1. Network Protocol System Fingerprinting - A Formal Approach Guoqiang Shu and David Lee INFOCOM 2006 Speaker: Chang Huan Wu 2008/10/31

2. 2 Outline Introduction A Formal Model Active and Passive Fingerprinting Defending Against Malicious Fingerprinting Conclusions

3. 3 Introduction (1/3) Identifying specific features of a network protocol implementation by analyzing its input/output behavior – Facilitate management – Exploit the vulnerability of certain implementations

4. 4 Introduction (2/3) Most network protocols are not specified completely and deterministically – Optional features – Unspecified behaviors under some circumstances

5. 5 Introduction (3/3) Goal : identify which implementation it is by analyzing the input/output behaviors – Active : use some predetermined input sequences for probing the target host – Passive : observe a trace of input/output messages from the target host without disrupting its normal operations

6. 6 A Formal Model (1/4) Parameterized Extended Finite State Machine (PEFSM) is a 6-tuple M = – S : a finite set of states – S init : initial state – I = {i 0, i 1, i 2 …, i p-1 }: input alphabet, each carries a vector of parameter values – O = {o 0, o 1, o 2 …, o q-1 } : output alphabet – X : finite set of variables with default initial values

7. 7 A Formal Model (2/4) – T : finite set of transitions – For t ∈ T, t = {s, s’, i, o, P(X, i), A(X, i, o) s / s’ : start state / end state i and o : input / output symbols with parameters P : predicate of the variables and input parameters A : an operation on the variables, based on the current variable values, input and output parameter values Example of PEFSM transition

8. 8 PEFSM model of a simplified TCP Tahoe implementation (State variables, guards and actions of transition are omitted) initial state (SYN) slow start (SS) congestion avoidance (CA) retransmission (REX) finish (Fin) Transition name Input / output

9. 9 A Formal Model (3/4) Given a candidate group of implementation machines, C = {M 1, M 2 …, M k }, a test sequence seq separates M i and M j if taking seq as input, M i and M j have different output A fingerprinting set F for a candidate group C is a set of test sequences, such that for each pair of machines in C, F contains a sequence that separates them

10. 10 A Formal Model (4/4) Given a candidate group, the goal of – Active fingerprinting : construct a fingerprinting set – Passive fingerprinting : if a specific candidate generate the given trace

11. 11 Active Fingerprinting Algorithm 1 generate a sequence that separate two candidates Algorithm 2 generate the fingerprint set Partition = { {M 1, M 2, M 3, M 4 } } M 1 M 3 can be separated by T 1 Use T 1 to separate {M 1, M 2, M 3, M 4 } Partition = { {M 1, M 4 }, {M 2, M 3 } } M 1 M 4 can be separated by T 2 Use T 2 to separate {M 1, M 4 } and {M 2, M 3 } … Until all sets in Partition have only one element If T 2 separates {M 1, M 4 } and {M 2, M 3 } => Partition = { {M 1 }, {M 2 }, {M 3 }, {M 4 } } fingerprint set = {T 1, T 2 }

12. 12 Active Fingerprinting using NMAP Tests (1/3) Nmap identifies a TCP stack implementation by using nine test sequences In the fingerprint database Nmap stores the encoded response to those test sequences of more than 1300 implementations

13. 13 Active Fingerprinting using NMAP Tests (2/3) Fig.3 is PEFSM of input / output of some implementation in Nmap All inputs except T 3 could be used as separating sequence for the two machines

14. 14 Active Fingerprinting using NMAP Tests (3/3) Ex. Use {Tseq, T1, T2, T3, PU} can separate each implementation in Router category * means there is no exact fingerprint set

15. 15 Passive Fingerprinting (1/2) Using TCP Behavior Inference Tool (TBIT) to generate specific traffic Observe input and output in trace and transit, if a candidate can not transit, it means that candidate can not generate that trace

16. 16 Passive Fingerprinting (2/2) NF: NoFR T: Tahoe R: Reno NR: NewReno After the duplicated acknowledgement ACK [12] is sent four times, we see a fast retransmission without timeout

17. 17 Defending Against Malicious Fingerprinting (1/5) Scrubbing Camouflage One important principal : the modification should be transparent to all regular users

18. 18 Defending Against Malicious Fingerprinting (2/5) When receiving I 3, discard it The grey circle represents the common user sets

19. 19 Defending Against Malicious Fingerprinting (3/5) When receiving I 3, response O 4 instead O 3 The grey circle represents the union of all user sets Regular user expect the trace from any implementation

20. 20 Defending Against Malicious Fingerprinting (4/5) Neither scrubbing nor camouflage is effective The grey circle represents the T 1 user sets Regular user expect the trace from T 1 implementation

21. 21 Defending Against Malicious Fingerprinting (5/5) Follow the maximum overlapping subset until there is only one implementation possible When receiving I 3, response O 3 because it is overlapped by M 1 and M 3 The grey circle represents the union of all user sets

22. 22 Conclusion Proposed a formal approach for fingerprinting Use PEFSM to model protocol implementation Proposed algorithms for active and passive fingerprinting

23. 23 Comments General and automated method Huge database (like Nmap database) is needed How to construct PEFSM?