Fast Detection and Traceback-Based Mitigation of Interest Flooding Attack

作     者：Kumar, Naveen Singh, Ashutosh Kumar Srivastava, Shashank 

作者机构：Department of Computer Science and Engineering Sardar Vallabhbhai National Institute of Technology Gujarat Surat 395007 India Department of Computer Science Allahabad Degree College University of Allahabad Uttar Pradesh Prayagraj 211003 India Computer Science and Engineering Motilal Nehru National Institute of Technology Allahabad Uttar Pradesh Prayagraj 211004 India 

出 版 物：《SN Computer Science》 (SN COMPUT. SCI.)

年 卷 期：2025年第6卷第2期

页      面：1-15页

主　　题：ANN Feature selection Interest Flooding Attack Named Data Networking NDN Traceback 

摘      要：The Pending Interest Table (PIT) in Named Data Networking (NDN) plays a crucial role by storing state information of requests within the router, enabling efficient data packet routing back to the requester. However, this mechanism is vulnerable to Interest Flooding Attacks (IFA), where an attacker sends a large number of malicious requests to overwhelm the PIT, disrupting network performance. Previous research primarily focused on offline detection of IFA using selected features and machine learning techniques. In this work, we build on these findings by deploying a trained Artificial Neural Network (ANN) classifier on each NDN router for real-time, online detection of IFA. Additionally, we introduce a novel traceback-based mitigation strategy activated upon detection, significantly improving the network’s resilience against such attacks. Our proposed method demonstrates superior performance in terms of satisfaction ratio and throughput for legitimate consumers compared to existing approach. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd. 2025.