Deep Learning-Powered Android Malware Detection and Prevention Leveraging Real-Time Twitter Threat Intelligence

Deep Learning-Powered Android Malware Detection and Prevention Leveraging Real-Time Twitter Threat Intelligence

Authors- Mrs.K.S.R. Manjusha, R.Sai Hrushi, K.Devi Naga Sri Aditya, U. Siva Tarun, K.Miriyam Nissy, D.Adhithi

Abstract-Smartphones have become an integral part of daily life, making security and privacy paramount, particularly for the Android operating system, which dominates the market. However, its widespread adoption also makes it a prime target for malware attacks, posing significant threats, especially to IoT devices dependent on Android applications. This paper presents a multilayer approach to Android malware detection, combining real-time data extraction from Twitter with deep learning techniques. Our method continuously updates a malware hash database every 48 hours using Twitter data, ensuring the latest threats are identified. Additionally, a deep learning model based on a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) is employed to analyse application permissions, achieving a 94% detection accuracy. This integrated approach offers a robust and adaptive solution to enhance Android OS security.

DOI: /10.61463/ijset.vol.13.issue2.256