Project Implementation Plan for Concept Drift Detection in Network Intrusion
Datasets
Objective: This project is dedicated to identifying concept drift within network
intrusion datasets from the years 2017 and 2018 and subsequently adapting machine
learning models to these changes. The project will utilize Google Colab for all
computational tasks, offering a flexible and powerful cloud-based environment for
data analysis, model training, and evaluation.
Phase 1: Data Preparation and Preprocessing
 Data Collection: Download the 2017 and 2018 CIC IDS datasets from specified
Google Drive links.
 Combination and Consolidation: Aggregate the files from each year into a
singular dataset to streamline the analysis process.
 Label Standardization: Convert the datasets' multi-class labels to binary
(Benign/Malicious) to simplify the classification challenge.
 Data Cleaning: Identify and eliminate any records with null or infinite values to
ensure the integrity of the datasets.
 Initial Analysis: Evaluate the distribution of malicious versus benign instances
both pre- and post-cleanup to gauge data balance and cleaning impact.
Phase 2: Model Training and Evaluation
 Classifier Retrieval: Access machine learning classifier scripts designated for
each dataset year within the "Ratio Bias" folder of the provided Google Drive
link.
 Application of Classifiers: Deploy the classifiers on the preprocessed datasets
for 2017 and 2018, tailoring the models to distinguish effectively between benign
and malicious network activities.
 Evaluation Metrics: Assess the performance of each classifier, emphasizing the
accuracy, precision, recall, and F1 scores to identify the most effective models.
Phase 3: Concept Drift Adaptation
 Detection of Drift: Train models on the 2017 dataset and evaluate them on the
2018 dataset to detect any concept drift, signified by variations in model
performance metrics.
 Comparative Analysis: Analyze F1 scores under different conditions—training
and testing on the same dataset versus cross-year evaluation—to understand the
extent and impact of concept drift.
 Model Adaptation Strategies: Based on observed drift, modify the machine
learning models through approaches such as retraining with recent data or
implementing continual learning techniques to preserve or enhance model
accuracy over time.