False-Locality Attack Detection Using CNN in Named Data Networking
Yujie Zeng, Guowei Wu, Rui Wang, Mohammad S. Obaidat, Kuei-Fang Hsiao · 2019
Named data networking(NDN) is a very promising architecture for future network, which can improve the network performance due to its in-network caching feature. However, the pervasive caching is vulnerable against False-Locality Attack (FLA), one kind of cache pollution attack, where attackers repeatedly request a specific set of non-popular contents to replace popular contents. Therefore, the cache hit of legal requests is reduced and the response delay is increased. To mitigate this attack and improve the network performance, we propose a detection scheme based on Convolutional Neural Network (CNN) by fully exploiting the regularity of past requests. The input data of CNN are related to the inherent characteristics of the cached contents including the request ratio, the standard deviation of repeated Interests, the variance of request interval and the change of cache hit ratio. The output of CNN indicates whether FLA has been launched. Simulations through multi-topologies are conducted to validate the performance of our scheme. Compared with other state-of-the-art schemes, it is more effective in detecting FLA with higher detecting ratio, higher cache hit and lower hop count.