A Deep Learning Framework for Intelligent Network Intrusion Detection and Threat Classification
Keywords:
Conventional Neural Networks (CNNs), Intrusion Detection System (IDS), NSL-KDD, Principal component analysis (PCA), ResNet-50, ResNet-101Abstract
In many areas of application, making better decisions and using predictive analytics requires correctly classifying large, complicated datasets. Unfortunately, problems like missing values, class imbalance, redundant features, and high-dimensional data make deep learning models less useful. For better classification performance, this study suggests a complete deep learning-based system that combines advanced data preparation, dimensionality reduction, feature engineering, and residual neural network designs. Firstly, the collected dataset is preprocessed by handling missing values, encoding labels, and using SMOTE to fix class imbalance and improve data quality. Following this, Principal Component Analysis (PCA) is used to reduce the dimensionality while preserving the most valuable features. The discriminative power of the dataset is improved using feature engineering methods such as feature selection and extraction. Statistical and pattern analysis are then used to understand the basic qualities of the data. Later, the cleaned dataset is utilized to teach several deep learning models, such as ResNet34, ResNet50, and ResNet101. The optimum model for classification is determined by comparative evaluation, and the final predictions are made using the best-performing design. Results like AUC-ROC, F1-score, recall, precision, and accuracy in classification are expected to improve using the suggested framework, which is also expected to decrease processing complexity and overfitting. A quick, scalable, and dependable classification framework that can handle imbalanced and high-dimensional datasets in a variety of real-world scenarios is produced by combining robust preprocessing, enhanced feature representation, and deep residual learning.
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