BOA-ACRF: An intrusion detection method for data imbalance problems

Abstract

As the cybersecurity landscape continues to evolve, intrusion detection systems (IDS), a critical component of defense frameworks, face unprecedented challenges. One of the primary factors contributing to the decline in intrusion detection performance is the issue of class imbalance within datasets. To address this challenge, this paper proposes an intrusion detection method designed specifically for the class imbalance problem, named BOA-ACRF. This method first improves the traditional Auxiliary Classifier Generative Adversarial Network (ACGAN) to enhance its capability in generating numerical data for specific traffic categories. Furthermore, the Bayesian Optimization Algorithm (BOA) is employed to automatically identify optimal model parameters. This approach not only effectively resolves the sensitivity of ACGAN to hyperparameters but also improves the generalization capability and detection performance of the Random Forest (RF) model. The effectiveness of BOA-ACRF is validated on three intrusion detection datasets: CIC-IDS-2017, CIC-UNSW-NB15 and NSL-KDD. Experimental results show that the proposed method achieves outstanding performance in accuracy, precision, recall, and F1-score, significantly surpassing current mainstream approaches. This work provides an effective framework and technical solution to address the class imbalance problem in the field of intrusion detection.