Towards Persistent Detection of DDoS Attacks in NDN: A Sketch-Based Approach

Abstract

As a promising architectural design for future Internet, Named Data Networking (NDN) relies on data names, instead of destination IP addresses, to deliver data. NDN supports data authenticity and integrity by making public key signatures mandatory on data content and data names. This handles the primary security concern in NDN, but is still vulnerable to new DDoS attacks, including Cache Pollution attacks and Interest Flooding attacks, which degrade NDN transmission significantly, by violating the crucial components of NDN routers. To defend against DDoS attacks in NDN, the most effective way is to persistently detect the malicious traffic and then throttle them. Except for the usual concern of the accuracy and efficiency in attack detection, since these attacks themselves have already imposed a huge burden on victims, to avoid exhausting the remaining resources on the victims for detection purpose, a lightweight detection solution is highly desired. We study DDoS attacks and propose a persistent detection solution based on an observed malicious traffic pattern, which leverages a novel sketch to monitor the malicious traffic in a timely and lightweight way. Additionally, our analysis and experiments demonstrate that, with fixed low resource consumption, the proposed solution can persistently detect DDoS attacks in NDN.