Network Anomaly Detector
The Network Anomaly Detector is a cutting-edge solution designed to safeguard networks by identifying and mitigating potential threats in real time. Leveraging the CIC-IDS-19 dataset and powered by the XGBoost algorithm, our system achieves an impressive 99.7% accuracy in detecting anomalies. Integrated with CICFlowMeter, it allows users to monitor their network traffic and protect their infrastructure with precision and efficiency.
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01
Data Collection and Pre-processing
The network anomaly detector leverages the CIC-IDS-19 dataset, which encompasses 10 days of network traffic data and 16 million instances. The dataset underwent rigorous cleaning to address issues such as noise and missing values. Additionally, class imbalance was a significant challenge, which we mitigated using techniques like oversampling, undersampling, and SMOTE (Synthetic Minority Over-sampling Technique). This thorough pre-processing ensures the dataset is primed for effective model training, ultimately enhancing the reliability and performance of the anomaly detection system
02
Feature Extraction and Selection
Effective feature extraction and selection are critical to the success of the network anomaly detector. We employed a comprehensive feature extraction process to identify the most relevant attributes from the network traffic data. By using statistical methods and domain knowledge, we were able to distill the dataset down to the most significant features that influence anomaly detection. This step not only reduced the dimensionality of the data but also enhanced the model's efficiency and accuracy, ensuring that the system can quickly and accurately identify anomalies in network traffic.
03
Model Training and Optimization
The core of our anomaly detection system is built on the XGBoost algorithm, renowned for its performance and speed in classification tasks. We meticulously trained the model on the pre-processed and feature-optimized dataset, achieving an impressive accuracy of 99.7% on testing. Optimization techniques, such as hyperparameter tuning and cross-validation, were employed to fine-tune the model's performance. This high level of accuracy signifies the model's capability to detect known network anomalies effectively, making it a robust tool for network security.
By focusing on these key areas, the network anomaly detector project showcases a comprehensive approach to addressing network security challenges through data-driven techniques and advanced machine learning algorithms.
04
Real time Integration
To facilitate real-time anomaly detection, we integrated the CICFlowMeter, a tool for extracting network traffic features. This integration allows users to input their network traffic data and utilize our trained model to detect anomalies on the fly. The seamless integration ensures that users can leverage the power of our anomaly detection system in their own networks, providing a practical and immediate solution to network security threats. This real-time capability, combined with the model's high accuracy, makes our network anomaly detector a valuable asset for any organization.