# Collective Intelligence: Decentralized Learning for Android Malware Detection in IoT with Blockchain

arXiv: https://arxiv.org/abs/2102.13376 · HTML: https://mlsanity.com/paper/2102.13376/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Builds on 8 of its 83 references that are in the map.
- First posted on arXiv 26 Feb 2021.

## Builds on (most cited first, 8 of 8)

- [Blockchain-Federated-Learning and Deep Learning Models for COVID-19 detection using CT Imaging](https://mlsanity.com/paper/2007.06537/) (2020)
- [Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection](https://mlsanity.com/paper/1704.08996/) (2017)
- [DL-Droid: Deep learning based android malware detection using real devices](https://mlsanity.com/paper/1911.10113/) (2019)
- [A Performance-Sensitive Malware Detection System Using Deep Learning on Mobile Devices](https://mlsanity.com/paper/2005.04970/) (2020)
- [Similarity-based Android Malware Detection Using Hamming Distance of Static Binary Features](https://mlsanity.com/paper/1908.05759/) (2019)
- [Robust Gait Recognition by Integrating Inertial and RGBD Sensors](https://mlsanity.com/paper/1610.09816/) (2016)
- [Monet: A User-oriented Behavior-based Malware Variants Detection System for Android](https://mlsanity.com/paper/1612.03312/) (2016)
- [Android Malware Detection Using Parallel Machine Learning Classifiers](https://mlsanity.com/paper/1607.08186/) (2016)

## Abstract (arXiv)

The widespread significance of Android IoT devices is due to its flexibility and hardware support features which revolutionized the digital world by introducing exciting applications almost in all walks of daily life, such as healthcare, smart cities, smart environments, safety, remote sensing, and many more. Such versatile applicability gives incentive for more malware attacks. In this paper, we propose a framework which continuously aggregates multiple user trained models on non-overlapping data into single model. Specifically for malware detection task, (i) we propose a novel user (local) neural network (LNN) which trains on local distribution and (ii) then to assure the model authenticity and quality, we propose a novel smart contract which enable aggregation process over blokchain platform. The LNN model analyzes various static and dynamic features of both malware and benign whereas the smart contract verifies the malicious applications both for uploading and downloading processes in the network using stored aggregated features of local models. In this way, the proposed model not only improves malware detection accuracy using decentralized model network but also model efficacy with blockchain. We evaluate our approach with three state-of-the-art models and performed deep analyses of extracted features of the relative model.