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Automated attack discovery techniques, such as attacker synthesis ormodel-based fuzzing, provide powerful ways to ensure network protocols operatecorrectly and securely. Such techniques, in general, require a formalrepresentation of the protocol, often in the form of a finite state machine(FSM). Unfortunately, many protocols are only described in English prose, andimplementing even a simple network protocol as an FSM is time-consuming andprone to subtle logical errors. Automatically extracting protocol FSMs fromdocumentation can significantly contribute to increased use of these techniquesand result in more robust and secure protocol implementations.  In this work we focus on attacker synthesis as a representative technique forprotocol security, and on RFCs as a representative format for protocol prosedescription. Unlike other works that rely on rule-based approaches or useoff-the-shelf NLP tools directly, we suggest a data-driven approach forextracting FSMs from RFC documents. Specifically, we use a hybrid approachconsisting of three key steps: (1) large-scale word-representation learning fortechnical language, (2) focused zero-shot learning for mapping protocol text toa protocol-independent information language, and (3) rule-based mapping fromprotocol-independent information to a specific protocol FSM. We show thegeneralizability of our FSM extraction by using the RFCs for six differentprotocols: BGPv4, DCCP, LTP, PPTP, SCTP and TCP. We demonstrate how automatedextraction of an FSM from an RFC can be applied to the synthesis of attacks,with TCP and DCCP as case-studies. Our approach shows that it is possible toautomate attacker synthesis against protocols by using textual specificationssuch as RFCs.

Automated Attack Synthesis by Extracting Finite State Machines from Protocol Specification Documents