ADASYN-Random Forest Based Intrusion Detection Model
This addresses intrusion detection in cybersecurity, but it is incremental as it combines existing methods (ADASYN and Random Forest) on a standard dataset.
The paper tackled the problem of imbalanced intrusion detection datasets by proposing an ADASYN-Random Forest model, which improved classification performance on the CICIDS 2017 dataset with enhanced precision, recall, F1 scores, and AUC values.
Intrusion detection has been a key topic in the field of cyber security, and the common network threats nowadays have the characteristics of varieties and variation. Considering the serious imbalance of intrusion detection datasets will result in low classification performance on attack behaviors of small sample size and difficulty to detect network attacks accurately and efficiently, using Adaptive Synthetic Sampling (ADASYN) method to balance datasets was proposed in this paper. In addition, Random Forest algorithm was used to train intrusion detection classifiers. Through the comparative experiment of Intrusion detection on CICIDS 2017 dataset, it is found that ADASYN with Random Forest performs better. Based on the experimental results, the improvement of precision, recall, F1 scores and AUC values after ADASYN is then analyzed. Experiments show that the proposed method can be applied to intrusion detection with large data, and can effectively improve the classification accuracy of network attack behaviors. Compared with traditional machine learning models, it has better performance, generalization ability and robustness.