Information-centric networks (ICNs) are a category of network architectures that focus on content, rather than hosts, to more effectively support the needs of today’s users. One major feature of such networks is in-network storage, which is realized by the presence of content storage routers throughout the network. These content storage routers cache popular content object chunks close to the consumers who request them in order to reduce latency for those end users and to decrease overall network congestion. Because of their prominence, network storage devices such as content storage routers will undoubtedly be major targets for malicious users. Two primary goals of attackers are to increase cache pollution and decrease hit rate by legitimate users. This would effectively reduce or eliminate the advantages of having in-network storage. Therefore, it is crucial to defend against these types of attacks. In this thesis, we study a specific ICN architecture called Named Data Networking (NDN) and simulate several attack scenarios on different network topologies to ascertain the effectiveness of different cache replacement algorithms, such as LRU and LFU (specifically, LFU-DA.) We apply our new per-face popularity with dynamic aging (PFP-DA) scheme to the content storage routers in the network and measure both cache pollution percentages as well as hit rate experienced by legitimate consumers. The current solutions in the literature that relate to reducing the effects of cache pollution largely focus on detection of attacker behavior. Since this behavior is very unpredictable, it is not guaranteed that any detection mechanisms will work well if the attackers employ smart attacks. Furthermore, current solutions do not consider the effects of a particularly aggressive attack against any single or small set of faces (interfaces.) Therefore, we have developed three related algorithms, namely PFP, PFP-DA, and Parameterized PFP-DA. PFP ensures that interests that ingress over any given face do not overwhelm the calculated popularity of a content object chunk. PFP normalizes the ranks on all faces and uses the collective contributions of these faces to determine the overall popularity, which in turn determines what content stays in the cache and what is evicted. PFP-DA adds recency to the original PFP algorithm and ensures that content object chunks do not remain in the cache longer than their true, current popularity dictates. Finally, we explore PFP-β, a parameterized version of PFP-DA, in which a β parameter is provided that causes the popularity calculations to take on Zipf-like characteristics, which in turn reduces the numeric distance between top rated items, and lower rated items, favoring items with multi-face contribution over those with single-face contributions and those with contributions over very few faces. We explore how the PFP-based schemes can reduce impact of contributions over any given face or small number of faces on an NDN content storage router. This in turn, reduces the impact that even some of the most aggressive attackers can have when they overwhelm one or a few faces, by normalizing the contributions across all contributing faces for a given content object chunk. During attack scenarios, we conclude that PFP-DA performs better than both LRU and LFU-DA in terms of resisting the effects of cache pollution and maintaining strong hit rates. We also demonstrate that PFP-DA performs better even when no attacks are being leveraged against the content store. This opens the door for further research both within and outside of ICN-based architectures as a means to enhance security and overall performance.Ph.D.College of Engineering & Computer ScienceUniversity of Michigan-Dearbornhttps://deepblue.lib.umich.edu/bitstream/2027.42/145175/1/John Baugh Final Dissertation.pdfDescription of John Baugh Final Dissertation.pdf : Dissertatio
