Securing Android Apps: Deep Learning-Powered Threat Detection from Tweet Analysis

Authors- Ch. Veera Gayathri, Addala Bhargavi, Korada Jayanthkumar, Pinnam John Suresh, Oleti Srinivas Reddy, Telu Dheera Guru Chakravarthi

Abstract-Smartphones have become an essential part of modern life, making security and privacy critical concerns, particularly for the Android operating system, which dominates the smartphone market. However, its widespread use also makes it a prime target for malware attacks, posing significant risks, especially to Internet of Things (IoT) devices that rely on Android applications. To mitigate these threats, this paper proposes a multi-layered malware detection approach that integrates deep learning techniques with real-time threat intelligence from Twitter. By updating a malware hash database every 48 hours using Twitter data, our system remains up to date with emerging threats. Additionally, we employ a Recurrent Neural Network (RNN) with Long Short-Term Memory (LSTM) units to analyse application permissions, achieving a 94% detection accuracy. This comprehensive approach strengthens Android security by delivering a proactive and adaptive malware detection system, effectively countering evolving cyber threats.

DOI: /10.61463/ijset.vol.13.issue2.224