  As Deep Packet Inspection (DPI) middleboxes become increasingly popular, a
spectrum of adversarial attacks have emerged with the goal of evading such
middleboxes. Many of these attacks exploit discrepancies between the middlebox
network protocol implementations, and the more rigorous/complete versions
implemented at end hosts. These evasion attacks largely involve subtle
manipulations of packets to cause different behaviours at DPI and end hosts, to
cloak malicious network traffic that is otherwise detectable. With recent
automated discovery, it has become prohibitively challenging to manually curate
rules for detecting these manipulations. In this work, we propose CLAP, the
first fully-automated, unsupervised ML solution to accurately detect and
localize DPI evasion attacks. By learning what we call the packet context,
which essentially captures inter-relationships across both (1) different
packets in a connection; and (2) different header fields within each packet,
from benign traffic traces only, CLAP can detect and pinpoint packets that
violate the benign packet contexts (which are the ones that are specially
crafted for evasion purposes). Our evaluations with 73 state-of-the-art DPI
evasion attacks show that CLAP achieves an Area Under the Receiver Operating
Characteristic Curve (AUC-ROC) of 0.963, an Equal Error Rate (EER) of only
0.061 in detection, and an accuracy of 94.6% in localization. These results
suggest that CLAP can be a promising tool for thwarting DPI evasion attacks.
