LAUSR

Computer networks have touched every aspect of human life, it cannot be overstated that cyber security is of great importance and significance. Intrusion detection techniques play an important role in the field of network security, but it also faces significant challenges. In this paper, we propose a Hybrid Firefly and Black Hole Algorithm (HFBHA) for parameter tuning of the XGBoost model and apply it to the study of intrusion detection systems. Firstly, the algorithm designs a double black hole mechanism by introducing the concept of the second black hole and adjusting the moving trajectory of the stars using the attraction of both black holes. Secondly, an improved initialization method of the stars is proposed, where a star that crosses the event horizon of the black hole has an opportunity to be replaced by a new star around the black hole. Finally, a combination of the firefly perturbation strategy and mutation operator is proposed to improve the global search capability of the algorithm. Both the effectiveness of the proposed method on the XBGoost parameter tuning problem and the feasibility of this strategy on intrusion detection applications are verified by comparison experiments based on the NSL-KDD dataset.