Browsing by Author "Wijekoon, J."

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Embargo
An Automated Solution For Securing Confidential Documents in a BYOD Environment
(2021 3rd International Conference on Advancements in Computing (ICAC), SLIIT, 2021-12-09) Abisheka, P.A.C.; Azra, M.A.F.; Poobalan, A.V.; Wijekoon, J.; Yapa, K.; Murthaja, M.
BYOD or Bring Your Own Device is a set of policies that allow employees of an organization to use their own devices for official work purposes. BYOD is an immensely popular concept in the present day due to the many advantages it provides. However, the implementation of BYOD policies entail diverse problems and as a result, the confidentiality of documents can be breached. Furthermore, employees without security awareness and training are highly vulnerable to endpoint attacks, network attacks, and zero-day attacks that lead to a breach of confidentiality, integrity, and availability (CIA). In this context, this paper proposes a comprehensive solution; ‘BYODENCE’, for the detection and prevention of unauthorized access to organizational documents. BYODENCE is an efficient BYOD solution which can produce competitive results in terms of accuracy and speed.
Embargo
A Geophone Based Surveillance System Using Neural Networks and IoT
(2020 2nd International Conference on Advancements in Computing (ICAC), SLIIT, 2020-12-10) Supun Hettigoda, Chamath Jayaminda; Amarathunga, U.; Wijesundara, M.; Wijekoon, J.; Thaha, S.
Securing our assets and properties from intruders and thieves has become increasingly challenging as intruders become technology aware. The most common approach to monitor physical assets is CCTV. However, this approach has a number of technical limitations in addition to the cost. The CCTV camera location is visible to the intruder and intruder can also identify possible blind spots in the CCTV coverage area. In this paper, we introduce a novel method to secure physical assets using Geophones, Neural Networks, and IoT Platforms. This can either be used stand alone or to complement existing CCTV systems. In this approach, the system monitors vibrations on ground to detect intruders. We have achieved up to 93.90% overall accuracy for person identification. The system is invisible to intruders and covers a large area with a smaller number of nodes, thereby reducing the cost of ownership.