Faculty of Computing
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Publication Embargo Image Processing and IoT-based Fish Diseases Identification and Fish Tank Monitoring System(IEEE, 2022-12-09) Ranaweera, I.U.; Weerakkody, G.K; Balasooriya, B.M.Eranda Kasun; Swarnakantha, N.H.P.Ravi SupunyaEvery person has their way of relaxing and having fun. The most well-liked approach to do it is to own a pet. When most individuals work from home and anxiety levels are high, people have certain restrictions on going outdoors and engaging in activities due to the existing COVID scenario. Consequently, we developed a product called AquaScanner. The problems that come with the aquarium environment can all be handled by our product. Our product primarily consists of an application that can regulate and monitor aquarium tanks by regulating feeding routines, fish disease detection, and water quality monitoring. The AquaScanner focuses on recognizing two significant illnesses, Fin Rot and Fungi bacteria, under the heading of disease identification. Additionally, the product will recommend treatments for the illness and provide two distinct methods for feeding the fish manually and automatically through the application. The AquaScanner can regulate feeding operations. Also, AquaScanner can independently monitor all key water parameters as part of the water quality measurement system. A user-friendly interface connects these three key elements. Owners of aquariums may manage and keep an eye on their beloved aquariums from anywhere in the world.Publication Embargo Banana Disease Identification Using Machine Learning Based Technologies and Weather-Based Dispersion Analysis(IEEE, 2022-12-09) Kothalawala, M.U.; Gaveshith, M.G. K; Tharaka, A.H.D.H.; Punchihewa, I.A; Sriyaratna, DBanana is the fourth most important food crop in the world as well as the most important and popular fruit crop in Sri Lanka. Banana leaf diseases are becoming one of the most important factors affecting agricultural products. As a result of these diseases, the quantity and quality of agricultural produce have drastically decreased. Hence, early detection and classification of banana leaf diseases are becoming more important than ever. But the ancient method of disease identification, visual observation is no longer helpful in this matter as it requires significant knowledge and experience related to banana diseases and symptoms which present farmers severely lacks. Therefore, using ICT-based approaches such as autoML, deep learning, natural language processing and APIs are very important towards the efficiency of the disease identification process and the accuracy of the diagnosis as well as keeping farmers synced with the information related to their plantation such as recent threats and nearby threats.Publication Embargo Deep Learning-Based Surveillance System for Coconut Disease and Pest Infestation Identification(IEEE, 2021-12-07) Vidhanaarachchi, S. P.; Akalanka, P. K. G. C.; Gunasekara, R. P. T. I.; Rajapaksha, H. M. U.D; Aratchige, N. S.; Lunugalage, D; Wijekoon, J. LThe coconut industry which contributes 0.8% to the national GDP is severely affected by diseases and pests. Weligama coconut leaf wilt disease and coconut caterpillar infestation are the most devastating; hence early detection is essential to facilitate control measures. Management strategies must reach approximately 1.1 million coconut growers with a wide range of demographics. This paper reports a smart solution that assists the stakeholders by detecting and classifying the disease, infestation, and deficiency for the sustainable development of the coconut industry. It leads to the early detections and makes stakeholders aware about the dispersions to take necessary control measures to save the coconut lands from the devastation. The results obtained from the proposed method for the identifications of disease, pest, deficiency, and degree of diseased conditions are in the range of 88% - 97% based on the performance evaluations.Publication Embargo Identification and Mitigation Tool for Sql Injection Attacks(SQLIA)(IEEE, 2020-11-26) Rankothge, W. H; Randeniya, M; Samaranayaka, VStructured Query Language Injection Attack (SQLIA) is a very frequent web security vulnerability. The attacker adds a malicious Structured Query Language (SQL) code to the input field of a web form, so that he can gain access to data or make unauthorized changes to data. A successful malicious SQL injection cause serious consequence to the victimized organization such as financial loss, reputation loss, compliance, and regulatory breaches. There have been several research works on detection and prevention of SQL injection attacks. However, still there is an absence of an advanced single tools for both identification and mitigation of SQL injection attacks. We have proposed an approach to identify and mitigate SQL injection attacks using a single tool and it allows software testers to identify the SQL injection vulnerabilities of their web applications during the testing stages. The proposed approach is based on parameterized queries and user input validation. Our results show that the tool provides 100% accurate and efficient results on identification and mitigation of SQL vulnerabilities.Publication Embargo Identification and Mitigation Tool For Cross-Site Request Forgery (CSRF)(IEEE, 2020-12-01) Rankothge, W. H; Randeniya, S M. NMost organizations use web applications for sharing resources and communication via the internet and information security is one of the biggest concerns in most organizations. Web applications are becoming vulnerable to threats and malicious attacks every day, which lead to violation of confidentiality, integrity, and availability of information assets.We have proposed and implemented a new automated tool for the identification and mitigation of Cross-Site Request Forgery (CSRF) vulnerability. A secret token pattern based has been used in the automated tool, which applies effective security mechanism on PHP based web applications, without damaging the content and its functionalities, where the authenticated users can perform web activities securely.Publication Embargo Digital Tool for Prevention, Identification and Emergency Handling of Heart Attacks(IEEE, 2021-09-30) Mihiranga, A; Shane, D; Indeewari, B; Udana, A; Nawinna, D. P; Attanayaka, BHeart attack is one of the most frequent causes of death in adults. The majority of heart attacks lead to death before any treatment is given to patients. The conventional mode of healthcare is passive, whereby patients themselves call the healthcare services requesting assistance. Consequently, if they are unconscious when heart failure occurs, they normally fail to call the service. To prevent patients from further harm and save their lives, the early and on-time diagnosis important. This paper presents an innovative web and mobile solution designed using it as Internet of Things (IoT) technology and Machine learning concepts to effectively manage heart patients, the ‘CARDIIAC’ system. This system can predict potential heart attack based on a set of identified risk factors. The system also can identify an actual heart attack using the readings from a wearable IoT device and notify the patient. The system is also equipped with emergency event coordination functionalities. Therefore, ‘CARDIIAC’ provides a holistic care for heart patients by effectively monitoring and managing emergencies related to heart diseases. This would be a socially important system to reduce the number of heart patients who die due to the inability to get immediate treatment.Publication Embargo Identification of Medicinal Plants by Visual Characteristics of Leaves and Flowers(IEEE, 2019-12-18) Jayalath, A. D. A. D. S; Amarawanshaline, T. G. A. G. D; Nawinna, D. P; Nadeeshan, P. V. D; Jayasuriya, H. PIn Ayurveda medicine, correct identification of medicinal plants is of great importance. Plants are identified by human experts using their visual features and aroma. Incorrect identification of medicinal plants may lead to adverse results. Plant identification can be automated using visual morphological characteristics such as the shape, color, and texture of the leaves and flowers. This paper presents how rare medicinal plants were identified with high accuracy by applying image processing and machine learning capabilities. For this study, a database was created from scanned images of leaves and flowers of rare medicinal plants used in Sri Lankan Ayurveda medicine. Both the front and back sides of leaves and flowers were captured. The leaves are classified based on the unique feature combination. Identification rates up to 98% have been obtained when tested over 10 plants.Publication Embargo E-Secure: An Automated Behavior Based Malware Detection System for Corporate E-Mail Traffic(SAI 2018: Intelligent Computing, 2018-11-02) Thebeyanthan, K.; Achsuthan, M.; Ashok, S.; Vaikunthan, P.; Senaratne, A. N; Abeywardena, K. YOver the year’s cyber-attacks have become much more sophisticated, bringing new challenges to the cyber world. Cyber security is becoming one of the major concerns in the area of network security these days. In recent times attackers have found new ways to bypass the malware detection technologies that are used in the security domain. The static analysis of malware is no longer considered an effective method compared to the propagating rate of malware bypassing static analysis. The first step that has to be followed to protect a system is to have a deep knowledge about existing malware, different types of malware, a method to detect the malware, and the method to bypass the effects caused by the malware. E-Secure is a behavior based malware detection system for corporate e-mail traffic. This paper proposes a malware security system as a solution to detect the malicious file that is passed through the e-mail of corporate network, and externally a file uploaded separately through a website for analysis. Since signature-based methods cannot identify the sophisticated malware effectively, the dynamic analysis is used to identify the malware. The Cuckoo Sandbox plays an important role in analyzing the behavior of malware but has no feature to extract the behavior, cluster it and produce results graphically in a way that is easier to understand. An application programming interface is used to extract the behavior of the malware and to train the machines automatically by feeding the extracted behavior. K-Means algorithm is used to cluster the malware based on the same behaviors. An application programming Interface is developed to illustrate the clusters graphically. After the completion of the training process, when a new malware arrives again an application programming interface is developed to identify the type of the malware. Risk analysis is used to state the criticality of a malware. The output of the whole process can be viewed through the E-Secure web interface which helps even a junior network security administrator to understand the detected malware and how critical the malware is.Publication Embargo Code Vulnerability Identification and Code Improvement using Advanced Machine Learning(IEEE, 2019-12-05) Ruggahakotuwa, L; Rupasinghe, L; Abeygunawardhana, P. K. WCyber-attacks are fairly mundane. The misconfigurations of the source code can result in security vulnerabilities that potentially encourage the attackers to exploit them and compromise the system. This paper aims to discover various mechanisms of automating the detection and correction of vulnerabilities in source code. Usage of static and dynamic analysis, various machine learning, deep learning, and neural network techniques will enhance the automation of detecting and correcting processes. This paper systematically presents the various methods and research efforts of detecting vulnerabilities in the source code, starting with what is a software vulnerability and what kind of exploitation, existing vulnerability detection methods, correction methods and efforts of best researches in the world relevant to the research area. A plugin will be developed which is capable of intelligently and efficiently detecting the vulnerable source code segment and correcting the source code accurately in the development stage.Publication Embargo Code Vulnerability Identification and Code Improvement using Advanced Machine Learning(IEEE, 2019-12-05) Ruggahakotuwa, L; Rupasinghe, L; Abeygunawardhana, P. K. WCyber-attacks are fairly mundane. The misconfigurations of the source code can result in security vulnerabilities that potentially encourage the attackers to exploit them and compromise the system. This paper aims to discover various mechanisms of automating the detection and correction of vulnerabilities in source code. Usage of static and dynamic analysis, various machine learning, deep learning, and neural network techniques will enhance the automation of detecting and correcting processes. This paper systematically presents the various methods and research efforts of detecting vulnerabilities in the source code, starting with what is a software vulnerability and what kind of exploitation, existing vulnerability detection methods, correction methods and efforts of best researches in the world relevant to the research area. A plugin will be developed which is capable of intelligently and efficiently detecting the vulnerable source code segment and correcting the source code accurately in the development stage.