| EMBER: An Open Dataset for Training Static PE Malware Machine Learning Models |
malwaredatasetml |
Anderson, Roth |
| EMBER2024 — A Benchmark Dataset for Holistic Evaluation of Malware Classifiers |
malwaredatasetbenchmark |
Joyce, Miller, Roth, Zak, et al. |
| Adversarial Attacks against Windows PE Malware Detection: A Survey of the State-of-the-Art |
adversarialmalwaresurvey |
Ling, Wu, Zhang, et al. |
| Adversarial attacks against Windows PE malware detection: A survey of the state-of-the-art |
adversarialmalwaresurvey |
Ling, Chen, Qian, Wu, Ji |
| Malware Detection by Eating a Whole EXE |
malwaredeep learning |
Raff, Barker, Sylvester, Brandon, Catanzaro, Nicholas |
| Learning to Evade Static PE Machine Learning Malware Models via Reinforcement Learning |
adversarialmalwareRL |
Anderson, Kharkar, Filar, Evans, Roth |
| Adversarial Examples for CNN-Based Malware Detectors |
adversarialmalwareCNN |
Chen, Su, Wang, He, Tang |
| A review of black-box adversarial attacks and defenses in machine learning-based malware detection |
adversarialmalwaresurvey |
Chen |
| HopSkipJumpAttack: A Query-Efficient Decision-Based Attack |
adversarialblack-box |
Chen, Jordan, Wainwright |
| Functionality-preserving Black-box Optimization of Adversarial Windows Malware |
adversarialmalwareblack-box |
Demetrio, Biggio, Lagorio, Roli, Armando |
| Towards a Practical Defense Against Adversarial Attacks on Deep Learning-Based Malware Detectors via Randomized Smoothing |
adversarialmalwaredefense |
Gibert, Zizzo, Le |
| Black-Box Attacks against RNN based Malware Detection Algorithms |
adversarialmalwareRNN |
Hu, Tan |
| Evaluating Realistic Adversarial Attacks against Machine Learning Models for Windows PE Malware Detection |
adversarialmalware |
Imran, Appice, Malerba |
| Adversarial training for raw-binary malware classifiers |
adversarialmalwaretraining |
Keane, Kantchelian, Stoian, Cassidy, et al. |
| Adversarial Malware Binaries: Evading Deep Learning for Malware Detection in Executables |
adversarialmalwaredeep learning |
Kolosnjaji, Demontis, Biggio, Maiorca, Giacinto, Roli, Eckert |
| Deceiving Portable Executable Malware Classifiers into Targeted Misclassification with Practical Adversarial Examples |
adversarialmalware |
Kucuk, Yan |
| GAMBD: Generating adversarial malware against MalConv |
adversarialmalwaremalconv |
Li, Li, Liang, Qin |
| The Limitations of Deep Learning in Adversarial Settings |
adversarialdeep learning |
Papernot, McDaniel, Jha, Fredrikson, Celik, Swami |
| Practical Black-Box Attacks against Machine Learning |
adversarialblack-box |
Papernot, McDaniel, Goodfellow, Jha, Celik, Swami |
| Generic Black-Box End-to-End Attack Against State of the Art API Call Based Malware Classifiers |
adversarialmalwareblack-box |
Rosenberg, Shabtai, Rokach, Elovici |
| Query-Efficient Black-Box Attack Against Sequence-Based Malware Classifiers |
adversarialmalwareblack-box |
Rosenberg, Shabtai, Rokach, Elovici |
| Exploring Adversarial Examples in Malware Detection |
adversarialmalware |
Suciu, Coull, Johns |
| Adversary Resistant Deep Neural Networks with an Application to Malware Detection |
adversarialmalwaredefense |
Wang, Guo, Zhang, Gunter, Danezis, Chen |
| A Survey of Adversarial Attack and Defense Methods for Malware Classification in Cyber Security |
adversarialmalwaresurvey |
Yan, Gu, Liu, Li, Li, Wu |
| A heuristic approach for detection of obfuscated malware |
malwareheuristic |
Treadwell, Zhou |
| A survey on heuristic malware detection techniques |
malwaresurvey |
Bazrafshan, Hashemi, Hazrati Fard, Hamzeh |
| Identifying useful features for malware detection in the Ember dataset |
malwareember |
Oyama, Miyashita, Kokubo |
| A comprehensive review on malware detection approaches |
malwaresurvey |
Aslan, Samet |
| Efficient Malware Analysis Using Metric Embeddings |
malwareembeddings |
Rudd et al. |
| EMBERSim: A Large-Scale Databank for Boosting Similarity Search in Malware Analysis |
malwareembeddingsdataset |
Corlatescu, Dinu, Gaman, Sumedrea |
| BEACON: Behavioral Malware Classification with Large Language Model Embeddings |
malwarellmembeddings |
arXiv preprint |
| GEMAL: Embedding Vector Generation Based on Function Call Graph |
malwareembeddingsgraph |
Springer |
| Malware Detection through Contextualized Vector Embeddings |
malwareembeddingsnlp |
IEEE |
| Similarity-Based Malware Classification Using Graph Neural Networks |
malwaregraph |
MDPI |
| Automatic Malware Description via Attribute Tagging and Similarity Embedding (SMART) |
malwareembeddings |
arXiv preprint |
| Advancements in File Similarity Techniques: Traditional and Modern Approaches for Malware Detection |
malwaresurvey |
Prasad |
| Using LLM Embeddings with Similarity Search for Botnet TLS Certificate Detection |
llmembeddings |
Rapid7 Research |
| Retrieval-Augmented Generation for Knowledge-Intensive NLP Tasks |
llmragnlp |
Lewis, Perez, Piktus, Petroni, et al. |
| Improving language understanding by generative pre-training |
llmnlp |
Radford, Narasimhan, Salimans, Sutskever |
| On the Biology of a Large Language Model |
llminterp |
Lindsey, Gurnee, Ameisen, Chen, Pearce, et al. |
| ReAct: Synergizing Reasoning and Acting in Language Models |
agentsllmreasoning |
Yao, Zhao, Yu, Du, Shafran, Narasimhan, Cao |
| Reflexion: Language Agents with Verbal Reinforcement Learning |
agentsllmRL |
Shinn et al. |
| Plan-and-Solve Prompting: Improving Zero-Shot Chain-of-Thought Reasoning by Large Language Models |
agentsllmprompting |
Wang et al. |
| Toolformer: Language Models Can Teach Themselves to Use Tools |
agentsllmtools |
Schick et al. |
| HuggingGPT: Solving AI Tasks with ChatGPT and its Friends in Hugging Face |
agentsllmplanning |
Shen et al. |
| Do the Right Thing: Studies in Limited Rationality |
agentsrationality |
Russell, Wefald |
| Open-source DeepResearch — Freeing our search agents |
agentsllmsearch |
Roucher, Villanova del Moral, Noyan, Wolf, Fourrier |
| Machine learning and deep learning |
mlfundamentals |
Janiesch, Zschech, Heinrich |
| CRISP-DM Twenty Years Later: From Data Mining Processes to Data Science Trajectories |
data miningprocess |
Martínez-Plumed, Contreras-Ochando, Ferri, et al. |
| DMME: Data mining methodology for engineering applications — a holistic extension to the CRISP-DM model |
data miningcrisp-dm |
Huber, Wiemer, Schneider, Ihlenfeldt |
| An application of multi-agent simulation to traffic behavior for evacuation in earthquake disaster |
multi-agentsimulation |
Kagaya, Uchida, Hagiwara |
| Application of Artificial Intelligence in the Study of Fishing Vessel Behavior |
aibehavioural |
Cheng, Zhang, Chen, Wang |
| Attribute-Aware Generative Design With Generative Adversarial Networks |
gangenerative |
Yuan, Moghaddam |