Research Papers - Dept of Computer Systems Engineering

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Author: search.filters.author.Abeygunawardhana, P. K. WSubject: search.filters.subject.Machine Learning

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    Prevent Data Exfiltration on Smart Phones Using Audio Distortion and Machine Learning
    (IEEE, 2021-07-27) Moonamaldeniya, M; Priyashantha, V. R. S. C; Gunathilake, M. B. N. B; Ransinghe, Y. M. P. B; Ratnayake, A. L. S. D; Abeygunawardhana, P. K. W
    Attacks on mobile devices have gained a significant amount of attention lately. This is because more and more individuals are switching to smartphones from traditional non-smartphones. Therefore, attackers or cybercriminals are now getting on the bandwagon to have an opportunity at obtaining information stored on smartphones. In this paper, we present an Android mobile application that will aid to minimize data exfiltration from attacks, such as, Acoustic Side-Channel Attack, Clipboard Jacking, Permission Misuse and Malicious Apps. This paper will commence its inception with an introduction explaining the current issues in general and how attacks such as side-channel attacks and clipboard jacking paved the way for data exfiltration. We will also discuss a few already existing solutions that try to mitigate these problems. Moving on to the methodology we will emphasize how we came about the solution and what methods we followed to achieve the end goal of securing the smartphone. In the final section, we will discuss the outcomes of the project and conclude what needs to be done in the future to enhance this project so that this mobile application will continue to keep the user's data safe from the criminals' grasps.
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    Digital Preservation and Noise Reduction using Machine Learning
    (IEEE, 2021-12-09) Aravinda, K. P; Sandeepa, K. G. H; Sedara, V. V; Chamodya, A. K. Y. L; Dharmasena, T; Abeygunawardhana, P. K. W
    This paper proposes a digital preservation solution for Sinhala audios to conserve those as documents with noise reduction. The solution has implemented multiple noise reduction techniques as a pre-processing step to remove unwanted internal and external noises. A two-step, two-way noise reduction process is applied to produce clean audios based on Deep Convolutional Neural Network (DCNN) and adaptive filter-based techniques. This approach implements two separate noise reduction models for internal and external noises. After that, the speech recognition decoder recognizes the speech and converts it to a Unicode document by acoustic, language, and pronunciation models using extracted audio features from the denoised audio. Further, noise reduction models are decoupled from the preservation solution and exposed as a sub solution for multilingualism noise reduction, supporting English and Sinhala audios.
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    Intelligent disease detection system for greenhouse with a robotic monitoring system
    (IEEE, 2020-12-10) Fernando, S; Nethmi, R; Silva, A; Perera, A; De Silva, R; Abeygunawardhana, P. K. W
    Greenhouse farming plays a significant role in the agricultural industry because of its controlled climatic features. Recent examinations have stated that the mean creation of the yields under greenhouses is lessening due to disease events in the plants. These foods have become an imposing undertaking because these plants are being assaulted by different bacterial diseases, micro-organisms, and pests. The chemicals are applied to the plants intermittently without thinking about the necessity of each plant. Several problems have occurred in the greenhouse environment due to these causes. Therefore, there is a huge necessity for a system to detect diseases at an early stage. This research focused on designing a system to detect disease, which causes yellowish in greenhouse plants. Plant yellowing can be considered a significant problem of plants that grow under greenhouse-controlled environments. Through this research is focused on the most important and one of the most attention-grabbing crop tomato. There are specific diseases that cause yellowish the tomato plant, and they have been identified. The techniques utilized for early recognition of infection are image processing, machine learning, and deep learning.