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      Significant Permission Identification for Machine-Learning-Based Android Malware Detection

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          Anomaly detection: A survey

          Anomaly detection is an important problem that has been researched within diverse research areas and application domains. Many anomaly detection techniques have been specifically developed for certain application domains, while others are more generic. This survey tries to provide a structured and comprehensive overview of the research on anomaly detection. We have grouped existing techniques into different categories based on the underlying approach adopted by each technique. For each category we have identified key assumptions, which are used by the techniques to differentiate between normal and anomalous behavior. When applying a given technique to a particular domain, these assumptions can be used as guidelines to assess the effectiveness of the technique in that domain. For each category, we provide a basic anomaly detection technique, and then show how the different existing techniques in that category are variants of the basic technique. This template provides an easier and more succinct understanding of the techniques belonging to each category. Further, for each category, we identify the advantages and disadvantages of the techniques in that category. We also provide a discussion on the computational complexity of the techniques since it is an important issue in real application domains. We hope that this survey will provide a better understanding of the different directions in which research has been done on this topic, and how techniques developed in one area can be applied in domains for which they were not intended to begin with.
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            Drebin: Effective and Explainable Detection of Android Malware in Your Pocket

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              TaintDroid

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                Author and article information

                Journal
                IEEE Transactions on Industrial Informatics
                IEEE Trans. Ind. Inf.
                Institute of Electrical and Electronics Engineers (IEEE)
                1551-3203
                1941-0050
                July 2018
                July 2018
                : 14
                : 7
                : 3216-3225
                Article
                10.1109/TII.2017.2789219
                745adf9b-729b-4426-b6cc-7b689f0b7fad
                © 2018

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