Network intrusion detection and prevention systems are vulnerable to evasion by attackers who craft ambiguous traffic to breach the defense of such systems. A normalizer is an inline network element that thwarts evasion attempts by removing ambiguities in network traffic. A particularly challenging step in normalization is the sound detection of inconsistent TCP retransmissions, wherein an attacker sends TCP segments with different payloads for the same sequence number space to present a network monitor with ambiguous analysis. Normalizers that buffer all unacknowledged data to verify the consistency of subsequent retransmissions consume inordinate amounts of memory on high-speed links. On the other hand, normalizers that buffer only the hashes of unacknowledged segments cannot verify the consistency of 20-30% of retransmissions that, according to our traces, do not align with the original transmissions. This paper presents the design of RoboNorm, a normalizer that buffers only the hashes of unacknowledged segments, and yet can detect all inconsistent retransmissions in any TCP byte stream. RoboNorm consumes 1-2 orders of magnitude less memory than normalizers that buffers all unacknowledged data, and is amenable to a high-speed implementation. RoboNorm is also robust to attacks that attempt to compromise its operation or exhaust its resources.