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List of (automatic) protocol reverse engineering tools for network protocols

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techge/PRE-list

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List of (automatic) protocol reverse engineering tools/methods/approaches for network protocols

This is a collection of 71 scientific papers about (automatic) protocol reverse engineering (PRE) methods and tools. The papers are categorized into different groups so that it is more easy to get an overview of existing solutions based on the problem you want to tackle.

The collection is based on the following three surveys and got extended afterwards:

  • J. Narayan, S. K. Shukla, and T. C. Clancy, “A Survey of Automatic Protocol Reverse Engineering Tools,” ACM Computing Surveys, vol. 48, no. 3, pp. 1–26, Feb. 2016, doi: 10.1145/2840724.PDF
  • J. Duchêne, C. Le Guernic, E. Alata, V. Nicomette, and M. Kaâniche, “State of the art of network protocol reverse engineering tools,” Journal of Computer Virology and Hacking Techniques, vol. 14, no. 1, pp. 53–68, Feb. 2018, doi: 10.1007/s11416-016-0289-8.PDF
  • B. D. Sija, Y.-H. Goo, K.-S. Shim, H. Hasanova, and M.-S. Kim, “A Survey of Automatic Protocol Reverse Engineering Approaches, Methods, and Tools on the Inputs and Outputs View,” Security and Communication Networks, vol. 2018, pp. 1–17, 2018, doi: 10.1155/2018/8370341.PDF

Furthermore, there is a very extensive surveys which focuses on the methods and approaches of PRE tools that are based on network traces. The work of Kleber et al. is an excellent starting point to see what was already tried and for which use cases a method is working best.

  • S. Kleber, L. Maile, and F. Kargl, “Survey of Protocol Reverse Engineering Algorithms: Decomposition of Tools for Static Traffic Analysis,” IEEE Communications Surveys & Tutorials, vol. 21, no. 1, pp. 526–561, 2019, doi: 10.1109/COMST.2018.2867544.PDF

Please help extending this collection by adding papers to thetools.ods.

Table of Contents

Overview

NameYearApproach used
PIP[1]2004Keyword detection and Sequence alignment based on Needleman and Wunsch 1970 and Smith and Waterman 1981; this approach was applied and extended by many following papers
GAPA[2]2005Protocol analyzer and open language that uses the protocol analyzer specification Spec → it is meant to be integrated in monitoring and analyzing tools
ScriptGen[3]2005Grouping and clustering messages, find edges from clusters to clusters for being able to replay messages once a similar message arrives
RolePlayer[4]2006Byte-wise sequence alignment (find variable fields in messages) and clustering with FSM simplification
Ma et al.[5]2006Please review
FFE/x86[6]2006Please review
Replayer[7]2006Please review
Discoverer[8]2007Tokenization of messages, recursive clustering to find formats, merge similar formats
Polyglot[9]2007Dynamic taint-analysis
PEXT[10]2007Message clustering for creating FSM graph and simplify FSM graph
Rosetta[11]2007Please review
AutoFormat[12]2008Dynamic taint-analysis
Tupni[13]2008Dynamic taint-analysis; look for loops to identify boundaries within messages
Boosting[14]2008Please review
ConfigRE[15]2008Please review
ReFormat[16]2009Dynamic taint-analysis, especially targeting encrypted protocols by looking for bitwise and arithmetic operations
Prospex[17]2009Dynamic taint-analysis with following message clustering, optionally provides fuzzing candidates for Peach fuzzer
Xiao et al.[18]2009Please review
Trifilo et al.[19]2009Measure byte-wise variances in aligned messages
Antunes and Neves[20]2009Please review
Dispatcher[21]2009Dynamic taint-analysis (successor of Polyglot using send instead of received messages)
Fuzzgrind[22]2009Please review
REWARDS[23]2010Please review
MACE[24]2010Please review
Whalen et al.[25]2010Please review
AutoFuzz[26]2010Please review
ReverX[27]2011Speech recognition (thus only for text-based protocols) to find carriage returns and spaces, afterwards looking for frequencies of keywords; multiple partial FSMs are merged and simplified to get PFSM
Veritas[28]2011Identifiying keywords, clustering and transition probability → probabilistic protocol state machine
Biprominer[29]2011Statistical analysis including three phases, learning phase, labeling phase and transition probability model building phase. Seethis figure.
ASAP[30]2011Please review
Howard[31]2011Please review
ProDecoder[32]2012Successor of Biprominer which also addresses text-based protocols; two-phases are used: first apply Biprominer, second use Needleman-Wunsch for alignment
Zhang et al.[33]2012Please review
Netzob[34]2012Seethis figure
PRISMA[35]2012Please review, follow-up paper/project to ASAP
ARTISTE[36]2012Please review
Wang et al.[37]2013Capturing of data, identifying frames and inferring the format by looking and frequency of frames and doing association analysis (using Apriori and FP-Growth).
Laroche et al.[38]2013Please review
AutoReEngine[39]2013Apriori Algorithm (based on Agrawal/Srikant 1994). Identify fields and keywords by considering the amount of occurrences. Message formats are considered as series of keywords. State machines are derived from labeled messages or frequent subsequences. Seethis figure for clarification.
Dispatcher2[40]2013Please review
ProVeX[41]2013Identify Botnet traffic and try to infer the botnet type by using signatures
Meng et al.[42]2014Please review
AFL[43]2014Please review
Proword[44]2014Please review
ProGraph[45]2015Please review
FieldHunter[46]2015Please review
RS Cluster[47]2015Please review
UPCSS[48]2015Please review
ARGOS[49]2015Please review
PULSAR[50]2015Reverse engineer network protocols with the aim to fuzz them with thus knowledge
Li et al.[51]2015Please review
Cai et al.[52]2016Please review
WASp[53]2016Pcap files are provided with context information (i.e. known MAC address), then grouping and analysing (looking for CRC, N-gram, Entropy, Features, Ranges), afterwards report creation based on scoring.
PRE-Bin[54]2016Please review
Xiao et al.[55]2016Please review
PowerShell[56]2017Please review
ProPrint[57]2017Please review
ProHacker[58]2017Please review
Esoul and Walkinshaw[59]2017Please review
PREUGI[60]2017Please review
NEMESYS[61]2018Please review
Goo et al.[62]2019Apriori based: Finding „frequent contiguous common subsequences“ via new Contiguous Sequential Pattern (CSP) algorithm which is based on Generalized Sequential Pattern (GSP) and other Apriori algorithms. CSP is used three times hierarchically to extract different information/fields based on previous results.
Universal Radio Hacker[63]2019Physical layer based analysis of proprietary wireless protocols considering wireless specific properties like Received Signal Strength Indicator (RSSI) and using statistical methods
Luo et al.[64]2019From abstract: “[…] this study proposes a type-aware approach to message clustering guided by type information. The approach regards a message as a combination of n-grams, and it employs the Latent Dirichlet Allocation (LDA) model to characterize messages with types and n-grams via inferring the type distribution of each message.”
Sun et al.[65]2019Please review
Yang et al.[66]2020Using deep-learning (LSTM-FCN) for reversing binary protocols
Sun et al.[67]2020"To measure format similarity of unknown protocol messages in a proper granularity, we propose relative measurements, Token Format Distance (TFD) and Message Format Distance (MFD), based on core rules of Augmented Backus-Naur Form (ABND)." for clustering process Silhouette Coefficient and Dunn Index are used. density based cluster algorithm DBSCAN is used for clustering of messages
Shim et al.[68]2020Follow up on Goo et al. 2019
IPART[69]2020Using extended voting expert algorithm to infer boundaries of fields, otherwise using three phase which are tokenizing, classifying and clustering.
NEMETYL[70]2020Please review
NetPlier[71]2021Probabilistic method for network trace based protocol reverse engineering.

Input and Output

NetT: input is a network trace (e.g. pcap)
ExeT: input is an execution trace (code/binary at hand)
PF: output is protocol format (describing the syntax)
PFSM: output is protocol finite state machine (describing semantic/sequential logic)

NameYearNetTExeTPFPFSMOther Output
PIP[1]2004Keywords/ fields
GAPA[2]2005
ScriptGen[3]2005Dialogs/scripts (for replaying)
RolePlayer[4]2006Dialogs/scripts
Ma et al.[5]2006App-identification
FFE/x86[6]2006
Replayer[7]2006
Discoverer[8]2007
Polyglot[9]2007
PEXT[10]2007
Rosetta[11]2007
AutoFormat[12]2008
Tupni[13]2008
Boosting[14]2008Field(s)
ConfigRE[15]2008
ReFormat[16]2009
Prospex[17]2009
Xiao et al.[18]2009
Trifilo et al.[19]2009
Antunes and Neves[20]2009
Dispatcher[21]2009C&C malware
Fuzzgrind[22]2009
REWARDS[23]2010
MACE[24]2010
Whalen et al.[25]2010
AutoFuzz[26]2010
ReverX[27]2011
Veritas[28]2011
Biprominer[29]2011
ASAP[30]2011Semantics
Howard[31]2011
ProDecoder[32]2012
Zhang et al.[33]2012
Netzob[34]2012
PRISMA[35]2012
ARTISTE[36]2012
Wang et al.[37]2013
Laroche et al.[38]2013
AutoReEngine[39]2013
Dispatcher2[40]2013C&C malware
ProVeX[41]2013Signatures
Meng et al.[42]2014
AFL[43]2014
Proword[44]2014
ProGraph[45]2015
FieldHunter[46]2015Fields
RS Cluster[47]2015Grouped-messages
UPCSS[48]2015Proto-classification
ARGOS[49]2015
PULSAR[50]2015
Li et al.[51]2015
Cai et al.[52]2016
WASp[53]2016scored analysis reports, spoofing candidates
PRE-Bin[54]2016
Xiao et al.[55]2016
PowerShell[56]2017Dialogs/scripts
ProPrint[57]2017Fingerprints
ProHacker[58]2017Keywords
Esoul and Walkinshaw[59]2017
PREUGI[60]2017
NEMESYS[61]2018
Goo et al.[62]2019
Universal Radio Hacker[63]2019
Luo et al.[64]2019
Sun et al.[65]2019
Yang et al.[66]2020
Sun et al.[67]2020
Shim et al.[68]2020
IPART[69]2020
NEMETYL[70]2020
NetPlier[71]2021

Tested protocols

NameYearText-basedBinary-basedHybridOther Protocols
PIP[1]2004HTTP
GAPA[2]2005HTTP
ScriptGen[3]2005HTTPNetBIOSDCE
RolePlayer[4]2006HTTP, FTP, SMTP, NFS, TFTPDNS, BitTorrent, QQ, NetBiosSMB, CIFS
Ma et al.[5]2006HTTP, FTP, SMTP, HTTPS (TCP-Protos)DNS, NetBIOS, SrvLoc (UDP-Protos)
FFE/x86[6]2006
Replayer[7]2006
Discoverer[8]2007HTTPRPCSMB, CIFS
Polyglot[9]2007HTTP, Samba, ICQDNS, IRC
PEXT[10]2007FTP
Rosetta[11]2007
AutoFormat[12]2008HTTP, SIPDHCP, RIP, OSPFSMB, CIFS
Tupni[13]2008HTTP, FTPRPC, DNS, TFTPWMF, BMP, JPG, PNG, TIF
Boosting[14]2008DNS
ConfigRE[15]2008
ReFormat[16]2009HTTP, MIMEIRCOne unknown protocol
Prospex[17]2009SMTP, SIPSMBAgobot (C&C)
Xiao et al.[18]2009HTTP, FTP, SMTP
Trifilo et al.[19]2009TCP, DHCP, ARP, KAD
Antunes and Neves[20]2009FTP
Dispatcher[21]2009HTTP, FTP, ICQDNS
Fuzzgrind[22]2009
REWARDS[23]2010
MACE[24]2010
Whalen et al.[25]2010
AutoFuzz[26]2010
ReverX[27]2011FTP
Veritas[28]2011SMTPPPLIVE, XUNLEI
Biprominer[29]2011XUNLEI, QQLive, SopCast
ASAP[30]2011HTTP, FTP, IRC, TFTP
Howard[31]2011
ProDecoder[32]2012SMTP, SIPSMB
Zhang et al.[33]2012HTTP, SNMP, ISAKMP
Netzob[34]2012FTP, SambaSMBUnknown P2P & VoIP protocol
PRISMA[35]2012
ARTISTE[36]2012
Wang et al.[37]2013ICMPARP
Laroche et al.[38]2013FTPDHCP
AutoReEngine[39]2013HTTP, FTP, SMTP, POP3DNS, NetBIOS
Dispatcher2[40]2013HTTP, FTP, ICQDNSSMB
ProVeX[41]2013HTTP, SMTP, IMAPDNS, VoIP, XMPPMalware Family Protocols
Meng et al.[42]2014TCP, ARP
AFL[43]2014
Proword[44]2014
ProGraph[45]2015HTTPDNS, BitTorrent, WeChat
FieldHunter[46]2015MSNPDNSSopCast, Ramnit
RS Cluster[47]2015FTP, SMTP, POP3, HTTPSDNS, XunLei, BitTorrent, BitSpirit, QQ, eMuleMSSQL, Kugoo, PPTV
UPCSS[48]2015HTTP, FTP, SMTP, POP3, IMAPDNS, SSL, SSHSMB
ARGOS[49]2015
PULSAR[50]2015
Li et al.[51]2015
Cai et al.[52]2016HTTP, SSDPDNS, BitTorrent, QQ, NetBios
WASp[53]2016IEEE 802.15.4 proprietary protocols, Smart plug & PSD systems
PRE-Bin[54]2016
Xiao et al.[55]2016
PowerShell[56]2017ARP, OSPF, DHCP, STPCDP/DTP/VTP, HSRP, LLDP, LLMNR, mDNS, NBNS, VRRP
ProPrint[57]2017
ProHacker[58]2017
Esoul and Walkinshaw[59]2017
PREUGI[60]2017
NEMESYS[61]2018
Goo et al.[62]2019HTTPDNS
Universal Radio Hacker[63]2019proprietary wireless protocols of IoT devices
Luo et al.[64]2019
Sun et al.[65]2019
Yang et al.[66]2020IPv4, TCP
Sun et al.[67]2020
Shim et al.[68]2020FTPModbus/TCP, Ethernet/IP
IPART[69]2020Modbus, IEC104, Ethernet/IP
NEMETYL[70]2020
NetPlier[71]2021

Source Code

Most papers do not provide the code used in the research. For the following papers exists (example) code.

NameYearSource Code
PIP[1]2004https://web.archive.org/web/20090416234849/http://4tphi.net/~awalters/PI/PI.html
ReverX[27]2011https://github.com/jasantunes/reverx
Netzob[34]2012https://github.com/netzob/netzob
PRISMA[35]2012https://github.com/tammok/PRISMA/
PULSAR[50]2015https://github.com/hgascon/pulsar
NEMESYS[61]2018https://github.com/vs-uulm/nemesys
Universal Radio Hacker[63]2019https://github.com/jopohl/urh
NetPlier[71]2021https://github.com/netplier-tool/NetPlier/

References

[1]

M. Beddoe, “The protocol informatics project,” 2004,http://www.4tphi.net/∼awalters/PI/PI.html.PDF

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C. Leita, K. Mermoud, and M. Dacier, “ScriptGen: an automated script generation tool for Honeyd,” in Proceedings of the 21st Annual Computer Security Applications Conference (ACSAC ’05), pp. 203–214, Tucson, Ariz, USA, December 2005.PDF

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W. Cui, V. Paxson, N. C. Weaver, and R. H. Katz, “Protocolindependent adaptive replay of application dialog,” in Proceedings of the 13th Symposium on Network and Distributed System Security (NDSS ’06), 2006.PDF

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Lim, J., Reps, T., Liblit, B.: Extracting output formats from executables. In: 13th Working Conference on Reverse Engineering, 2006. WCRE ’06, pp. 167–178. IEEE, Benevento (2006). doi:10.1109/WCRE.2006.29PDF

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W. Cui, J. Kannan, and H. J. Wang, “Discoverer: Automatic protocol reverse engineering from network traces.,” in USENIX security symposium, 2007, pp. 1–14.PDF

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J. Caballero, H. Yin, Z. Liang, and D. Song, “Polyglot: automatic extraction of protocol message format using dynamic binary analysis,” in Proceedings of the 14th ACM Conference on Computer and Communications Security (CCS ’07), pp. 317–329, ACM, November 2007.PDF

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M. Shevertalov and S. Mancoridis, “A reverse engineering tool for extracting protocols of networked applications,” in Proceedings of the 14th Working Conference on Reverse Engineering (WCRE ’07), pp. 229–238, October 2007.PDF

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Caballero, J., Song, D.: Rosetta: Extracting Protocol Semantics Using Binary Analysis with Applications to Protocol Replay and NAT Rewriting. Technical Report CMU-CyLab-07-014, Carnegie Mellon University, Pittsburgh (2007)

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Z. Lin, X. Jiang, D. Xu, and X. Zhang, “Automatic protocol format reverse engineering through context-aware monitored execution,” in Proceedings of the 15th Symposium on Network and Distributed System Security (NDSS ’08), February 2008.PDF

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W. Cui, M. Peinado, K. Chen, H. J. Wang, and L. Irun-Briz, “Tupni: automatic reverse engineering of input formats,” in Proceedings of the 15th ACM Conference on Computer and Communications Security (CCS ’08), pp. 391–402, ACM, Alexandria, Va, USA, October 2008.PDF

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K. Gopalratnam, S. Basu, J. Dunagan, and H. J. Wang, “Automatically extracting fields from unknown network protocols,” in Proceedings of the 15th Symposium on Network and Distributed System Security (NDSS ’08), 2008.PDF

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Wang, R., Wang, X., Zhang, K., Li, Z.: Towards automatic reverse engineering of software security configurations. In: Proceedings of the 15th ACM Conference on Computer and Communications Security, CCS ’08, pp. 245–256. ACM, Limerick (2008). doi:10.1145/1455770.1455802

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Z. Wang, X. Jiang, W. Cui, X. Wang, and M. Grace, “ReFormat: automatic reverse engineering of encrypted messages,” in Computer Security—ESORICS 2009. ESORICS 2009, M. Backes and P. Ning, Eds., vol. 5789 of Lecture Notes in Computer Science, pp. 200–215, Springer, Berlin, Germany, 2009.PDF

[17]

P. M. Comparetti, G. Wondracek, C. Kruegel, and E. Kirda, “Prospex: protocol specification extraction,” in Proceedings of the 30th IEEE Symposium on Security and Privacy, pp. 110–125, Berkeley, Calif, USA, May 2009.PDF

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M.-M. Xiao, S.-Z. Yu, and Y. Wang, “Automatic network protocol automaton extraction,” in Proceedings of the 3rd International Conference on Network and System Security (NSS ’09), pp. 336–343, October 2009.

[19]

A. Trifilo, S. Burschka, and E. Biersack, “Traffic to protocol reverse engineering,” in Proceedings of the IEEE Symposium on Computational Intelligence for Security and Defense Applications, pp. 1–8, July 2009.PDF

[20]

J. Antunes and N. Neves, “Building an automaton towards reverse protocol engineering,” 2009,http://www.di.fc.ul.pt/∼nuno/PAPERS/INFORUM09.pdf.

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J. Caballero, P. Poosankam, C. Kreibich, and D. Song, “Dispatcher: enabling active botnet infiltration using automatic protocol reverse-engineering,” in Proceedings of the 16th ACM Conference on Computer and Communications Security (CCS ’09), pp. 621–634, ACM, Chicago, Ill, USA, November 2009.PDF

[22]

Campana, G.: Fuzzgrind: an automatic fuzzing tool. In: Hack. lu. Hack. lu, Luxembourg (2009)

[23]

Lin, Z., Zhang, X., Xu, D.: Automatic reverse engineering of data structures from binary execution. In: Proceedings of the 17th Annual Network and Distributed System Security Symposium (NDSS). Internet Society, San Diego (2010)

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Cho, C.Y., Babi D., Shin, E.C.R., Song, D.: Inference and analysis of formal models of botnet command and control protocols. In: Proceedings of the 17th ACM Conference on Computer and Communications Security, CCS ’10, pp. 426–439. ACM, New York, NY (2010). doi:10.1145/1866307.1866355Cho, C.Y., Babi, D., Poosankam, P., Chen, K.Z., Wu, E.X., Song, D.: MACE: model-inference-assisted concolic exploration for protocol and vulnerability discovery. In: Proceedings of the 20th USENIX Conference on Security, SEC’11, p. 19. USENIX Association, Berkeley, CA (2011)

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S. Whalen, M. Bishop, and J. P. Crutchfield, “Hidden Markov Models for Automated Protocol Learning,” in Security and Privacy in Communication Networks, vol. 50, S. Jajodia and J. Zhou, Eds. Berlin, Heidelberg: Springer Berlin Heidelberg, 2010, pp. 415–428.PDF

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S. Gorbunov and A. Rosenbloom, “Autofuzz: Automated network protocol fuzzing framework,” IJCSNS, vol. 10, no. 8, p. 239, 2010.PDF

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J. Antunes, N. Neves, and P. Verissimo, “Reverse engineering of protocols from network traces,” in Proceedings of the 18th Working Conference on Reverse Engineering (WCRE ’11), pp. 169–178, October 2011.PDF

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I. Bermudez, A. Tongaonkar, M. Iliofotou, M. Mellia, and M. M. Munafo, “Automatic protocol field inference for deeper protocol understanding,” in Proceedings of the 14th IFIP Networking Conference (Networking ’15), pp. 1–9, May 2015.PDF

[47]

J.-Z. Luo, S.-Z. Yu, and J. Cai, “Capturing uncertainty information and categorical characteristics for network payload grouping in protocol reverse engineering,” Mathematical Problems in Engineering, vol. 2015, Article ID 962974, 9 pages, 2015.

[48]

R. Lin, O. Li, Q. Li, and Y. Liu, “Unknown network protocol classification method based on semi supervised learning,” in Proceedings of the IEEE International Conference on Computer and Communications (ICCC ’15), pp. 300–308, Chengdu, China, October 2015.

[49]

Zeng, J., Lin, Z.: Towards automatic inference of kernel object semantics from binary code. In: 18th International Symposium, RAID 2015, vol. 9404, pp. 538–561. Springer, Kyoto (2015). doi:10.1007/978-3-319-26362-5

[50]

H. Gascon, C. Wressnegger, F. Yamaguchi, D. Arp, and K. Rieck, “Pulsar: Stateful Black-Box Fuzzing of Proprietary Network Protocols,” in Security and Privacy in Communication Networks, vol. 164, B. Thuraisingham, X. Wang, and V. Yegneswaran, Eds. Cham: Springer International Publishing, 2015, pp. 330–347.PDF

[51]

H. Li, B. Shuai, J. Wang, and C. Tang, “Protocol Reverse Engineering Using LDA and Association Analysis,” in 2015 11th International Conference on Computational Intelligence and Security (CIS), Shenzhen, China, Dec. 2015, pp. 312–316, doi: 10.1109/CIS.2015.83.

[52]

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K. Choi, Y. Son, J. Noh, H. Shin, J. Choi, and Y. Kim, “Dissecting customized protocols: automatic analysis for customized protocols based on IEEE 802.15.4,” in Proceedings of the 9th ACM Conference on Security and Privacy in Wireless and Mobile Networks, pp. 183–193, Darmstadt, Germany, July 2016.PDF

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S. Tao, H. Yu, and Q. Li, “Bit‐oriented format extraction approach for automatic binary protocol reverse engineering,” IET Communications, vol. 10, no. 6, pp. 709–716, Apr. 2016, doi: 10.1049/iet-com.2015.0797.PDF

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M.-M. Xiao and Y.-P. Luo, “Automatic protocol reverse engineering using grammatical inference,” IFS, vol. 32, no. 5, pp. 3585–3594, Apr. 2017, doi: 10.3233/JIFS-169294.

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Y.-H. Goo, K.-S. Shim, M.-S. Lee, and M.-S. Kim, “Protocol Specification Extraction Based on Contiguous Sequential Pattern Algorithm,” IEEE Access, vol. 7, pp. 36057–36074, 2019, doi: 10.1109/ACCESS.2019.2905353.PDF

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K. Shim, Y. Goo, M. Lee, and M. Kim, “Clustering method in protocol reverse engineering for industrial protocols,” International Journal of Network Management, Jun. 2020, doi: 10.1002/nem.2126.PDF

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X. Wang, K. Lv, and B. Li, “IPART: an automatic protocol reverse engineering tool based on global voting expert for industrial protocols,” International Journal of Parallel, Emergent and Distributed Systems, vol. 35, no. 3, pp. 376–395, May 2020, doi: 10.1080/17445760.2019.1655740.

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Ye, Yapeng, Zhuo Zhang, Fei Wang, Xiangyu Zhang, and Dongyan Xu. “NetPlier: Probabilistic Network Protocol Reverse Engineering from Message Traces.” In NDSS. 2021.PDF

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