Enhanced IDS Using BBA and SMOTE-ENN for Imbalanced Data for Cybersecurity

The growing importance of the internet has intensified the risks to network systems, necessitating enhanced security measures. Intrusion Detection Systems (IDS) play a crucial role in protecting against hostile activity, ensuring the integrity of data, and maintaining business continuity. By utilizing machine learning, contemporary intrusion detection systems (IDS) provide instantaneous monitoring and efficient avoidance of potential threats. This article presents an IDS that utilizes a machine learning framework to specifically identify attacks in the UNSW-NB15 and NSL-KDD datasets. Data preprocessing involves handling missing values and applying standard scaler normalization to achieve uniform feature scaling. The Binary Bat Algorithm (BBA) improves model efficacy through feature extraction. By creating artificial samples, SMOTE-ENN addresses class imbalances and enhances the model’s ability to make accurate predictions across different classes. The attacks are classified using a Random Forest (RF) model, which achieves an accuracy of 97.3% while reducing the time required for training. The results highlight the exceptional performance of our IDS in comparison to conventional approaches, providing a potential option for comprehensive intrusion detection in network systems.