Awesome-Malware-Related-Papers

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Awesome-Malware-Related-Papers

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A curated list of malware-related papers.

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# Awesome-Malware-Related-Papers

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A curated list of malware-related papers.

### Contents:

- [Awesome-Malware-Related-Papers](#awesome-malware-related-papers)
    - [Contents:](#contents)
  - [1. Detection Papers](#1-detection-papers)
    - [1. Android](#1-android)
    - [2. Windows](#2-windows)
    - [3. Linux](#3-linux)
    - [4. PDF](#4-pdf)
  - [2. Adversarial Attack Papers](#2-adversarial-attack-papers)
    - [1. Android](#1-android-1)
      - [1. Evasion Attack](#1-evasion-attack)
      - [2. Poisoning Attack](#2-poisoning-attack)
    - [2. Windows](#2-windows-1)
      - [1. Evasion Attack](#1-evasion-attack-1)
    - [3. PDF](#3-pdf)
      - [1. Evasion Attack](#1-evasion-attack-2)
    - [4. Source Code Attribution](#4-source-code-attribution)
      - [1. Evasion Attack](#1-evasion-attack-3)
  - [3. Adversarial Defense Papers](#3-adversarial-defense-papers)
    - [1. Android](#1-android-2)
      - [1. Adversarial Defense](#1-adversarial-defense)
      - [2. Poisoning Defense](#2-poisoning-defense)
    - [2. Windows PE](#2-windows-pe)
      - [1. Adversarial Defense](#1-adversarial-defense-1)
    - [3. IoT](#3-iot)
      - [1. Adversarial Defense](#1-adversarial-defense-2)
  - [4. Misc Papers](#4-misc-papers)
  - [5. LLM-Related Papers](#5-llm-related-papers)
  - [6. Survey Papers](#6-survey-papers)
  - [Contributing](#contributing)
  - [Licenses](#licenses)

## 1. Detection Papers

### 1. Android

1. **Hey, You, Get off of My Market: Detecting Malicious Apps in Official and Alternative Android Market**. NDSS 2012. `Permission Footprinting for known malware, Heuristic-based detection engine for unknown malware` [[pdf](https://yajin.org/papers/ndss12_droidranger.pdf)]

2. **RiskRanker: Scalable and Accurate Zero-day Android Malware Detection**. MobiSys 2012. `Two-order Risk analyze engine by android behavior` [[pdf](https://yajin.org/papers/mobisys12.pdf)]

3. **Dissecting Android Malware: Characterization and Evolution**. IEEE S&P 2012. `Measurement Study of Android` [[pdf](https://yajin.org/papers/oakland12_sok.pdf)] [[dataset (not available now)](http://www.malgenomeproject.org/)]

4. **Using Probabilistic Generative Models for Ranking Risks of Android Apps**. CCS 2012. `Probabilistic Method with permission information` [[pdf](https://nds2.ccs.neu.edu/papers/android_risks.pdf)]

5. **DREBIN: Effective and Explainable Detection of Android Malware in Your Pocket**. NDSS 2014. `Static feature groups from manifest and disassembled code, SVM` [[pdf](https://www.sec.cs.tu-bs.de/pubs/2014-ndss.pdf)] [[dataset](https://www.sec.tu-bs.de/~danarp/drebin/index.html)]

6. **Apposcopy: Semantics-Based Detection of Android Malware through Static Analysis**. FSE 2014. `Semantic-based feature to detect a spefic family malware that steals private user information` [[pdf](https://dl.acm.org/doi/10.1145/2635868.2635869)]

7. **DroidMiner: Automated Mining and Characterization of Fine-grained Malicious Behaviors in Android Applications**. ESORICS 2014. `Detection by extracting the malware modalities` [[pdf](http://www.csl.sri.com/users/vinod/papers/droidminer.pdf)]

8. **Semantic Modelling of Android Malware for Effective Malware Comprehension, Detection, and Classification**. ISSTA 2016. `Detection by capuature the semantic information` [[pdf](https://dl.acm.org/doi/10.1145/2931037.2931043)]

9.  **MaMaDroid: Detecting Android Malware by Building Markov Chains of Behavioral Models**. NDSS 2017. `Behavior Model, Sequence of abstract API calls, Markov Chain` [[pdf](https://www.ndss-symposium.org/wp-content/uploads/2017/09/ndss2017_03B-3_Mariconti_paper.pdf)]

10. **Transcend: Detecting Concept Drift in Malware Classification Models**. USENIX Security 2017. `Conformal Evaluator for OOD` [[pdf](https://www.usenix.org/system/files/conference/usenixsecurity17/sec17-jordaney.pdf)] [[code](https://s2lab.cs.ucl.ac.uk/projects/transcend/)]

11. **Yes, Machine Learning Can Be More Secure! A Case Study on Android Malware Detection**. TDSC 2017. `A variant algorithm to SVM for resisting the obsfucation based attack` [[pdf](https://arxiv.org/pdf/1704.08996.pdf)]

12. **Lightweight, Obfuscation-Resilient Detection and Family Identification of Android Malware**. TOSEM 2017. `Detection by using Android-API usage for lightweight` [[pdf](https://dl.acm.org/doi/abs/10.1145/3162625)]

13. **DeepRefiner: Multi-layer Android Malware Detection System Applying Deep Neural Networks**. Euro S&P 2018. `code to vec for detection` [[pdf](https://ieeexplore.ieee.org/document/8406618)]

14. **Coevolution of Mobile Malware and Anti-Malware**. TIFS 2018. `Coevolution with android malware and anti-malware` [[pdf](https://ieeexplore.ieee.org/abstract/document/8332973)]

15. **TESSERACT: Eliminating Experimental Bias in Malware Classification across Space and Time**. USENIX 2019. `New Metric to evaluate the classify performance during time` [[pdf](https://www.usenix.org/system/files/sec19-pendlebury.pdf)] [[code](https://s2lab.cs.ucl.ac.uk/projects/tesseract/)]

16. **A Multimodal Deep Learning Method for Android Malware Detection Using Various Features**. TIFS 2019. `Use opcode sequence to detect Malware` [[pdf](https://ieeexplore.ieee.org/abstract/document/8443370)]

17. **AndrEnsemble: Leveraging API Ensembles to Characterize Android Malware Families**. AsiaCCS 2019. `Use API call graph to detect Malware` [[pdf](https://dl.acm.org/doi/10.1145/3321705.3329854)]

18. **Android Malware Detection via (Somewhat) Robust Irreversible Feature Transformations**. TIFS 2020. `Detect Malware using feature fusion` [[pdf](https://ieeexplore.ieee.org/abstract/document/9013026)]

19. **SDAC: A Slow-Aging Solution for Android Malware Detection Using Semantic Distance Based API Clustering**. TDSC 2020. `Mitigate model aging by API clustering` [[pdf](https://ieeexplore.ieee.org/abstract/document/9013026)]

20. **Byte-level malware classification based on markov images and deep learning**. Computers & Security 2020. `Use markov image to detect malware` [[pdf](https://www.sciencedirect.com/science/article/pii/S0167404820300262)]

21. **Enhancing State-of-the-art Classifiers with API Semantics to Detect Evolved Android Malware**. CCS 2020. `Use API Relation to detect Malware` [[pdf](https://dl.acm.org/doi/pdf/10.1145/3372297.3417291)]

22. **VAHunt: Warding Off New Repackaged Android Malware in App-Virtualization’s Clothing**. CCS 2020. `App Virtualization malware detection` [[pdf](https://dl.acm.org/doi/10.1145/3372297.3423341)]

23. **Why an Android App Is Classified as Malware: Toward Malware Classification Interpretation**. TOSEM 2021. `interperting malware detection algorithm` [[pdf](https://dl.acm.org/doi/abs/10.1145/3423096)] [[code](https://github.com/wubozhi/Xmal)]

24. **SpecView: Malware Spectrum Visualization Framework With Singular Spectrum Transformation**. TIFS 2021. `Detection using spectrum` [[pdf](https://ieeexplore.ieee.org/abstract/document/9607026)]

25. **An Inside Look into the Practice of Malware Analysis**. CCS 2021. `Malware analysis in practice measurement` [[pdf](https://blough.ece.gatech.edu/research/papers/ccs21.pdf)]

26. **Differential Training: A Generic Framework to Reduce Label Noises for Android Malware Detection**. NDSS 2021. `Detect noisy labels` [[pdf](https://www.ndss-symposium.org/wp-content/uploads/ndss2021_4C-3_24126_paper.pdf)]

27. **CADE: Detecting and Explaining Concept Drift Samples for Security Applications**. USENIX Security 2021. `OOD Detection` [[pdf](https://www.usenix.org/system/files/sec21-yang-limin.pdf)] [[code](https://github.com/whyisyoung/CADE)]

28. **Can We Leverage Predictive Uncertainty to Detect Dataset Shift and Adversarial Examples in Android Malware Detection?**. ACSAC 2021. `Predictive Uncertainity is useful to dataset shift but is useless for adversarial attack` [[pdf](https://arxiv.org/pdf/2109.09654.pdf)]

29. **Heterogeneous Temporal Graph Transformer: An Intelligent System for Evolving Android Malware Detection**. KDD 2021. `Detect Malware Using relation graph` [[pdf](https://doi.org/10.1145/3447548.3467168)] [[code](https://github.com/kdd2021drdroid/KDD2021_DrDroid)]

30. **Transcending TRANSCEND: Revisiting Malware Classification in the Presence of Concept Drift**. IEEE S&P 2022. `OOD detection` [[pdf](https://arxiv.org/pdf/2010.03856.pdf)]  [[code](https://s2lab.cs.ucl.ac.uk/projects/transcend/)]

31. **A Deep Dive Inside DREBIN: An Explorative Analysis beyond Android Malware Detection Scores**. ACM TOPS 2022. `Analyze the feature of Drebin classifier` [[pdf](https://dl.acm.org/doi/full/10.1145/3503463)]

32. **MsDroid: Identifying Malicious Snippets for Android Malware Detection**. TDSC 2023. `Andorid malware snippets detection using GNN` [[pdf](https://ieeexplore.ieee.org/document/9762803)] [[code](https://github.com/E0HYL/MsDroid)]

33. **Continuous Learning for Android Malware Detection**. USENIX Security 2023. `New Continual Learning Paridigram for Malware detection` [[pdf](https://arxiv.org/pdf/2302.04332.pdf)] [[code](https://github.com/wagner-group/active-learning)]

### 2. Windows

1. **Malware Detection by Eating a Whole EXE**. AAAI WS 2018. `Detection using raw bytes` [[pdf](https://arxiv.org/pdf/1710.09435.pdf)]

2. **Activation Analysis of a Byte-Based Deep Neural Network for Malware Classification**. AAAI WS 2018. `Detection using raw bytes` [[pdf](https://arxiv.org/pdf/1710.09435.pdf)]

3. **Towards Paving the Way for Large-Scale Windows Malware Analysis: Generic Binary Unpacking with Orders-of-Magnitude Performance Boost**. IEEE S&P WS 2019. `Detection using raw bytes` [[pdf](https://arxiv.org/pdf/1903.04717.pdf)]

4. **Enhancing Robustness of Malware Detection using Synthetically-adversarial Samples**. GLOBECOM 2020. `Adversarial training for enhence the detection` [[pdf](https://ieeexplore.ieee.org/abstract/document/9322377)]

5.  **Learning from Context: A Multi-View Deep Learning Architecture for Malware Detection**. IEEE S&P WS 2020. `Detection with filepath` [[pdf](https://ieeexplore.ieee.org/document/9283862)]

6.  **When Malware is Packin’ Heat; Limits of Machine Learning Classifiers Based on Static Analysis Features**. NDSS 2020. `ML classifier ability for packed malware` [[pdf](https://www.ndss-symposium.org/wp-content/uploads/2020/02/24310-paper.pdf)]

7.  **You Are What You Do: Hunting Stealthy Malware via Data Provenance Analysis**. NDSS 2020. `Detect Parasitic Malware` [[pdf](https://www.ndss-symposium.org/wp-content/uploads/2020/02/24167-paper.pdf)]

8.  **Survivalism: Systematic Analysis of Windows Malware Living-Off-The-Land**. IEEE S&P 2021. `Analysis Living-off-the-land Malware` [[pdf](http://www.mgraziano.info/docs/survivalism_sp21.pdf)]

9.  **I-MAD: Interpretable Malware Detector Using Galaxy Transformer**. Computers&Security 2021. `Detecting malware using transformers` [[pdf](https://arxiv.org/pdf/1909.06865.pdf)]

10. **Does Every Second Count? Time-based Evolution of Malware Behavior in Sandboxes**. NDSS 2021. `Time for dynamic analysis` [[pdf](https://www.s3.eurecom.fr/docs/ndss21_kuechler.pdf)]

11. **Prevalence and Impact of Low-Entropy Packing Schemes in the Malware Ecosystem**. NDSS 2022. `Determine executables packing` [[pdf](https://www.ndss-symposium.org/wp-content/uploads/2020/02/24297-paper.pdf)]

12. **CruParamer: Learning on Parameter-Augmented API Sequences for Malware Detection**. TIFS 2022. `Detection using API sequences` [[pdf](https://ieeexplore.ieee.org/document/9715123)]

13. **MalGraph: Hierarchical Graph Neural Networks for Robust Windows Malware Detection**. INFOCOM 2022. `Detection Using GNN` [[pdf](https://ieeexplore.ieee.org/document/9796786)]

### 3. Linux

1. **Understanding Linux Malware**. IEEE S&P 2018. `Static and Dynamic analysis for linux malware` [[pdf](https://reyammer.io/publications/2018_oakland_linuxmalware.pdf)]

### 4. PDF

1. **On Training Robust PDF Malware Classifiers**. USENIX Security 2020. `Robustness Certification` [[pdf](https://www.usenix.org/system/files/sec20-chen-yizheng.pdf)]

## 2. Adversarial Attack Papers

### 1. Android

#### 1. Evasion Attack

1. **DroidChameleon: Evaluating Android Anti-malware against Transformation Attacks**. ASIA CCS 2013. `Code Transformation techniques to generate malware` [[pdf](https://pages.cs.wisc.edu/~vrastogi/static/papers/rcj13b.pdf)]

2. **Catch Me If You Can: Evaluating Android Anti-Malware Against Transformation Attacks**. TIFS 2014. `Evaluate DroidChameleon in AV Engines` [[pdf](https://ieeexplore.ieee.org/abstract/document/6661334)]

3. **Adversarial Examples for Malware Detection**. ESORICS 2017. `Perturbed AndroidManifest File` [[pdf](https://link.springer.com/content/pdf/10.1007/978-3-319-66399-9_4.pdf)]

4. **Malware Detection in Adversarial Settings: Exploiting Feature Evolutions and Confusions in Android Apps**. ACSAC 2017. `Perturbated Malware using program transplant` [[pdf](http://youngwei.com/pdf/MRV.pdf)]

5. **Quantifying the Impact of Adversarial Evasion Attacks on Machine Learning Based Android Malware Classifiers**. NCA 2017. `Adversarial Attack in derbin by transfer attack` [[pdf](https://ieeexplore.ieee.org/abstract/document/8171381)]

6. **Picking on the family: Disrupting android malware triage by forcing misclassification**. Expert Systems with Applications 2018. `Adversarial Attack on derbin and a detection algorithm` [[pdf](https://www.sciencedirect.com/science/article/pii/S0957417417307881)]

7. **Adversarial-Example Attacks Toward Android Malware Detection System**. IEEE SYSTEMS JOURNAL 2020. `Adversarial attack using GAN, but not mention problem space` [[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?arnumber=8688632)]

8. **Adversarial Deep Ensemble: Evasion Attacks and Defenses for Malware Detection**. TIFS 2020. `Adversarial attack android, ensumble` [[pdf](https://arxiv.org/pdf/2006.16545.pdf)] [[code](https://github.com/deqangss/adv-dnn-ens-malware)]

9.  **Intriguing Properties of Adversarial ML Attacks in the Problem Space**. IEEE S&P 2020. `Adversarial Attack for drebin` [[pdf](https://arxiv.org/pdf/1911.02142.pdf)] [[code](https://s2lab.cs.ucl.ac.uk/projects/intriguing/)]

10. **Android HIV: A Study of Repackaging Malware for Evading Machine-Learning Detection**. TIFS 2020. `White-box C&W repackage attack` [[pdf](https://arxiv.org/pdf/1808.04218)]

11. **ShadowDroid: Practical Black-box Attack against ML-based Android Malware Detection**. IEEE ICAPDS 2021. `Transfer attack to malware detector` [[pdf](https://ieeexplore.ieee.org/abstract/document/9763777)]

12. **Robustness of Image-based Android Malware Detection Under Adversarial Attacks**. IEEE ICC 2021. `Attack Visual-based Feature by add benign component` [[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9500425)]

13. **Structural Attack against Graph Based Android Malware Detection**. ACM CCS 2021. `Adversarial Attack to Grpah base Malware detection` [[pdf](https://dl.acm.org/doi/abs/10.1145/3460120.3485387)]

14. **Measuring Vulnerabilities of Malware Detectors with Explainability-Guided Evasion Attacks**. Arxiv 2022. `Explainable-guided adversarial attack` [[pdf](https://arxiv.org/pdf/2111.10085.pdf)]

15. **Realizable Universal Adversarial Perturbations for Malware**. Arxiv 2022. `UAP for Adversarial Malware` [[pdf](https://s2lab.cs.ucl.ac.uk/downloads/universal.pdf)]

16. **EvadeDroid: A Practical Evasion Attack on Machine Learning for Black-box Android Malware Detection**. Arxiv 2022. `Black Box attack using random search` [[pdf](https://arxiv.org/pdf/2110.03301.pdf)]

17. **OFEI: A Semi-black-box Android Adversarial Sample Attack Framework Against DLaaS**. TOC 2023. `Feature Space Adversarial Attack on Drebin` [[pdf](https://arxiv.org/pdf/2105.11593.pdf)]

18. **Black-box Adversarial Example Attack towards FCG Based Android Malware Detection under Incomplete Feature Information**. USENIX Security 2023. `Black-box Android Adversarial Malware against the FCG-based ML classifier` [[pdf](https://arxiv.org/pdf/2303.08509.pdf)]

19. **Efficient Query-Based Attack against ML-Based Android Malware Detection under Zero Knowledge Setting**. ACM CCS 2023. `Semantic similar perturbations are more likely to have similar evasion effectiveness` [[pdf](https://arxiv.org/pdf/2309.01866.pdf)] [[code](https://github.com/gnipping/AdvDroidZero-Access-Instructions)]

#### 2. Poisoning Attack

1. **Automated Poisoning Attacks and Defenses in Malware Detection Systems: An Adversarial Machine Learning Approach**. Computers & Security 2018. `Poisoning attack in android` [[pdf](https://www.sciencedirect.com/science/article/pii/S0167404817302444)]

2. **When Does Machine Learning FAIL? Generalized Transferability for Evasion and Poisoning Attacks**. USENIX Security 2018. `Target Poisoning attack by injecting perturbed benign apps for drebin classifier` [[pdf](https://www.usenix.org/system/files/conference/usenixsecurity18/sec18-suciu.pdf)]

3. **Explanation-Guided Backdoor Poisoning Attacks Against Malware Classifiers**. USENIX Security 2021. `Backdoor attack in android` [[pdf](https://www.usenix.org/system/files/sec21-severi.pdf)]

4. **Backdoor Attack on Machine Learning Based Android Malware Detectors**. TDSC 2021. `Backdoor attack in android` [[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=9477038)]

5.  **Jigsaw Puzzle: Selective Backdoor Attack to Subvert Malware Classifiers**. IEEE S&P 2023. `Backdoor attack with the targeted family` [[pdf](https://arxiv.org/pdf/2202.05470.pdf)]

### 2. Windows

#### 1. Evasion Attack

1.  **Generating Adversarial Malware Examples for Black-Box Attacks Based on GAN**. Arxiv 2017. `Gernerate AE using GAN` [[pdf](https://arxiv.org/pdf/1702.05983.pdf)]

2.  **Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers**. ACSAC 2020. `Black-Box Adversarial Attack for windows API sequence-based classification method. Injecting a lot of API Sequences and remove it` [[pdf](https://arxiv.org/pdf/1911.02142.pdf)]

3.  **Functionality-Preserving Black-Box Optimization of Adversarial Windows Malware**. TIFS 2021. `Black-box adversarial attack by adding benign bytes to malicious file. Using Generic Algorithm` [[pdf](https://arxiv.org/pdf/2003.13526.pdf)]

4. **MAB-Malware: A Reinforcement Learning Framework for Attacking Static Malware Classifiers**. Asia CCS 2022. `Reinforcement Learning` [[pdf](https://arxiv.org/pdf/2003.03100.pdf)]

5. **Semantics-preserving Reinforcement Learning Attack Against Graph Neural Networks for Malware Detection**. TDSC 2022. `Reinforcement Learning by adding nop instruction` [[pdf](https://arxiv.org/pdf/2009.05602.pdf)]

### 3. PDF

#### 1. Evasion Attack

1. **Evading Classifiers by Morphing in the Dark**. ACM CCS 2017. `Morpher and search to generate adversarial PDF` [[pdf](https://arxiv.org/pdf/1705.07535.pdf)]

### 4. Source Code Attribution

#### 1. Evasion Attack

1. **Misleading Authorship Attribution of Source Code using Adversarial Learning**. USENIX Security 2019. `Adversarial attack in source code, MCST` [[pdf](https://arxiv.org/pdf/1905.12386.pdf)] [[code](http://www.tu-braunschweig.de/sec/research/code/imitator)]

2.  **A Practical Black-box Attack on Source Code Authorship Identification Classifiers**. TIFS 2021. `Transfer attack for code identification` [[pdf](https://nesa.zju.edu.cn/download/lqj_pdf_tifs2021.pdf)]

## 3. Adversarial Defense Papers

### 1. Android

#### 1. Adversarial Defense

1.  **Selective Adversarial Learning for Mobile Malware**. IEEE TrustCom 2019. `Simple Adversarial Defense` [[pdf](https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=8887403)]

2.  **Effectiveness of Adversarial Examples and Defenses for Malware Classification**. ICSPCS 2019. `Distillation, Ensumble, Adversaial Training` [[pdf](https://arxiv.org/pdf/1909.04778.pdf)]

3. **Robust Android Malware Detection against Adversarial Example Attacks**. WWW 2021. `Use VAE to distinguish the adversarial malware` [[pdf](https://www4.comp.polyu.edu.hk/~csxluo/RobustDetection.pdf)] [[code](https://github.com/YangXiuxuan/Robust-Android-Malware-Detection-Against-Adversarial-Example-Attacks)]

4. **A Framework for Enhancing Deep Neural Networks Against Adversarial Malware**. TNSE 2021. `Use AE to get robust feature embedding` [[pdf](https://arxiv.org/pdf/2004.07919.pdf)] [[code](https://github.com/deqangss/aics2019_challenge_adv_mal_defense)]

5.  **Robust Android Malware Detection System Against Adversarial Attacks Using Q-Learning**. NDSS Poster 2021. `Q-Learning for adversarial attack and retraining for defense` [[pdf](https://www.ndss-symposium.org/wp-content/uploads/NDSS2021posters_paper_3.pdf)]

#### 2. Poisoning Defense

1.  **On Defending Against Label Flipping Attacks on Malware Detection Systems**. Neural Computing and Applications 2020. `Detected poisoned sample by label prediction` [[pdf](https://link.springer.com/content/pdf/10.1007/s00521-020-04831-9.pdf)]

### 2. Windows PE

#### 1. Adversarial Defense

1. **Adversarial Training for Raw-Binary Malware Classifiers**. USENIX Security 2023. `Adversarial Training for Windows PE malware` [[pdf](https://www.usenix.org/system/files/sec23fall-prepub-146-lucas.pdf)]

### 3. IoT

#### 1. Adversarial Defense

1.  **Adversarial android malware detection for mobile multimedia applications in IoT environments**. Multimedia Tools and Applications 2020. `Robust Neural Network` [[pdf](https://link.springer.com/content/pdf/10.1007/s11042-020-08804-x.pdf)]

## 4. Misc Papers

1. **Automatically Inferring Malware Signatures for Anti-Virus Assisted Attacks**. ACM AsiaCCS 2017. `Inferring the signature of AVs` [[pdf](https://dl.acm.org/doi/10.1145/3052973.3053002)]

2. **Toward Systematically Exploring Antivirus Engines**. DIMVA 2018. `Infer AVs components` [[pdf](https://link.springer.com/content/pdf/10.1007/978-3-319-93411-2_18.pdf)]

3. **Measuring and Modeling the Label Dynamics of Online Anti-Malware Engines**. USENIX Security 2020. `Label dynamics in vriustotal` [[pdf](https://www.usenix.org/system/files/sec20-zhu.pdf)]

4. **DeepReflect: Discovering Malicious Functionality through Binary Reconstruction**. USENIX Security 2021. `Use AutoEncoder to identify the functionality of components` [[pdf](https://www.usenix.org/system/files/sec21fall-downing.pdf)]

5. **Debiasing Android Malware Datasets: How Can I Trust Your Results If Your Dataset Is Biased?**. TIFS 2022. `Debias Dataset` [[pdf](https://ieeexplore.ieee.org/document/9787514/)]

6. **A Large-scale Temporal Measurement of Android Malicious Apps: Persistence, Migration, and Lessons Learned**. USENIX Security 2022. `Malware influence days in realworld` [[pdf](https://www.usenix.org/system/files/sec22-shen-yun.pdf)]

7. **The Droid is in the Details: Environment-aware Evasion of Android Sandboxes**. NDSS 2022. `Classify Android Sandbox` [[pdf](https://droid-in-the-details.github.io/)]

8. **Trojan Source: Invisible Vulnerabilities**. USENIX Security 2023. `UTF-8 Control Symbol to inject malware` [[pdf](https://www.usenix.org/system/files/sec23fall-prepub-151-boucher.pdf)]

9.  **Humans vs. Machines in Malware Classification**. USENIX Security 2023. `Leveraging Feature Difference between human and ML-based classifiers` [[pdf](https://www.usenix.org/system/files/sec23summer_241-aonzo-prepub.pdf)]

10. **URET: Universal Robustness Evaluation Toolkit (for Evasion)**. USENIX Security 2023. `General Toolbox to select the perdefined perturbations` [[pdf](https://www.usenix.org/system/files/sec23summer_347-eykholt-prepub.pdf)] [[code](https://github.com/IBM/URET)]

11. **PELICAN: Exploiting Backdoors of Naturally Trained Deep Learning Models In Binary Code Analysis**. USENIX Security 2023. `Reverse engineering natural backdoor in transformer-based x86 binary code analysis task` [[pdf](https://www.usenix.org/system/files/sec23fall-prepub-493-zhang-zhuo.pdf)]

12. **On The Empirical Effectiveness of Unrealistic Adversarial Hardening Against Realistic Adversarial Attacks**. IEEE S&P 2023. `Adversarial attacks on feature space may enhance the robustness in problem space` [[pdf](https://arxiv.org/pdf/2202.03277.pdf)] [[code](https://github.com/serval-uni-lu/realistic_adversarial_hardening)]

13. **Decoding the Secrets of Machine Learning in Windows Malware Classification: A Deep Dive into Datasets, Features, and Model Performance**. ACM CCS 2023. `static features are better than dynamic feature in WindowsPE malware detection` [[pdf](https://arxiv.org/pdf/2307.14657.pdf)]

14. **FINER: Enhancing State-of-the-art Classifiers with Feature Attribution to Facilitate Security Analysis**. ACM CCS 2023. `Ensumble explaination for different stakeholder`  [[pdf](https://arxiv.org/pdf/2308.05362.pdf)] [[code](https://github.com/E0HYL/FINER-explain)]

15. **From Grim Reality to Practical Solution: Malware Classification in Real-World Noise**. IEEE S&P 2023. `Noise Learning method for malware detection` [[pdf](https://henrygwb.github.io/publications/sp23.pdf)] [[code](https://github.com/gnipping/morse)]

## 5. LLM-Related Papers

1. **GPThreats-3: Is Automatic Malware Generation a Threat?**. IEEE S&P Workshops 2023. `Exporing the possibility of GPT-3 for generate malware and deobfuscation` [[pdf](https://ieeexplore.ieee.org/document/10188649)] [[code](https://github.com/marcusbotacin/Automated.Malware.Generation)]

2. **An Attacker’s Dream? Exploring the Capabilities of ChatGPT for Developing Malware**. CSET Workshop 2023. `Exporing the possibility of ChatGPT for generate malware` [[pdf](https://dl.acm.org/doi/abs/10.1145/3607505.3607513)]

## 6. Survey Papers

1. **Android Security: A Survey of Issues, Malware Penetration, and Defenses**. IEEE Communications Surveys & Tutorials 2015. [[pdf](https://ieeexplore.ieee.org/document/69999118)]

2. **Arms Race in Adversarial Malware Detection: A Survey**. ACM Computing Surveys 2021. [[pdf](https://arxiv.org/pdf/2005.11671.pdf)]

3. **Deep Learning for Android Malware Defenses: a Systematic Literature Review**. ACM Computing Surveys 2022. [[pdf](https://dl.acm.org/doi/abs/10.1145/3544968)]

4. **MalRadar: Demystifying Android Malware in the New Era**. ACM Meas. Anal. Computing Surveys 2022. [[pdf](https://dl.acm.org/doi/10.1145/3530906)]

## Contributing

This list is mainly maintained by Ping He from [NESA Lab](https://nesa.zju.edu.cn/index.html).

We are very much welcome contributors for contributing this repository!

**Markdown format**
```markdown
**Paper Name**. Conference Year. `Keywords` [[pdf](pdf_link)] [[code](code_link)] [[dataset](dataset_link)]
```

## Licenses

[![CC0](http://i.creativecommons.org/p/zero/1.0/88x31.png)](http://creativecommons.org/publicdomain/zero/1.0/)

To the extent possible under law, [gnipping](https://github.com/gnipping) all copyright and related or neighboring rights to this repository.

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