Attention-Based LSTM Model for IFA Detection in Named Data Networking

Abstract

As one of the next generation networks, Named Data Networking (NDN) performs well on content distribution. However, it is vulnerable against a new type of denial-of-service (DoS) attacks, interest flooding attacks (IFAs), one of the fatal threats to NDN. The attackers request nonexist content to occupy the Pending Interest Table (PIT), and it causes the degradation of network performance. Because of the great harm and strong concealment of this attack, it is urgent to detect and throttle the attack. This paper proposes a detection mechanism based on Long Short-Term Memory (LSTM) with attention mechanism, which uses sequence with different treatments. Once IFA is detected, the Hellinger distance is used to recognize malicious Interest prefix. The simulation results show that the proposed scheme can resist IFA effectively compared to state-of-the-art schemes.