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Specifically, an extensive study is conducted on various hyper-parameters to determine optimal configurations, and then a performance evaluation is carried out on those configurations to compare and maximize detection accuracy in our target networks. The results achieve a detection accuracy of approximately 95%, with an approximate F1 score of 93%. In addition, the evaluation is extended to include other machine learning frameworks, specifically comparing Microsoft Cognitive Toolkit (CNTK) and Theano with TensorFlow. The future needs are discussed in the realm of machine learning for mobile malware detection, including adversarial training, scalability, and the evaluation of additional data and features.<\/jats:p>","DOI":"10.4018\/ijsi.2019100101","type":"journal-article","created":{"date-parts":[[2019,8,27]],"date-time":"2019-08-27T14:56:32Z","timestamp":1566917792000},"page":"1-24","source":"Crossref","is-referenced-by-count":7,"title":["Towards Deep Learning-Based Approach for Detecting Android Malware"],"prefix":"10.4018","volume":"7","author":[{"given":"Jarrett","family":"Booz","sequence":"first","affiliation":[{"name":"Towson University, Towson, USA"}]},{"given":"Josh","family":"McGiff","sequence":"additional","affiliation":[{"name":"Towson University, Towson, USA"}]},{"given":"William G.","family":"Hatcher","sequence":"additional","affiliation":[{"name":"Towson University, Towson, USA"}]},{"given":"Wei","family":"Yu","sequence":"additional","affiliation":[{"name":"Towson University, Towson, USA"}]},{"given":"James","family":"Nguyen","sequence":"additional","affiliation":[{"name":"Towson University, Towson, USA"}]},{"given":"Chao","family":"Lu","sequence":"additional","affiliation":[{"name":"Towson University, Towson, USA"}]}],"member":"2432","reference":[{"key":"IJSI.2019100101-0","unstructured":"Keras: The python deep learning library. 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