Silent Defenders: Innovations in Network Intrusion Detection

Abstract

With the rapid growth of digital communication, ensuring the security of data transmitted over networks has become a critical concern. Intrusion Detection Systems (IDS) are pivotal in identifying and mitigating cyberattacks. This study presents an advanced IDS leveraging both machine learning and deep learning techniques, specifically, the random forest classifier and Long Short-Term Memory (LSTM) networks, for accurate detection of intrusions. The system utilizes the UNSW-NB15 dataset, undergoing preprocessing to handle missing and noisy data before classification. The proposed hybrid approach improves prediction accuracy and significantly reduces false positive rates compared to traditional methods. Additionally, the system enhances proactive security by notifying users via email in real-time upon detection of an attack, allowing timely preventative measures. Performance evaluation metrics, including accuracy, precision, recall, and F1-score, affirm the system’s robustness and practical applicability in real-world network environments.