# Android Security using NLP Techniques: A Review

arXiv: https://arxiv.org/abs/2107.03072 · HTML: https://mlsanity.com/paper/2107.03072/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 21 of its 153 references that are in the map.
- First posted on arXiv 7 Jul 2021.

## Builds on (most cited first, 21 of 21)

- [Attention Is All You Need](https://mlsanity.com/paper/1706.03762/) (2017)
- [BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding](https://mlsanity.com/paper/1810.04805/) (2018)
- [RoBERTa: A Robustly Optimized BERT Pretraining Approach](https://mlsanity.com/paper/1907.11692/) (2019)
- [Efficient Estimation of Word Representations in Vector Space](https://mlsanity.com/paper/1301.3781/) (2013)
- [Learning Phrase Representations using RNN Encoder-Decoder for Statistical Machine Translation](https://mlsanity.com/paper/1406.1078/) (2014)
- [Distributed Representations of Words and Phrases and their Compositionality](https://mlsanity.com/paper/1310.4546/) (2013)
- [Sequence to Sequence Learning with Neural Networks](https://mlsanity.com/paper/1409.3215/) (2014)
- [Deep contextualized word representations](https://mlsanity.com/paper/1802.05365/) (2018)
- [Enriching Word Vectors with Subword Information](https://mlsanity.com/paper/1607.04606/) (2016)
- [A Hierarchical Latent Variable Encoder-Decoder Model for Generating Dialogues](https://mlsanity.com/paper/1605.06069/) (2016)
- [Combating Adversarial Misspellings with Robust Word Recognition](https://mlsanity.com/paper/1905.11268/) (2019)
- [Breaking NLI Systems with Sentences that Require Simple Lexical Inferences](https://mlsanity.com/paper/1805.02266/) (2018)
- [A BERT Baseline for the Natural Questions](https://mlsanity.com/paper/1901.08634/) (2019)
- [On the use of BERT for Neural Machine Translation](https://mlsanity.com/paper/1909.12744/) (2019)
- [Towards Better UD Parsing: Deep Contextualized Word Embeddings, Ensemble, and Treebank Concatenation](https://mlsanity.com/paper/1807.03121/) (2018)
- [A Conditional Variational Framework for Dialog Generation](https://mlsanity.com/paper/1705.00316/) (2017)
- [Automated Test Input Generation for Android: Are We There Yet?](https://mlsanity.com/paper/1503.07217/) (2015)
- [Assigning personality/identity to a chatting machine for coherent conversation generation](https://mlsanity.com/paper/1706.02861/) (2017)
- [Latent Variable Dialogue Models and their Diversity](https://mlsanity.com/paper/1702.05962/) (2017)
- [SemEval-2019 Task 1: Cross-lingual Semantic Parsing with UCCA](https://mlsanity.com/paper/1903.02953/) (2019)
- [apk2vec: Semi-supervised multi-view representation learning for profiling Android applications](https://mlsanity.com/paper/1809.05693/) (2018)

## Abstract (arXiv)

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.