riyasai22 / intrusion-detection-and-attack-classification

Intrusion-Detection-and-Attack-Classification

The Intrusion Detection and Attack Classification system is a security solution designed to detect and classify unauthorized activities or attacks within a computer network. It employs various machine learning algorithms, including Support Vector Machines (SVM), Random Forest, Linear Regression, and Artificial Neural Networks (ANN). By leveraging machine learning techniques, the system can continuously analyze network traffic, detect suspicious activities, and accurately classify them as potential intrusions or attacks. This enables proactive monitoring, timely response, and effective mitigation of security incidents, enhancing the overall security posture of the network infrastructure.

The KDD99 dataset, was employed to train a model using a combination of traditional machine learning algorithms, ensemble methods, and deep learning techniques. Various preprocessing techniques, such as label encoding, scaling, dimensionality reduction, and upsampling, were applied to optimize the data before training.

Label encoding was utilized to convert categorical variables into numerical representations suitable for the algorithms. Scaling techniques were applied to ensure that features were on a similar scale, facilitating accurate model training. Dimensionality reduction techniques, such as Principal Component Analysis (PCA) or feature selection, were employed to reduce the number of features and enhance computational efficiency without compromising performance. Upsampling techniques were utilized to address class imbalance issues by increasing the representation of minority classes in the dataset.

Performance evaluation of the trained models was conducted using metrics like recall, precision, and F1 score, which provide insights into the model's ability to correctly classify intrusions and non-intrusions. The achieved accuracy score of 97% demonstrates the effectiveness of the trained models in accurately identifying and classifying instances of intrusions.

The combination of traditional, ensemble ML, and deep learning algorithms, along with appropriate preprocessing techniques, allowed for the creation of a robust and accurate intrusion detection system. By achieving a high accuracy score and leveraging performance metrics like recall, precision, and F1 score, the trained models provide a solid foundation for detecting and mitigating potential security breaches within the network environment.