1 INTRODUCTION  
1.1 Motivation and Contribution  
1.2 Survey Organization  
1.3 Literature Search Resources  

2 MACHINE LEARNING PRELIMINARIES  
2.1 Deep Learning  
2.2 Convolutional Neural Network  
2.3 Reinforcement Learning  
2.4 Recurrent Neural Network  
2.5 Generative Adversarial Network  

3 FILE STRUCTURE  
3.1 Windows PE File Structure  
3.2 Android File Structure  
3.3 PDF File Structure  

4 MALWARE DETECTION  
4.1 Signature Based Malware Detection  
4.2 Zero-day Malware Detection  

5 ADVERSARIAL THREAT MODEL  
5.1 Adversarial Knowledge  
5.2 Attack Surface  
5.3 Adversarial Capabilities  
5.4 Adversarial Goals  

6 ADVERSARIAL ALGORITHMS  
6.1 Limited-memory Broyden-Fletcher-Goldfarb-Shanno (L-BFGS)  
6.2 Fast Gradient Sign Method (FGSM)  
6.3 Iterative Gradient Sign Method (IGSM)  
6.4 Jacobian Saliency Map Attack (JSMA)  
6.5 Carlini & Wagner attack (C&W)  
6.6 DeepFool  
6.7 Zeroth Order Optimization (ZOO)  
6.8 One Pixel Attack (OPA)  

7 ADVERSARIAL MALWARE EVASION ATTACKS  

8 CHALLENGES AND FUTURE DIRECTIONS  
8.1 Realistic (Practical) Attacks  
8.2 Perturbation Insertion Space  
8.3 Enhancing Efficiency  
8.4 Mapping Space Challenge  
8.5 Automated Attacks  
8.6 Explainable Adversarial  
8.7 Transferable Attacks  
8.8 Attacking Adversarial Defense  
8.9 Functionality Verification  
8.10 Attacking Federated Learning  
8.11 Benign Files Attack  
8.12 Targeting Unexplored Algorithms  
8.13 Standardizing Testbed and Metrics  
8.14 Adversarial Defense  

9 CONCLUSION