# DeepCatra: Learning Flow- and Graph-based Behaviors for Android Malware Detection

arXiv: https://arxiv.org/abs/2201.12876 · HTML: https://mlsanity.com/paper/2201.12876/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 30 Jan 2022.

## Built on by (most cited first, 5 of 5)

- [A Survey on Malware Detection with Graph Representation Learning](https://mlsanity.com/paper/2303.16004/) (2023)
- [Use of Graph Neural Networks in Aiding Defensive Cyber Operations](https://mlsanity.com/paper/2401.05680/) (2024)
- [Deep Image: A precious image based deep learning method for online malware detection in IoT Environment](https://mlsanity.com/paper/2204.01690/) (2022)
- [Better Call Graphs: A New Dataset of Function Call Graphs for Malware Classification](https://mlsanity.com/paper/2512.20872/) (2025)
- [A Quantitative Analysis of Graph Representation Strategies for Cyber Attack Detection](https://mlsanity.com/paper/2610.04019/) (2026)

## Abstract (arXiv)

As Android malware is growing and evolving, deep learning has been introduced into malware detection, resulting in great effectiveness. Recent work is considering hybrid models and multi-view learning. However, they use only simple features, limiting the accuracy of these approaches in practice. In this paper, we propose DeepCatra, a multi-view learning approach for Android malware detection, whose model consists of a bidirectional LSTM (BiLSTM) and a graph neural network (GNN) as subnets. The two subnets rely on features extracted from statically computed call traces leading to critical APIs derived from public vulnerabilities. For each Android app, DeepCatra first constructs its call graph and computes call traces reaching critical APIs. Then, temporal opcode features used by the BiLSTM subnet are extracted from the call traces, while flow graph features used by the GNN subnet are constructed from all the call traces and inter-component communications. We evaluate the effectiveness of DeepCatra by comparing it with several state-of-the-art detection approaches. Experimental results on over 18,000 real-world apps and prevalent malware show that DeepCatra achieves considerable improvement, e.g., 2.7% to 14.6% on F1-measure, which demonstrates the feasibility of DeepCatra in practice.