Smart Computing and Systems Engineering (SCSE)

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    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.
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    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.