A key characteristic of commonly deployed deep packet inspection (DPI) systems is that they implement a simpli- fied state machine of the network stack that often differs from that of the end hosts. The discrepancies between the two state machines have been exploited to bypass such DPI middleboxes. However, most prior approaches to do so rely on manually crafted adversarial packets, which not only is labor-intensive but may not work well across a plurality of DPI-based middleboxes. Our goal in this work is to develop an automated way to craft such candidate packets, targeting TCP implementations in particular. Our approach to achieve this goal hinges on the key insight that while the TCP state machines of DPI implementations are obscure, those of the end hosts are well established. Thus, in our system SYMTCP, using symbolic execution, we systematically explore the TCP implementation of an end host, identifying candidate packets that can reach critical points in the code (e.g., which causes the packets to be accepted or dropped/ignored); such automatically identified packets are then fed through the DPI middlebox to determine if a discrepancy is induced and the middlebox can be bypassed. We find that our approach is extremely effective. It can generate tens of thousands of candidate adversarial packets in less than an hour. When evaluating against multiple state-of-the-art DPI middleboxes such as Zeek and Snort, as well as a state-level censorship firewall, Great Firewall of China, we identify not only previously known evasion strategies, but also novel ones that were never previously reported (e.g., involving urgent pointer). The system can extend easily to test other combinations of operating systems and DPI middleboxes, and serve as a valuable testing tool of future DPIs’ robustness against evasion attempts.
SymTCP: Eluding Stateful Deep Packet Inspection with Automated Discrepancy Discovery
Zhongjie Wang (University of California, Riverside), Shitong Zhu (University of California, Riverside), Yue Cao (University of California, Riverside), Zhiyun Qian (University of California, Riverside), Chengyu Song (University of California, Riverside), Srikanth V. Krishnamurthy (University of California, Riverside), Kevin S. Chan (U.S. Army Research Lab), Tracy D. Braun (U.S. Army Research Lab)
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