MaMaDroid2.0 -- The Holes of Control Flow Graphs
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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.
Key facts. Cited by 1 paper 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.
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19 of 102 references.
References · 19
- 2014Explaining and Harnessing Adversarial Examplescited by 7,364
- 2016Towards Evaluating the Robustness of Neural Networkscited by 2,998
- 2017Evasion Attacks against Machine Learning at Test Timecited by 689
- 2018Generating Natural Language Adversarial Examplescited by 424
- 2018TextBugger: Generating Adversarial Text Against Real-world Applicationscited by 381
- 2017Adversarial Examples: Attacks and Defenses for Deep Learningcited by 351
- 2018TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Timecited by 108
- 2017Generating Adversarial Malware Examples for Black-Box Attacks Based on GANcited by 75
- 2017Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detectioncited by 52
- 2020Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detectioncited by 33
- 2016MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Modelscited by 30
- 2018Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detectioncited by 19
7 more
- 2017Improving Robustness of ML Classifiers against Realizable Evasion Attacks Using Conserved Featurescited by 18
- 2020Towards Feature Space Adversarial Attackcited by 11
- 2017Evading Classifiers by Morphing in the Darkcited by 9
- 2019Unsupervised Adversarial Attacks on Deep Feature-based Retrieval with GANcited by 4
- 2018Android Malware Detection based on Factorization Machinecited by 2
- 2019DaDiDroid: An Obfuscation Resilient Tool for Detecting Android Malware via Weighted Directed Call Graph Modellingcited by 2
- 2022Evaluation of Neural Networks Defenses and Attacks using NDCG and Reciprocal Rank Metricscited by 2
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