Advanced Machine Learning Techniques for Enhancing Network Intrusion Detection and Classification Using DarkNet CIC2020

The increase of illegal endeavors and malware traffic inside the Darknet presents a detracting challenge to cybersecurity. This study deals with the problems by applying leading machine learning (ML) techniques to reduce the use of the Darknet misuse while still preserving the aura of mystery, anonymity, and privacy. Leveraging the Darknet CIC2020 dataset, the research performs binary and multiclass classification, which are based on modern algorithms, with autoencoder being one of them. Convolutional neural networks (CNN), long short-term memory (LSTM), and XGBoost were used to discriminate the complicated systems. Results indicate XGBoost performed better in cases of both binary and multi-class classifications showing tremendous reliability, accuracy, recall, and F1-score. Furthermore, the study extends its scope by introducing ensemble techniques such as voting classifier and stacking classifier, aiming to enhance predictive accuracy by joining diverse base estimators. Combining autoencoder and XGBoost, alongside investigating the CNN+LSTM architecture, enhances the model's effectiveness. These hybrid approaches are implemented so that they can affect the components of various algorithms, creating more finely grained acting metrics on a per-order basis. This approach is to analyze Darknet traffic patterns that enable us to understand the importance of Internet security measures.