Mining mobile apps for anomalies
There is no doubt that mobile applications play a huge role in our lives today, just in 2017 alone, there have been 178.1 billion mobile application downloads. [1] With our reliance on mobile applications, security of these applications is more important than ever, especially how applications handle...
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sg-ntu-dr.10356-761902023-03-03T20:32:41Z Mining mobile apps for anomalies Seow, Wei Yang Shar Lwin Khin School of Computer Science and Engineering DRNTU::Engineering::Computer science and engineering::Computer systems organization::Special-purpose and application-based systems There is no doubt that mobile applications play a huge role in our lives today, just in 2017 alone, there have been 178.1 billion mobile application downloads. [1] With our reliance on mobile applications, security of these applications is more important than ever, especially how applications handle your personal data. This project investigated the effectiveness of an approach consisting of a combination of topic modelling and static analysis to detect anomalous android mobile applications. We analysed applications from a repository known as fdroid and using Mallet, we split the applications into categories where we performed static analysis using FlowDroid on the applications. The source sink pairs of applications within each category were recorded down and a dataset was built. It was used to compare with unknown applications that belong in that category, via the source sink pairs to detect anomalies. Anomalous applications such as a repackaged application that contained malware was able to be detected. It was concluded that, the approach done in the project is viable, however there are still several aspects of the project that could be improved on to give better results. Bachelor of Engineering (Computer Science) 2018-11-27T07:12:35Z 2018-11-27T07:12:35Z 2018 Final Year Project (FYP) http://hdl.handle.net/10356/76190 en Nanyang Technological University 55 p. application/pdf |
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DRNTU::Engineering::Computer science and engineering::Computer systems organization::Special-purpose and application-based systems Seow, Wei Yang Mining mobile apps for anomalies |
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There is no doubt that mobile applications play a huge role in our lives today, just in 2017 alone, there have been 178.1 billion mobile application downloads. [1] With our reliance on mobile applications, security of these applications is more important than ever, especially how applications handle your personal data. This project investigated the effectiveness of an approach consisting of a combination of topic modelling and static analysis to detect anomalous android mobile applications. We analysed applications from a repository known as fdroid and using Mallet, we split the applications into categories where we performed static analysis using FlowDroid on the applications. The source sink pairs of applications within each category were recorded down and a dataset was built. It was used to compare with unknown applications that belong in that category, via the source sink pairs to detect anomalies. Anomalous applications such as a repackaged application that contained malware was able to be detected. It was concluded that, the approach done in the project is viable, however there are still several aspects of the project that could be improved on to give better results. |
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Shar Lwin Khin |
author_facet |
Shar Lwin Khin Seow, Wei Yang |
format |
Final Year Project |
author |
Seow, Wei Yang |
author_sort |
Seow, Wei Yang |
title |
Mining mobile apps for anomalies |
title_short |
Mining mobile apps for anomalies |
title_full |
Mining mobile apps for anomalies |
title_fullStr |
Mining mobile apps for anomalies |
title_full_unstemmed |
Mining mobile apps for anomalies |
title_sort |
mining mobile apps for anomalies |
publishDate |
2018 |
url |
http://hdl.handle.net/10356/76190 |
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1759853964894928896 |