references.md

References:

Dataset:

• https://www.kaggle.com/amauricio/pe-files-malwares/data

Code and Thesis References:

• Dataset Loading and Visualising
  • https://www.dataquest.io/blog/jupyter-notebook-tutorial/
  • https://github.com/stephensheridan/adding-columns-to-pandas-dataframe
  • https://medium.com/@Zack_Elbazi/applying-artificial-intelligence-in-cyber-security-malware-detection-using-machine-learning-1147b75bfaf3
• Feature Importance/Selection
  • https://towardsdatascience.com/feature-selection-with-pandas-e3690ad8504b
  • https://mlwhiz.com/blog/2019/08/07/feature_selection/
  • https://h1ros.github.io/posts/feature-importance/
  • https://github.com/WillKoehrsen/feature-selector/blob/master/Feature%20Selector%20Usage.ipynb
  • https://towardsdatascience.com/feature-selection-with-pandas-e3690ad8504b
• Principal Component Analysis and Random Forest
  • https://datascienceplus.com/principal-component-analysis-pca-with-python/
  • https://jakevdp.github.io/PythonDataScienceHandbook/05.09-principal-component-analysis.html
  • https://stackabuse.com/implementing-pca-in-python-with-scikit-learn/
  • https://www.geeksforgeeks.org/principal-component-analysis-with-python/
  • https://builtin.com/data-science/step-step-explanation-principal-component-analysis
  • https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_classification_algorithms_random_forest.htm
• Naive Bayes Classifier
  • https://www.kaggle.com/prashant111/naive-bayes-classifier-tutorial
  • https://www.dataquest.io/blog/sci-kit-learn-tutorial/
  • https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_classification_algorithms_naive_bayes.htm
• KNN Classifier
  • https://www.kaggle.com/amolbhivarkar/knn-for-classification-using-scikit-learn
  • https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_knn_algorithm_finding_nearest_neighbors.htm
• Logistic Regression Classifier
  • https://towardsdatascience.com/building-a-logistic-regression-in-python-step-by-step-becd4d56c9c8
  • https://www.tutorialspoint.com/machine_learning_with_python/machine_learning_with_python_classification_algorithms_logistic_regression.htm
• Other Similar Work
  • https://github.com/obarrera/Machine-Learning-Malware-Detection/blob/master/Machine%20Learning%20Malware%20Detection%20/Machine%20Learning%20Malware%20Detection%20.md
  • https://media.kaspersky.com/en/enterprise-security/Kaspersky-Lab-Whitepaper-Machine-Learning.pdf
  • https://tech-zealots.com/malware-analysis/pe-portable-executable-structure-malware-analysis-part-2/
  • https://www.kaggle.com/amauricio/simple-machine-learning-antimalware
  • https://www.kaggle.com/amauricio/pe-files-malwares/data
  • https://github.com/amauricio/sklearn-antimalware
  • https://towardsdatascience.com/why-using-crisp-dm-will-make-you-a-better-data-scientist-66efe5b72686
  • https://medium.com/@Zack_Elbazi/applying-artificial-intelligence-in-cyber-security-malware-detection-using-machine-learning-1147b75bfaf3
  • https://www.hindawi.com/journals/tswj/2014/260905/
  • https://arxiv.org/ftp/arxiv/papers/1903/1903.02966.pdf
  • https://media.kaspersky.com/en/enterprise-security/Kaspersky-Lab-Whitepaper-Machine-Learning.pdf
  • https://www.academia.edu/38078184/ARTICLE_TYPE_Static_Malware_Clustering_Using_Enhanced_Deep_Embedding_Method
  • https://www.sciencedirect.com/science/article/pii/S1319157817300149
  • https://www.dataschool.io/simple-guide-to-confusion-matrix-terminology/
  • https://towardsdatascience.com/beyond-accuracy-precision-and-recall-3da06bea9f6c
  • https://stats.stackexchange.com/questions/258938/pca-before-random-forest-regression-provide-better-predictive-scores-for-my-data