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Therefore, to reduce classification time and improve accuracy, different feature reduction tools have been used in past literature. Community detection is one of the major tools in social network analysis but its implementation in the context of malware detection is quite rare. In this article, the authors introduce a community-based feature reduction technique for Android malware detection. The proposed method is evaluated on two datasets consisting of 3004 benign components and 1363 malware components. The proposed community-based feature reduction technique produces a classification accuracy of 98.20% and ROC value up to 0.989.<\/p>","DOI":"10.4018\/jgim.2018070105","type":"journal-article","created":{"date-parts":[[2018,4,16]],"date-time":"2018-04-16T13:41:24Z","timestamp":1523886084000},"page":"54-77","source":"Crossref","is-referenced-by-count":7,"title":["Community Based Feature Selection Method for Detection of Android Malware"],"prefix":"10.4018","volume":"26","author":[{"given":"Abhishek","family":"Bhattacharya","sequence":"first","affiliation":[{"name":"Institute of Engineering & Management, Kolkata, India"}]},{"given":"Radha Tamal","family":"Goswami","sequence":"additional","affiliation":[{"name":"Birla Institute of Technology, Mesra, India"}]}],"member":"2432","reference":[{"key":"JGIM.2018070105-0","doi-asserted-by":"crossref","unstructured":"Arp, D., Spreitzenbarth, M, Hubner, M., Gascon, H., Rieck, K. & Siemens, C. (2014). 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