In this paper, we consider the problem of approximate membership testing problem on skewed network traffic traces, in which some hot or popular items repeat frequently. Previous solutions suffer from either high false positive rates or low lookup throughput. To address this problem, we propose a variant of the cuckoo filter, enhanced with a hotness-aware suffix cache. We note that a false positive item must have a matched fingerprint in the cuckoo filter, and propose to reduce false positives by memorizing them, but with their suffixes only. For each false positive item, we apply a linear-congruential-based hash function and then divide the hash value into three parts: the bucket index to be accessed in the cuckoo filter, the fingerprint to be stored in the cuckoo filter, and the suffix to be cached. Combining the three parts, we propose RCF that can uniquely identify a hot false positive item, which thus reduces hot false positives. Our evaluation results indicate that RCF significantly outperforms non-adaptive filters on skewed data traces. Given the same memory size, it achieves a much lower false positive ratio without sacrificing its lookup throughput. Compared with adaptive filters, RCF provides a competitive false positive ratio while offering a considerably higher lookup throughput.