# Machine Learning Based Cyber Attacks Targeting on Controlled Information: A Survey

arXiv: https://arxiv.org/abs/2102.07969 · HTML: https://mlsanity.com/paper/2102.07969/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 28 of its 156 references that are in the map.
- First posted on arXiv 16 Feb 2021.

## Builds on (most cited first, 28 of 28)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Communication-Efficient Learning of Deep Networks from Decentralized Data](https://mlsanity.com/paper/1602.05629/) (2016)
- [Explaining and Harnessing Adversarial Examples](https://mlsanity.com/paper/1412.6572/) (2014)
- [Deep Learning Face Attributes in the Wild](https://mlsanity.com/paper/1411.7766/) (2014)
- [Deep Learning with Differential Privacy](https://mlsanity.com/paper/1607.00133/) (2016)
- [Membership Inference Attacks against Machine Learning Models](https://mlsanity.com/paper/1610.05820/) (2016)
- [Deep Sets](https://mlsanity.com/paper/1703.06114/) (2017)
- [How transferable are features in deep neural networks?](https://mlsanity.com/paper/1411.1792/) (2014)
- [Practical Black-Box Attacks against Machine Learning](https://mlsanity.com/paper/1602.02697/) (2016)
- [Evasion Attacks against Machine Learning at Test Time](https://mlsanity.com/paper/1708.06131/) (2017)
- [Distillation as a Defense to Adversarial Perturbations against Deep Neural Networks](https://mlsanity.com/paper/1511.04508/) (2015)
- [Stealing Machine Learning Models via Prediction APIs](https://mlsanity.com/paper/1609.02943/) (2016)
- [Exploiting Unintended Feature Leakage in Collaborative Learning](https://mlsanity.com/paper/1805.04049/) (2018)
- [ML-Leaks: Model and Data Independent Membership Inference Attacks and Defenses on Machine Learning Models](https://mlsanity.com/paper/1806.01246/) (2018)
- [Semi-supervised Knowledge Transfer for Deep Learning from Private Training Data](https://mlsanity.com/paper/1610.05755/) (2016)
- [Deep Models Under the GAN: Information Leakage from Collaborative Deep Learning](https://mlsanity.com/paper/1702.07464/) (2017)
- [Machine Learning with Membership Privacy using Adversarial Regularization](https://mlsanity.com/paper/1807.05852/) (2018)
- [Hacking Smart Machines with Smarter Ones: How to Extract Meaningful Data from Machine Learning Classifiers](https://mlsanity.com/paper/1306.4447/) (2013)
- [Stealing Hyperparameters in Machine Learning](https://mlsanity.com/paper/1802.05351/) (2018)
- [Distributed and parallel time series feature extraction for industrial big data applications](https://mlsanity.com/paper/1610.07717/) (2016)
- [Beyond Frontal Faces: Improving Person Recognition Using Multiple Cues](https://mlsanity.com/paper/1501.05703/) (2015)
- [Technical Aspects of Cyber Kill Chain](https://mlsanity.com/paper/1606.03184/) (2016)
- [6thSense: A Context-aware Sensor-based Attack Detector for Smart Devices](https://mlsanity.com/paper/1706.10220/) (2017)
- [Pyramid: Enhancing Selectivity in Big Data Protection with Count Featurization](https://mlsanity.com/paper/1705.07512/) (2017)
- [A software approach to defeating side channels in last-level caches](https://mlsanity.com/paper/1603.05615/) (2016)
- [Privacy-preserving schemes for Ad Hoc Social Networks: A survey](https://mlsanity.com/paper/1610.06095/) (2016)
- [A Cyber Kill Chain Based Taxonomy of Banking Trojans for Evolutionary Computational Intelligence](https://mlsanity.com/paper/1807.10446/) (2018)
- [Towards Distributed Privacy-Preserving Prediction](https://mlsanity.com/paper/1910.11478/) (2019)

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

- [Powerful Physical Adversarial Examples Against Practical Face Recognition Systems](https://mlsanity.com/paper/2203.15498/) (2022)
- [Privacy Inference Attacks and Defenses in Cloud-based Deep Neural Network: A Survey](https://mlsanity.com/paper/2105.06300/) (2021)
- [Privacy Preservation in Artificial Intelligence and Extended Reality (AI-XR) Metaverses: A Survey](https://mlsanity.com/paper/2310.10665/) (2023)
- [Uncertainty-aware Personal Assistant for Making Personalized Privacy Decisions](https://mlsanity.com/paper/2205.06544/) (2022)
- [Phantom: A High-Performance Computational Core for Sparse Convolutional Neural Networks](https://mlsanity.com/paper/2111.05002/) (2021)

## Abstract (arXiv)

Stealing attack against controlled information, along with the increasing number of information leakage incidents, has become an emerging cyber security threat in recent years. Due to the booming development and deployment of advanced analytics solutions, novel stealing attacks utilize machine learning (ML) algorithms to achieve high success rate and cause a lot of damage. Detecting and defending against such attacks is challenging and urgent so that governments, organizations, and individuals should attach great importance to the ML-based stealing attacks. This survey presents the recent advances in this new type of attack and corresponding countermeasures. The ML-based stealing attack is reviewed in perspectives of three categories of targeted controlled information, including controlled user activities, controlled ML model-related information, and controlled authentication information. Recent publications are summarized to generalize an overarching attack methodology and to derive the limitations and future directions of ML-based stealing attacks. Furthermore, countermeasures are proposed towards developing effective protections from three aspects -- detection, disruption, and isolation.