On Modeling Link Flooding Attacks and Defenses
Xin Wang, Xiaobo Ma, Jiahao Peng, Jianfeng Li, Lei Xue, Wenjun Hu, Feng Li · IEEE Access · 2021
The increasing popularity of Internet of Things (IoTs) is making people universally connected and thus bringing the ease of life. Because of their sheer volume, weak security, and continual operation, IoT devices, along with many computer servers, are widely compromised to launch powerful distributed denial-of-service (DDoS) attacks. The emerging link flooding attacks (LFAs) are one type of such attacks that attract significant attention in both academia and industry against the routing infrastructure. The attack traffic flows originating from bots (e.g., compromised IoT devices) are deliberately aggregated at upstream critical links and grow intensified, gradually making a network connected to the critical links disconnected. Although LFAs are far more sophisticated than traditional DDoS attacks, whether such sophistication comes without a downside has never been investigated. In this paper, by modeling link flooding attacks and defenses, we tackle a series of questions concerning the practical issues of LFAs. Specifically, from the perspective of attacks, we advance a novel notion of strike precision, and reveal that LFAs may exhibit attack interference (i.e., unexpectedly interfere the connectivity of innocent networks) which might undermine the stealthiness and persistence of LFAs. From the perspective of defenses, we make the first step to study attack intention, i.e., inversely inferring the target network to disconnect based on the identified links under attack. Furthermore, we consider a strong defender who employs traffic engineering to mitigate LFAs, and formulate the game-theoretic interactions between attackers and defenders. Our formulation demonstrates that LFAs can be effectively mitigated based on traffic engineering from a game-theoretic perspective. We also study practical issues of non-cooperative defenses (e.g., light-weight probe deployment, multi-protocol-based measurement).