# New data poison attacks on machine learning classifiers for mobile exfiltration

arXiv: https://arxiv.org/abs/2210.11592 · HTML: https://mlsanity.com/paper/2210.11592/

## Key facts

- Cited by 2 papers in the map (data through 14 Oct 2026).
- Placed in Backdoor and data poisoning attacks, one of 12 directions in LLM safety and security.
- Builds on 56 of its 104 references that are in the map.
- First posted on arXiv 20 Oct 2022.

## Placed in

- LLM safety and security / [Backdoor and data poisoning attacks](https://mlsanity.com/safety/backdoor-and-data-poisoning-attacks/)

## Builds on (most cited first, 30 of 56)

- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Improved Training of Wasserstein GANs](https://mlsanity.com/paper/1704.00028/) (2017)
- [Conditional Generative Adversarial Nets](https://mlsanity.com/paper/1411.1784/) (2014)
- [BadNets: Identifying Vulnerabilities in the Machine Learning Model Supply Chain](https://mlsanity.com/paper/1708.06733/) (2017)
- [Targeted Backdoor Attacks on Deep Learning Systems Using Data Poisoning](https://mlsanity.com/paper/1712.05526/) (2017)
- [The Limitations of Deep Learning in Adversarial Settings](https://mlsanity.com/paper/1511.07528/) (2015)
- [Universal adversarial perturbations](https://mlsanity.com/paper/1610.08401/) (2016)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [One pixel attack for fooling deep neural networks](https://mlsanity.com/paper/1710.08864/) (2017)
- [Adversarial Examples Are Not Easily Detected: Bypassing Ten Detection Methods](https://mlsanity.com/paper/1705.07263/) (2017)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [Poison Frogs! Targeted Clean-Label Poisoning Attacks on Neural Networks](https://mlsanity.com/paper/1804.00792/) (2018)
- [ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models](https://mlsanity.com/paper/1806.01246/) (2018)
- [Wild Patterns: Ten Years After the Rise of Adversarial Machine Learning](https://mlsanity.com/paper/1712.03141/) (2017)
- [Hidden Trigger Backdoor Attacks](https://mlsanity.com/paper/1910.00033/) (2019)
- [Analyzing Federated Learning through an Adversarial Lens](https://mlsanity.com/paper/1811.12470/) (2018)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [The Hidden Vulnerability of Distributed Learning in Byzantium](https://mlsanity.com/paper/1802.07927/) (2018)
- [Certified Defenses for Data Poisoning Attacks](https://mlsanity.com/paper/1706.03691/) (2017)
- [Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality](https://mlsanity.com/paper/1801.02613/) (2018)
- [MagNet: a Two-Pronged Defense against Adversarial Examples](https://mlsanity.com/paper/1705.09064/) (2017)
- [Manipulating Machine Learning: Poisoning Attacks and Countermeasures for Regression Learning](https://mlsanity.com/paper/1804.00308/) (2018)
- [Towards Poisoning of Deep Learning Algorithms with Back-gradient Optimization](https://mlsanity.com/paper/1708.08689/) (2017)
- [PRADA: Protecting against DNN Model Stealing Attacks](https://mlsanity.com/paper/1805.02628/) (2018)
- [Stronger Data Poisoning Attacks Break Data Sanitization Defenses](https://mlsanity.com/paper/1811.00741/) (2018)
- [Sever: A Robust Meta-Algorithm for Stochastic Optimization](https://mlsanity.com/paper/1803.02815/) (2018)
- [Stealing Hyperparameters in Machine Learning](https://mlsanity.com/paper/1802.05351/) (2018)
- [Robust Anomaly Detection and Backdoor Attack Detection Via Differential Privacy](https://mlsanity.com/paper/1911.07116/) (2019)
- [Understanding Membership Inferences on Well-Generalized Learning Models](https://mlsanity.com/paper/1802.04889/) (2018)

## Built on by (most cited first, 2 of 2)

- [Explainable Data Poison Attacks on Human Emotion Evaluation Systems based on EEG Signals](https://mlsanity.com/paper/2301.06923/) (2023)
- [Mitigating Label Flipping Attacks in Malicious URL Detectors Using Ensemble Trees](https://mlsanity.com/paper/2403.02995/) (2024)

## Abstract (arXiv)

Most recent studies have shown several vulnerabilities to attacks with the potential to jeopardize the integrity of the model, opening in a few recent years a new window of opportunity in terms of cyber-security. The main interest of this paper is directed towards data poisoning attacks involving label-flipping, this kind of attacks occur during the training phase, being the aim of the attacker to compromise the integrity of the targeted machine learning model by drastically reducing the overall accuracy of the model and/or achieving the missclassification of determined samples. This paper is conducted with intention of proposing two new kinds of data poisoning attacks based on label-flipping, the targeted of the attack is represented by a variety of machine learning classifiers dedicated for malware detection using mobile exfiltration data. With that, the proposed attacks are proven to be model-agnostic, having successfully corrupted a wide variety of machine learning models; Logistic Regression, Decision Tree, Random Forest and KNN are some examples. The first attack is performs label-flipping actions randomly while the second attacks performs label flipping only one of the 2 classes in particular. The effects of each attack are analyzed in further detail with special emphasis on the accuracy drop and the misclassification rate. Finally, this paper pursuits further research direction by suggesting the development of a defense technique that could promise a feasible detection and/or mitigation mechanisms; such technique should be capable of conferring a certain level of robustness to a target model against potential attackers.