Detection and Defense of Cache Pollution Based on Popularity Prediction in Named Data Networking

Named Data Networking (NDN) is one of the most promising information-centric networking architectures that can improve the network performance by supporting the large scale content distribution. However, the use of in-network caching mechanism increases the opportunity of cache pollution attack, where the attackers intend to reduce the cache hit of legal users by releasing fake requests to fill the precious cache with non-popular contents. To prevent the degradation of network performance caused by such an attack, it is becoming particularly important to detect the attack and then throttle it. In this article, we propose a detection and defense scheme with the help of grey forecast, which can effectively exploit the regularity of past Interests and popularity by comprehensively considering three major factors to predict the future popularity of each cached content. If the predicted popularity of any content differs too much from the actually calculated one in several consecutive slices, the pollution attack will be determined. Once the attack is detected, the defense will be taken by suppressing the popularity increase of the suspicious content to mitigate the damage of the pollution attack. We also consider a special case, where there exists a sudden burst of traffic from legal users that cannot be simply dropped. The simulations in ndnSIM indicate that our proposed method is effective in detecting and defending the pollution attack with higher cache hit, higher detecting ratio, and lower hop count compared to other state-of-the-art schemes.