Network Intrusion Detection Using K-Nearest Neighbors (KNN) and Recurrent Neural Networks (RNN)

In today's interconnected world, the security of computer networks is of utmost importance due to the prevalence of cyber threats. To tackle this challenge, a machine learning-based methodology for detecting network intrusions was adopted. The approach leverages two powerful algorithms: K-Nearest Neighbors (KNN) and Recurrent Neural Network (RNN). The proposed methodology involves several steps: data collection, preprocessing, feature extraction, training KNN and RNN models concurrently to capture temporal dependencies, merging model outputs to enhance accuracy, and testing the integrated model on various data types to reduce false alarms. For evaluation, a dataset simulating intrusions in a military network environment was employed. The dataset includes normal and attack instances, and preprocessing techniques were applied to prepare the data for model training. KNN's architecture involves identifying K nearest neighbors based on distance measures, while RNN captures temporal dependencies in sequential data. Confusion metrics, including true positives, true negatives, false positives, and false negatives, along with performance metrics such as accuracy, precision, sensitivity, and F1 score, were utilized to assess model performance. In conclusion, the adopted methodology utilizing KNN and RNN demonstrates strong performance in network intrusion detection. Both algorithms exhibit high accuracy, precision, and sensitivity, making them effective tools for combating cyber threats in real-world network environments.