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Objective: -** To build binary and multi-class classifiers for differentiation between benign and malicious events, and type of malicious events over a large and imbalanced cybersecurity dataset.\n\n**2. Feature selection: -** Nature inspired Heuristic alhgorithms were used\n\u003cbr /\u003e\n  2.1 Artificial Bee Colony optimization (ABC)\n\u003cbr /\u003e\n  2.2 Flower Pollination Algorithm (FPA)\n\n**3. Machine Learning algorithm: -** KNN (K Nearest Neighbor)\n   \n**4. Objective function for feature selection: -** Recall - Penalty\n\u003cbr /\u003e\n  Recall: To maximize True positives and minimize False negatives\n\u003cbr /\u003e\n  Penalty: To reduce the number of features obtained out of feature selection\n\n**5. Evaluation: -** 13 metrics were used to evaluate performance of classifiers\n\n**6. Dataset: -** CIC dataset (Canada Institute of Cybersecurity)\n\n**7. Scaling approaches: -** Two independent methods were used\n\u003cbr /\u003e\n   7.1 Standard Scaler\n\u003cbr /\u003e\n   7.2 Robust Scaler\n\n**8. Results: -**\n\u003cbr /\u003e\n   8.1 When number of generations=5, models trained using FPA performed better than ABC.\n\u003cbr /\u003e\n   8.2 When number of generations=100, models trained using ABC performed better than FPA.\n\u003cbr /\u003e\n   8.3 Models trained with Standard scaler performed better than the models trained with Robust Scaler, irrespective of feature selection method and the number of generations.\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftaruchit%2Fbits_dissertation","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Ftaruchit%2Fbits_dissertation","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Ftaruchit%2Fbits_dissertation/lists"}