# A Survey on Adversarial Attacks for Malware Analysis

arXiv: https://arxiv.org/abs/2111.08223 · HTML: https://mlsanity.com/paper/2111.08223/

## Key facts

- Cited by 16 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 16 Nov 2021.

## Built on by (most cited first, 16 of 16)

- [From ChatGPT to ThreatGPT: Impact of Generative AI in Cybersecurity and Privacy](https://mlsanity.com/paper/2307.00691/) (2023)
- [MERLIN -- Malware Evasion with Reinforcement LearnINg](https://mlsanity.com/paper/2203.12980/) (2022)
- [Analysis of Label-Flip Poisoning Attack on Machine Learning Based Malware Detector](https://mlsanity.com/paper/2301.01044/) (2023)
- [Cyber Sentinel: Exploring Conversational Agents in Streamlining Security Tasks with GPT-4](https://mlsanity.com/paper/2309.16422/) (2023)
- [Out-of-Distribution Data: An Acquaintance of Adversarial Examples -- A Survey](https://mlsanity.com/paper/2404.05219/) (2024)
- [A Comparison of Adversarial Learning Techniques for Malware Detection](https://mlsanity.com/paper/2308.09958/) (2023)
- [StratDef: Strategic Defense Against Adversarial Attacks in ML-based Malware Detection](https://mlsanity.com/paper/2202.07568/) (2022)
- [Chaotic Variational Auto encoder-based Adversarial Machine Learning](https://mlsanity.com/paper/2302.12959/) (2023)
- [Analyzing the Impact of Adversarial Examples on Explainable Machine Learning](https://mlsanity.com/paper/2307.08327/) (2023)
- [Adversarial Patterns: Building Robust Android Malware Classifiers](https://mlsanity.com/paper/2203.02121/) (2022)
- [Robustness of Selected Learning Models under Label-Flipping Attack](https://mlsanity.com/paper/2501.12516/) (2025)
- [Certifiably robust malware detectors by design](https://mlsanity.com/paper/2508.10038/) (2025)
- [Explainability-Guided Adversarial Attacks on Transformer-Based Malware Detectors Using Control Flow Graphs](https://mlsanity.com/paper/2604.03843/) (2026)
- [Adversarial Co-Evolution of Malware and Detection Models: A Bilevel Optimization Perspective](https://mlsanity.com/paper/2604.22569/) (2026)
- [Gray-Box Poisoning of Continuous Malware Ingestion Pipelines](https://mlsanity.com/paper/2605.04698/) (2026)
- [A Comprehensive Review of One-Pixel Attack: Research Status, Taxonomy, Applications, Regulation Policy and Future Directions](https://mlsanity.com/paper/2610.00125/) (2026)

## Abstract (arXiv)

Machine learning has witnessed tremendous growth in its adoption and advancement in the last decade. The evolution of machine learning from traditional algorithms to modern deep learning architectures has shaped the way today's technology functions. Its unprecedented ability to discover knowledge/patterns from unstructured data and automate the decision-making process led to its application in wide domains. High flying machine learning arena has been recently pegged back by the introduction of adversarial attacks. Adversaries are able to modify data, maximizing the classification error of the models. The discovery of blind spots in machine learning models has been exploited by adversarial attackers by generating subtle intentional perturbations in test samples. Increasing dependency on data has paved the blueprint for ever-high incentives to camouflage machine learning models. To cope with probable catastrophic consequences in the future, continuous research is required to find vulnerabilities in form of adversarial and design remedies in systems. This survey aims at providing the encyclopedic introduction to adversarial attacks that are carried out against malware detection systems. The paper will introduce various machine learning techniques used to generate adversarial and explain the structure of target files. The survey will also model the threat posed by the adversary and followed by brief descriptions of widely accepted adversarial algorithms. Work will provide a taxonomy of adversarial evasion attacks on the basis of attack domain and adversarial generation techniques. Adversarial evasion attacks carried out against malware detectors will be discussed briefly under each taxonomical headings and compared with concomitant researches. Analyzing the current research challenges in an adversarial generation, the survey will conclude by pinpointing the open future research directions.