# Identification of Significant Permissions for Efficient Android Malware Detection

arXiv: https://arxiv.org/abs/2103.00643 · HTML: https://mlsanity.com/paper/2103.00643/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 30 references that are in the map.
- First posted on arXiv 28 Feb 2021.

## Builds on (most cited first, 3 of 3)

- [Comparison of Deep Learning and the Classical Machine Learning Algorithm for the Malware Detection](https://mlsanity.com/paper/1809.05889/) (2018)
- [An investigation of the classifiers to detect android malicious apps](https://mlsanity.com/paper/1802.08611/) (2018)
- [DeepIntent: ImplicitIntent based Android IDS with E2E Deep Learning architecture](https://mlsanity.com/paper/2010.08607/) (2020)

## Built on by (most cited first, 7 of 7)

- [Deep Reinforcement Learning for Cybersecurity Threat Detection and Protection: A Review](https://mlsanity.com/paper/2206.02733/) (2022)
- [ActDroid: An active learning framework for Android malware detection](https://mlsanity.com/paper/2401.16982/) (2024)
- [LSTM Hyper-Parameter Selection for Malware Detection: Interaction Effects and Hierarchical Selection Approach](https://mlsanity.com/paper/2109.11500/) (2021)
- [ADVERSARIALuscator: An Adversarial-DRL Based Obfuscator and Metamorphic Malware SwarmGenerator](https://mlsanity.com/paper/2109.11542/) (2021)
- [Reassessing feature-based Android malware detection in a contemporary context](https://mlsanity.com/paper/2301.12778/) (2023)
- [DRo: A data-scarce mechanism to revolutionize the performance of Deep Learning based Security Systems](https://mlsanity.com/paper/2109.05470/) (2021)
- [A Comprehensive Analysis of Evolving Permission Usage in Android Apps: Trends, Threats, and Ecosystem Insights](https://mlsanity.com/paper/2508.02008/) (2025)

## Abstract (arXiv)

Since Google unveiled Android OS for smartphones, malware are thriving with 3Vs, i.e. volume, velocity, and variety. A recent report indicates that one out of every five business/industry mobile application leaks sensitive personal data. Traditional signature/heuristic-based malware detection systems are unable to cope up with current malware challenges and thus threaten the Android ecosystem. Therefore recently researchers have started exploring machine learning and deep learning based malware detection systems. In this paper, we performed a comprehensive feature analysis to identify the significant Android permissions and propose an efficient Android malware detection system using machine learning and deep neural network. We constructed a set of 16 permissions (8% of the total set) derived from variance threshold, auto-encoders, and principal component analysis to build a malware detection engine that consumes less train and test time without significant compromise on the model accuracy. Our experimental results show that the Android malware detection model based on the random forest classifier is most balanced and achieves the highest area under curve score of 97.7%, which is better than the current state-of-art systems. We also observed that deep neural networks attain comparable accuracy to the baseline results but with a massive computational penalty.