Android Security using NLP Techniques: A Review
arXiv:2107.03072
Abstract
Android is among the most targeted platform by attackers. While attackers are improving their techniques, traditional solutions based on static and dynamic analysis have been also evolving. In addition to the application code, Android applications have some metadata that could be useful for security analysis of applications. Unlike traditional application distribution mechanisms, Android applications are distributed centrally in mobile markets. Therefore, beside application packages, such markets contain app information provided by app developers and app users. The availability of such useful textual data together with the advancement in Natural Language Processing (NLP) that is used to process and understand textual data has encouraged researchers to investigate the use of NLP techniques in Android security. Especially, security solutions based on NLP have accelerated in the last 5 years and proven to be useful. This study reviews these proposals and aim to explore possible research directions for future studies by presenting state-of-the-art in this domain. We mainly focus on NLP-based solutions under four categories: description-to-behaviour fidelity, description generation, privacy and malware detection.
References in corpus (7)
- Distributed Representations of Words and Phrases and their Compositionality
- Sequence to Sequence Learning with Neural Networks
- Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation
- A BERT Baseline for the Natural Questions
- Assigning personality/identity to a chatting machine for coherent conversation generation
- Towards a Robust Deep Neural Network in Texts: A Survey
- Combating Adversarial Misspellings with Robust Word Recognition