• AFSANA
• Dr.SAYED ABDULHAYAN

Now a days the Android operating system experiences a vast popularity. This is a platform that has been established itself not only for the mobile communication world also in case of Internet of Things (IoT). For this kind of popularity, however, it comes at the amount of security, as it become the most targeted malicious applications. Therefore , there is a increasing need for portable , automatic, and sophisticated malware detection solutions. In this paper, Android malware detection and attribution of framework that defines on the classication sequences using the deep learning techniques. It will Start from raw sequence of the API call methods, it will automatically learns and extracts the benign and malicious patterns from actual samples to detect the malware in Android. The multimodal deep learning method can be serve as pervasive malware detection technique that is not only applied on servers, but also on mobile and even IoT devices.

"A MULTIMODAL DEEP LEARNING METHOD FOR ANDROID MALWARE DETECTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.6, Issue 6, page no.104-110, June-2019, Available :http://www.jetir.org/papers/JETIR1906452.pdf

"A MULTIMODAL DEEP LEARNING METHOD FOR ANDROID MALWARE DETECTION ", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.6, Issue 6, page no. pp104-110, June-2019, Available at : http://www.jetir.org/papers/JETIR1906452.pdf