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Item Analysis and detection of potentially harmful Android applications using machine learning(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Kavneth, G.A.S.; Jayalal, S.With the rapid advancement of technology today, smartphones have become more and more powerful and attract a huge number of users with new features provided by mobile device operating systems such as Android and iOS. Android extended its lead by capturing 86% of the total market in 2017 (Gartner, 2017) and became the most popular mobile operating system. However, this huge demand and freedom has made the hackers and cybercriminals more curious to generate malicious apps towards the Android operating system. Thus, research on effective and efficient mobile threat analysis becomes an emerging and important topic in cybersecurity research area. This paper proposes a static-dynamic hybrid malware detecting scheme for Android applications. While the static analysis could be fast, and less resource consuming technique and dynamic analysis can be used for high complexity and deep analysis. The suggested methods can automatically deliver an unknown application for both static and dynamic analysis and determine whether Android application is a malware or not. The experimental results show that the suggested scheme is effective as its detection accuracy can achieve to 93% ∼ 100%. The findings have been more accurate in identifying Android malwares rather than separating those two static and dynamic behaviors. Furthermore, this research compares the machine learning algorithms for static and dynamic analysis of the Android malwares and compare the accuracy by the data used to train the machine learning models. It reveals Deep Neural Networks and SVM can be used for and higher accuracy. In addition, era of the training and testing dataset highly effect the accuracy of the results regarding Android applications.Item Remote access for personal cloud devices(International Research Conference on Smart Computing and Systems Engineering - SCSE 2018, 2018) Wickramarachchi, A.; Mallawaarachchi, V.With the developments in cloud computing, there have been raising concerns towards the privacy of content stored in the cloud. People tend to steer towards personal cloud devices as they improve the locality of data by holding such cloud devices in the vicinity, in order to ensure data privacy. These devices are mostly found in the form of Network-attached Storages (NAS) that are accessible within the local network. Although there are gains in security and privacy, numerous drawbacks exist among personal cloud devices when compared to cloud-based solutions. One major drawback is the remote access to content and sharing of content with remote users. A popular method of sharing media to remote users is the generation of a link, which is globally accessible through a centralized server. The proposed solution implements a link sharing mechanism, an online cross platform file browser and a remote access control mechanism which uses end-to-end encrypted tunnels to communicate in a peer-to-peer manner. This solution makes use of WebRTC which utilizes Datagram Transport Layer Security (DTLS) to ensure encrypted delivery of data. The presented text contains the experimental setup, user interfaces and results obtained after evaluating the performance of the proposed system.Item Machine learning based model for Android malware analysis and detection.(International Research Symposium on Pure and Applied Sciences, 2017 Faculty of Science, University of Kelaniya, Sri Lanka., 2017) Kavneth, G. A. S.; Jayalal, S.Rapid advancement of technology has enabled smartphones to become extremely powerful. They are capable of attracting a considerable amount of users with new features provided by mobile device operating systems such as Android and iOS. Android extended its lead by capturing 86 percent of the total market in 2017, and became the most popular mobile operating system. The Android operating system, which is found on a wide range of devices is owned by Google and powered by the Linux kernel. It is an open source operating system that enables mobile application developers to access unlocked hardware and develop new apps as they wish. However, this huge demand and freedom has made the hackers and cybercriminals more curious to generate malicious apps towards the Android operating system. They constantly target the security vulnerabilities in the operating system to gain access within the system. Even though, Google provides a primary set of security services, there are possibilities for potentially harmful applications in the Google Play store and other third party application stores. Thus, research on effective and efficient mobile threat analysis becomes an emerging and important topic in cybersecurity research area. Many researchers proposed various security analysis and evaluation strategies such as static analysis and dynamic analysis. In this research, we propose a hybrid approach, which aggregates the static and dynamic analysis for detecting security threats and attacks by Android malware application. This approach has two phases. First phase is the static analysis for applications, which will analyze the mobile application without execution. This focuses on extracting app APK file and examining permission requests made by Android apps that have declared in AndroidManifest.xml, as a means for detecting malwares. Because, in most of cases extra permissions granted by applications will allow the attacker to exploit the device. As the next phase, we perform dynamic analysis for mobile application. This phase focuses on runtime data obtained from the applications such as CPU, scheduler information from every running application, network calls, sensor data and so forth. For both phases, we have used supervised, machine learning based algorithms to train models and recognize malwares. In the first phase, potentially harmful applications can be identified as well as in the proposed hybrid mechanism, which is a combination of both phases. Data that was collected by several cybersecurity research centers were used for the evaluation of the proposed hybrid approach and both real-life malware and benign app data demonstrated a good detection performance with high scalability. The initial findings have been more accurate in identifying Android malwares rather than separating those two static and dynamic behaviors.Item Ancient Reservoirs of Sri Lanka: A Modern Biological Resource for Assuring Food Security in Rural Communities(University of Kelaniya, 2005) Kularatne, M.G.; Amarasingha, U.S.Reservoir construction in Sri Lanka dates back to times even before the period of written history. In Sri Lanka (64,652 km2), there are over 200 large (750 – 7,793 ha) and medium-sized (250 – 750 ha) reservoirs with a cumulative extent of over 130,000 ha, which support capture fisheries. In addition, there are over 15,000 small (<50 ha) village reservoirs with a total extent of about 39,000 ha. The reservoir density in Sri Lanka (about 2.6 ha for every km2 of island) is one of the highest, if not the highest in the world. Almost entire reservoir resource in Sri Lanka, with the exception of recently constructed hydroelectric reservoirs, supports agricultural food production in the country. As the extent of perennial reservoirs in each district is directly related to per capita freshwater fish consumption, in addition to agricultural production, major perennial reservoirs of Sri Lanka support animal protein production in the form of fish production. This is of particular importance because marine fish consumption is much low in inland districts possibly due to the availability of good quality freshwater fish locally. Also, there is a significant potential for the development of culture-based fisheries in small, village reservoirs of the country. An average fish yield of about 450 kg ha-1 can be achieved during a single culture cycle within a year from the culture-based fisheries in these village reservoirs. However, in order to achieve success of this strategy, a strong extension mechanism is needed to obtain active community participation. As inland fishery is a source of relatively cheap animal protein for rural communities, future prospects of this sector for food security need to be properly understood to give a high priority for inland fisheries research and development in national development plans.