XGBoost-Based Detection of DDoS Attacks in Named Data Networking

Named Data Networking (NDN) is highly susceptible to Distributed Denial of Service (DDoS) attacks, such as Interest Flooding Attack (IFA) and Cache Pollution Attack (CPA). These attacks exploit the inherent data retrieval and caching mechanisms of NDN, leading to severe disruptions in data availability and network efficiency, thereby undermining the overall performance and reliability of the system. In this paper, an attack detection method based on an improved XGBoost is proposed and applied to the hybrid attack pattern of IFA and CPA. Through experiments, the performance of the new attacks and the efficacy of the detection algorithm are analyzed. In comparison with other algorithms, the proposed method is demonstrated to have advantages in terms of the advanced nature of the proposed classifier, which is confirmed by the AUC-score.