# Enhancing Cache Robustness in Information-Centric Networks: Per-Face Popularity Approaches
## 5 Results
### Varying Cache Size  
For these tests, one attacker is present for both the simple and advanced topologies, with a request frequency of 720 requests/second. Regular consumers request 120 requests/second. The reason for these frequencies was to give the attacker an overwhelming advantage as an individual item requester. For these tests in this section, only the cache size was varied, ranging from 100 items (1% of the total content space) to 500 items (5% of the total content space). The content space consists of all the items that can be cached within the domain of discourse. The top 100 and top 50, respectively, refer to the percentage of how many of the expected _actually popular_ items that should be in the cache if there was no attack being perpetrated. The number of content objects requested by an attacker is equal to the cache size, so it is possible for the attacker to attempt to completely overwhelm the cache. For example, it is clear that if all the consumers attached to a router were attackers, then the cache would be entirely filled with only attacker content. The tests in this section and subsequent sections are to test variations and determine the robustness of the cache replacement algorithms. The data in Table 3 give us an interesting picture of how the cache size (measured in the number of packets that the cache can accommodate) can affect both pollution and hit rates. The pollution percentage indicates the percentage of the cache at the router occupied by attacker content. The hit percentage (hit rate) is the percentage of legitimate consumer interests that are able to be satisfied by the content store's cache upon arrival. We also look  
Figure 2: Advanced Topology (AT) with five routers, twenty-three consumers, and a producer. at the top 100 and top 50 items (according to Zipf-like distribution) to see how stable the algorithms are at maintaining the truly popular contents in the cache. For all cache sizes, PFP-DA has both the lowest cache pollution percentages and the highest hit rates. For example, even considering the smallest cache size (100, or 1%), LFU-DA does reasonably well, with an overall hit rate of 12.67%. But this is over 6% less than PFP-DA. The percentages for the top 100 and top 50 objects are even more remarkable. LFU-DA, for example, maintains 50% to 81.03%, which is good, but PFP-DA maintains percentages from around 75% to nearly 99% for the top 50 items. This shows overall excellent performance and, in specific, good resistance to the single attacker. Now, let us consider the Advanced Topology (AT) results. Both Tables 4 and 5 show the data collected and summarized for the Advanced Topology (AT). The R1 router is the router with most of the consumers attached and with the single attacker attached as well. The routers R0, R2, R3, and R4 all demonstrate very low hit percentages and sometimes higher cache pollution. An interesting discovery is that the overall cache pollution percentage is not necessarily an indicator of the hit rate. For example, on R2, PFP-DA maintains higher cache pollution percentages than LFU-DA or LRU, but the hit rates are still better for PFP-DA than the other algorithms. This is because the cache pollution percentage does not capture information about the quality of items being cached (i.e., what rank in the Zipf distribution they have). However, in terms of hit rate, PFP-DA dominates all hit rate metrics across all routers. Again, we see the top item hit rates on R1 in the 70% to nearly 100% range for PFP-DA.