• AFSANA
• Dr. SAYED ABDULHAYAN

With the widely uses of smartphones, the number of malware has been increasing exponentially. Among android devices, smart devices are the most targeted devices by malware because of their growing popularity. In this paper proposes a novel framework for Android malware detection. The framework utilities various kinds of features to reflect the properties of Android applications from various aspects, and the features are refined using the similarity-based feature extraction or existence based method for effective feature indication on malware detection. Besides, a multimodal learning method is proposed to be used as a malware detection model.

Android malware, Machine learning , intrusion detection , malware detection, neural network.

"ANDROID MALWARE DETECTION USING MULTIMODAL DEEP LEARNING METHOD ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.194-201, June-2019, Available :http://www.jetir.org/papers/JETIR1906463.pdf

"ANDROID MALWARE DETECTION USING MULTIMODAL DEEP LEARNING METHOD ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp194-201, June-2019, Available at : http://www.jetir.org/papers/JETIR1906463.pdf