# MaMaDroid2.0 -- The Holes of Control Flow Graphs

arXiv: https://arxiv.org/abs/2202.13922 · HTML: https://mlsanity.com/paper/2202.13922/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 19 of its 102 references that are in the map.
- First posted on arXiv 28 Feb 2022.

## Builds on (most cited first, 19 of 19)

- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Towards Evaluating the Robustness of Neural Networks](https://mlsanity.com/paper/1608.04644/) (2016)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Generating Natural Language Adversarial Examples](https://mlsanity.com/paper/1804.07998/) (2018)
- [TextBugger: Generating Adversarial Text Against Real-world Applications](https://mlsanity.com/paper/1812.05271/) (2018)
- [Adversarial Examples: Attacks and Defenses for Deep Learning](https://mlsanity.com/paper/1712.07107/) (2017)
- [TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time](https://mlsanity.com/paper/1807.07838/) (2018)
- [Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN](https://mlsanity.com/paper/1702.05983/) (2017)
- [Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection](https://mlsanity.com/paper/1704.08996/) (2017)
- [Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection](https://mlsanity.com/paper/2006.16545/) (2020)
- [MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models](https://mlsanity.com/paper/1612.04433/) (2016)
- [Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection](https://mlsanity.com/paper/1808.04218/) (2018)
- [Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Features](https://mlsanity.com/paper/1708.08327/) (2017)
- [Towards Feature Space Adversarial Attack](https://mlsanity.com/paper/2004.12385/) (2020)
- [Evading Classifiers by Morphing in the Dark](https://mlsanity.com/paper/1705.07535/) (2017)
- [Unsupervised Adversarial Attacks on Deep Feature-based Retrieval with GAN](https://mlsanity.com/paper/1907.05793/) (2019)
- [Android Malware Detection based on Factorization Machine](https://mlsanity.com/paper/1805.11843/) (2018)
- [DaDiDroid: An Obfuscation Resilient Tool for Detecting Android Malware via Weighted Directed Call Graph Modelling](https://mlsanity.com/paper/1905.09136/) (2019)
- [Evaluation of Neural Networks Defenses and Attacks using NDCG and Reciprocal Rank Metrics](https://mlsanity.com/paper/2201.05071/) (2022)

## Built on by (most cited first, 1 of 1)

- [Adversarial Patterns: Building Robust Android Malware Classifiers](https://mlsanity.com/paper/2203.02121/) (2022)

## Abstract (arXiv)

Android malware is a continuously expanding threat to billions of mobile users around the globe. Detection systems are updated constantly to address these threats. However, a backlash takes the form of evasion attacks, in which an adversary changes malicious samples such that those samples will be misclassified as benign. This paper fully inspects a well-known Android malware detection system, MaMaDroid, which analyzes the control flow graph of the application. Changes to the portion of benign samples in the train set and models are considered to see their effect on the classifier. The changes in the ratio between benign and malicious samples have a clear effect on each one of the models, resulting in a decrease of more than 40% in their detection rate. Moreover, adopted ML models are implemented as well, including 5-NN, Decision Tree, and Adaboost. Exploration of the six models reveals a typical behavior in different cases, of tree-based models and distance-based models. Moreover, three novel attacks that manipulate the CFG and their detection rates are described for each one of the targeted models. The attacks decrease the detection rate of most of the models to 0%, with regards to different ratios of benign to malicious apps. As a result, a new version of MaMaDroid is engineered. This model fuses the CFG of the app and static analysis of features of the app. This improved model is proved to be robust against evasion attacks targeting both CFG-based models and static analysis models, achieving a detection rate of more than 90% against each one of the attacks.