reliability and affordability provided by them. However, there are several drawbacks faced by the cloud users and cloud service providers. Apart from the security perspective, the cloud users are facing challenges in control and visibility, lack of standard service interfaces, difficulty in deploying applications across multiple clouds and vendor lock-in. Also, cloud service providers are facing challenges in degradation of the quality of service provided because of the distance between cloud data center and the end user and unexpected interruption of services etc. The above problems can be reduced to a greater extent or mitigated by adopting Multi Cross Cloud Infrastructure. This benefits the cloud users to receive the best quality services to increase their productivity. Hence, the main aim of this research is to build a common platform to manage the cross-cloud environment particularly Microsoft AZURE cloud and Amazon Web Services (AWS) with multiple features such as policies based container migration among the clouds and finding the best virtual machines (VM) across the clouds to deploy new containers. Cross-cloud management platform can be implemented within an organization or Enterprise and is used by the 3rd level support team such as Infrastructure team to provide multiple services (E.g. - Delivering application containers, Migration of containers on request) to end users based on some service level agreements (SLA) with more control and visibility.
Embargo
Policies Based Container Migration Using Cross-Cloud Management Platform
(IEEE, 2018-12-21) Janarthanan, K; Peramune, P. R. L. C; Ranaweera, A. T; Krishnamohan, T; Rupasinghe, L; Sampath, K. K; Liyanapathirana, C
Over the last decade, cloud computing has helped in variety of ways to humanity. Mainly in the ways of, achieving Disaster Recovery (DR) and in protecting the end users' data and Anywhere, Any device, Anytime access to the users' data. This research further helps people and organization to overcome common problems related to clouds such as, vendor-lock in and legal regulation. In today's world, more and more organizations are adopting the cloud services mainly because of the reliability and affordability provided by them. However, there are several drawbacks faced by the cloud users and cloud service providers. Apart from the security perspective, the cloud users are facing challenges in control and visibility, lack of standard service interfaces, difficulty in deploying applications across multiple clouds and vendor lock-in. Also, cloud service providers are facing challenges in degradation of the quality of service provided because of the distance between cloud data center and the end user and unexpected interruption of services etc. The above problems can be reduced to a greater extent or mitigated by adopting Multi Cross Cloud Infrastructure. This benefits the cloud users to receive the best quality services to increase their productivity. Hence, the main aim of this research is to build a common platform to manage the cross-cloud environment particularly Microsoft AZURE cloud and Amazon Web Services (AWS) with multiple features such as policies based container migration among the clouds and finding the best virtual machines (VM) across the clouds to deploy new containers. Cross-cloud management platform can be implemented within an organization or Enterprise and is used by the 3rd level support team such as Infrastructure team to provide multiple services (E.g. - Delivering application containers, Migration of containers on request) to end users based on some service level agreements (SLA) with more control and visibility.