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<p>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.</p>

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Fast Detection and Traceback-Based Mitigation of Interest Flooding Attack

  • 期刊论文

  • 发表日期：

    2025年1月24日

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  • Naveen Kumar,
  • Ashutosh Kumar Singh,
  • Shashank Srivastava

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.