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ivanCipriano / Malware_Detection_Project_Gr06

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End-to-end PE malware detection with XGBoost and MalConv2. Adversarial robustness evaluation via GAMMA attack, SHAP interpretability, and multi-model Pareto comparison.

python machine-learning deep-learning static-analysis scikit-learn pytorch cybersecurity xgboost feature-engineering malware-detection adversarial-machine-learning security-evaluation shap explainability pe-analysis black-box-attack ember-dataset graduate-research malconv2 gamma-attack
  • Updated Jun 13, 2026
  • Python

Anish-530 / EMBERGuard

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An end-to-end malware detection pipeline leveraging multiple machine learning models, ensemble learning, and explainable AI techniques to accurately classify malicious and benign files. Built using the EMBER 2018 dataset with XGBoost, LightGBM, CatBoost, Neural Networks, and SHAP-based interpretability.

python data-science machine-learning deep-learning neural-network cybersecurity xgboost classification lightgbm ensemble-learning malware-analysis feature-engineering malware-detection security-research catboost explainable-ai threat-detection shap ember-dataset
  • Updated Jun 5, 2026
  • Jupyter Notebook

jordanricky1604-ship-it / malware-families-catalog

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2,899 real-world malware families categorized for security teams & incident response. Schema.org-ready dataset derived from EMBER 2018 with FAQ, MITRE ATT&CK, CISA advisory cross-refs, and per-family profiles. Apache-2.0 licensed.

ember machine-learning incident-response malware dataset cybersecurity ransomware infosec malware-analysis soc msp threat-intelligence malware-families blue-team mitre-attack threat-detection malware-classification security-dataset ember-dataset ember2018
  • Updated Jun 12, 2026
  • Python

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