Datasets
Standard Dataset
AIoT Malware detection (Zeek preprocessed)
- Citation Author(s):
- Submitted by:
- Rafael Kaliski
- Last updated:
- Fri, 02/16/2024 - 12:43
- DOI:
- 10.21227/kkpn-2q14
- Data Format:
- Links:
- License:
416 Views- Categories:
- Keywords:
Abstract
Datsets and scripts are for derivation of a lightweight AIoT malware detection model.
Datasets and Scripts for “Lightweight Meta-learning BotNet Attack Detection”
— Requirements (code tested with):
Python==3.9
matplotlib==3.5.2
mkl==2022.1.0
mkl_service==2.4.0
mlens==0.2.3
numpy==1.21.5
pandas==1.4.2
scikit_learn==1.1.1
scipy==1.8.0
seaborn==0.11.2
==============================
Datasets and Scripts
==============================
Binary classifier:
——
IoT23 Script: Ensemble_IoT23_Binary.ipynb
IoT23 Dataset: IoT23.csv
Meta Models: SuperLearner, Subsemble, Sequential
====
IoT23 Script: NML_IoT23_Binary.ipynb
IoT23 Dataset: IoT23.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
——
TON Script: Ensemble_TON_Binary.ipynb
TON Dataset: ToN_Distrib.csv
Meta Models: SuperLearner, Subsemble, Sequential
=====
TON Script: NML_TON_Binary.ipynb
TON Dataset: ToN_Distrib.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
——
KDD Script: MetaLearning_KDDCUP99.ipynb
KDD Dataset: KDD99_Labeled.csv
Meta Models: SuperLearner, Subsemble, Sequential
=====
KDD Script: NML_KDD.ipynb
KDD Dataset: KDD99_Labeled.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
+++++++++++++++++++++++++++++++
Multicategory classifer:
——
IoT23 Script: Ensemble_MultiCategories_IOT23.ipynb
IoT23 Dataset: Iot-Data.csv
Meta Models: SuperLearner, Subsemble, Sequential
====
IoT23 Script: NonMetaLearner_MultiCategories_IOT23.ipynb
IoT23 Dataset: Iot-Data.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
——
TON Script: Ensembles_MultiCategories_TON.ipynb
TON Dataset: ToN_Distrib.csv
Meta Models: SuperLearner, Subsemble, Sequential
=====
TON Script: NonMetaLearners_MultiCategories_TON.ipynb
TON Dataset: ToN_Distrib.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
——
KDD Script: Ensembles_MultiCategories_KDD99.ipynb
KDD Dataset: KDD99_10%_Labeled.csv
Meta Models: SuperLearner, Subsemble, Sequential
=====
KDD Script: NonMetaLearner_MultiCategories_KDD99.ipynb
KDD Dataset: KDD99_10%_Labeled.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
+++++++++++++++++++++++++++++++
——
Python==3.9
matplotlib==3.5.2
mkl==2022.1.0
mkl_service==2.4.0
mlens==0.2.3
numpy==1.21.5
pandas==1.4.2
scikit_learn==1.1.1
scipy==1.8.0
seaborn==0.11.2
==============================
Datasets and Scripts
==============================
Binary classifier:
——
IoT23 Script: Ensemble_IoT23_Binary.ipynb
IoT23 Dataset: IoT23.csv
Meta Models: SuperLearner, Subsemble, Sequential
====
IoT23 Script: NML_IoT23_Binary.ipynb
IoT23 Dataset: IoT23.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
——
TON Script: Ensemble_TON_Binary.ipynb
TON Dataset: ToN_Distrib.csv
Meta Models: SuperLearner, Subsemble, Sequential
=====
TON Script: NML_TON_Binary.ipynb
TON Dataset: ToN_Distrib.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
——
KDD Script: MetaLearning_KDDCUP99.ipynb
KDD Dataset: KDD99_Labeled.csv
Meta Models: SuperLearner, Subsemble, Sequential
=====
KDD Script: NML_KDD.ipynb
KDD Dataset: KDD99_Labeled.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
+++++++++++++++++++++++++++++++
Multicategory classifer:
——
IoT23 Script: Ensemble_MultiCategories_IOT23.ipynb
IoT23 Dataset: Iot-Data.csv
Meta Models: SuperLearner, Subsemble, Sequential
====
IoT23 Script: NonMetaLearner_MultiCategories_IOT23.ipynb
IoT23 Dataset: Iot-Data.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
——
TON Script: Ensembles_MultiCategories_TON.ipynb
TON Dataset: ToN_Distrib.csv
Meta Models: SuperLearner, Subsemble, Sequential
=====
TON Script: NonMetaLearners_MultiCategories_TON.ipynb
TON Dataset: ToN_Distrib.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
——
KDD Script: Ensembles_MultiCategories_KDD99.ipynb
KDD Dataset: KDD99_10%_Labeled.csv
Meta Models: SuperLearner, Subsemble, Sequential
=====
KDD Script: NonMetaLearner_MultiCategories_KDD99.ipynb
KDD Dataset: KDD99_10%_Labeled.csv
Ensemble Models: Bagging, Boosting, Stacking
Weak Leaner Models: MLP, Naive Bayes, Random Forest, Logistic Regression, Decision Tree
+++++++++++++++++++++++++++++++
——
Dataset Files
- DataSets Alna_IoTJ_DataSets.zip (250.89 MB)
- Scripts Alna_IoTJ_Scripts.zip (67.47 kB)
