# Comprehensive Survey on Adversarial Examples in Cybersecurity: Impacts, Challenges, and Mitigation Strategies

arXiv: https://arxiv.org/abs/2412.12217 · HTML: https://mlsanity.com/paper/2412.12217/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 53 references that are in the map.
- First posted on arXiv 16 Dec 2024.

## Builds on (most cited first, 9 of 9)

- [Distilling the Knowledge in a Neural Network](https://mlsanity.com/paper/1503.02531/) (2015)
- [Intriguing properties of neural networks](https://mlsanity.com/paper/1312.6199/) (2013)
- [Feature Squeezing: Detecting Adversarial Examples in Deep Neural Networks](https://mlsanity.com/paper/1704.01155/) (2017)
- [Adv-Makeup: A New Imperceptible and Transferable Attack on Face Recognition](https://mlsanity.com/paper/2105.03162/) (2021)
- [Adversarial Perturbations Against Deep Neural Networks for Malware Classification](https://mlsanity.com/paper/1606.04435/) (2016)
- [COPYCAT: Practical Adversarial Attacks on Visualization-Based Malware Detection](https://mlsanity.com/paper/1909.09735/) (2019)
- [MaskDGA: A Black-box Evasion Technique Against DGA Classifiers and Adversarial Defenses](https://mlsanity.com/paper/1902.08909/) (2019)
- [Mitigating Challenges in Ethereum's Proof-of-Stake Consensus: Evaluating the Impact of EigenLayer and Lido](https://mlsanity.com/paper/2410.23422/) (2024)
- [Strengthening DeFi Security: A Static Analysis Approach to Flash Loan Vulnerabilities](https://mlsanity.com/paper/2411.01230/) (2024)

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

- [Deep Positive-Negative Prototypes for Adversarially Robust Discriminative Prototypical Learning](https://mlsanity.com/paper/2504.03782/) (2025)

## Abstract (arXiv)

Deep learning (DL) has significantly transformed cybersecurity, enabling advancements in malware detection, botnet identification, intrusion detection, user authentication, and encrypted traffic analysis. However, the rise of adversarial examples (AE) poses a critical challenge to the robustness and reliability of DL-based systems. These subtle, crafted perturbations can deceive models, leading to severe consequences like misclassification and system vulnerabilities. This paper provides a comprehensive review of the impact of AE attacks on key cybersecurity applications, highlighting both their theoretical and practical implications. We systematically examine the methods used to generate adversarial examples, their specific effects across various domains, and the inherent trade-offs attackers face between efficacy and resource efficiency. Additionally, we explore recent advancements in defense mechanisms, including gradient masking, adversarial training, and detection techniques, evaluating their potential to enhance model resilience. By summarizing cutting-edge research, this study aims to bridge the gap between adversarial research and practical security applications, offering insights to fortify the adoption of DL solutions in cybersecurity.