PRE-list

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

This is a collection of 69 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 the tools.ods.

Table of Contents

• Overview
• Input and Output
• Tested protocols
• Source code
• References

Overview ↑

Name	Year	Approach used
PIP [1]	2004	Keyword 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]	2005	Protocol analyzer and open language that uses the protocol analyzer specification Spec → it is meant to be integrated in monitoring and analyzing tools
ScriptGen [3]	2005	Grouping and clustering messages, find edges from clusters to clusters for being able to replay messages once a similar message arrives
RolePlayer [4]	2006	Byte-wise sequence alignment (find variable fields in messages) and clustering with FSM simplification
Ma et al. [5]	2006	Please review
FFE/x86 [6]	2006	Please review
Replayer [7]	2006	Please review
Discoverer [8]	2007	Tokenization of messages, recursive clustering to find formats, merge similar formats
Polyglot [9]	2007	Dynamic taint-analysis
PEXT [10]	2007	Message clustering for creating FSM graph and simplify FSM graph
Rosetta [11]	2007	Please review
AutoFormat [12]	2008	Dynamic taint-analysis
Tupni [13]	2008	Dynamic taint-analysis; look for loops to identify boundaries within messages
Boosting [14]	2008	Please review
ConfigRE [15]	2008	Please review
ReFormat [16]	2009	Dynamic taint-analysis, especially targeting encrypted protocols by looking for bitwise and arithmetic operations
Prospex [17]	2009	Dynamic taint-analysis with following message clustering, optionally provides fuzzing candidates for Peach fuzzer
Xiao et al. [18]	2009	Please review
Trifilo et al. [19]	2009	Measure byte-wise variances in aligned messages
Antunes and Neves [20]	2009	Please review
Dispatcher [21]	2009	Dynamic taint-analysis (successor of Polyglot using send instead of received messages)
Fuzzgrind [22]	2009	Please review
REWARDS [23]	2010	Please review
MACE [24]	2010	Please review
Whalen et al. [25]	2010	Please review
AutoFuzz [26]	2010	Please review
ReverX [27]	2011	Speech 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]	2011	Identifiying keywords, clustering and transition probability → probabilistic protocol state machine
Biprominer [29]	2011	Statistical analysis including three phases, learning phase, labeling phase and transition probability model building phase. See this figure.
ASAP [30]	2011	Please review
Howard [31]	2011	Please review
ProDecoder [32]	2012	Successor 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]	2012	Please review
Netzob [34]	2012	See this figure
PRISMA [35]	2012	Please review, follow-up paper/project to ASAP
ARTISTE [36]	2012	Please review
Wang et al. [37]	2013	Capturing 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]	2013	Please review
AutoReEngine [39]	2013	Apriori 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. See this figure for clarification.
Dispatcher2 [40]	2013	Please review
ProVeX [41]	2013	Identify Botnet traffic and try to infer the botnet type by using signatures
Meng et al. [42]	2014	Please review
AFL [43]	2014	Please review
Proword [44]	2014	Please review
ProGraph [45]	2015	Please review
FieldHunter [46]	2015	Please review
RS Cluster [47]	2015	Please review
UPCSS [48]	2015	Please review
ARGOS [49]	2015	Please review
PULSAR [50]	2015	Reverse engineer network protocols with the aim to fuzz them with thus knowledge
Li et al. [51]	2015	Please review
Cai et al. [52]	2016	Please review
WASp [53]	2016	Pcap 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]	2016	Please review
Xiao et al. [55]	2016	Please review
PowerShell [56]	2017	Please review
ProPrint [57]	2017	Please review
ProHacker [58]	2017	Please review
Esoul and Walkinshaw [59]	2017	Please review
PREUGI [60]	2017	Please review
NEMESYS [61]	2018	Please review
Goo et al. [62]	2019	Apriori 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]	2019	Physical 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]	2019	From 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]	2019	Please review
Yang et al. [66]	2020	Using 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]	2020	Follow up on Goo et al. 2019
IPART [69]	2020	Using extended voting expert algorithm to infer boundaries of fields, otherwise using three phase which are tokenizing, classifying and clustering.

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)

Name	Year	NetT	ExeT	PF	PFSM	Other Output
PIP [1]	2004	✔				Keywords/ fields
GAPA [2]	2005		✔	✔	✔
ScriptGen [3]	2005	✔				Dialogs/scripts (for replaying)
RolePlayer [4]	2006	✔				Dialogs/scripts
Ma et al. [5]	2006	✔				App-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]	2008	✔				Field(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]	2009		✔			C&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]	2011	✔				Semantics
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]	2013		✔			C&C malware
ProVeX [41]	2013	✔				Signatures
Meng et al. [42]	2014	✔			✔
AFL [43]	2014		✔
Proword [44]	2014
ProGraph [45]	2015	✔		✔
FieldHunter [46]	2015	✔				Fields
RS Cluster [47]	2015	✔				Grouped-messages
UPCSS [48]	2015	✔				Proto-classification
ARGOS [49]	2015		✔
PULSAR [50]	2015
Li et al. [51]	2015	✔		✔
Cai et al. [52]	2016	✔		✔
WASp [53]	2016	✔		✔		scored analysis reports, spoofing candidates
PRE-Bin [54]	2016	✔		✔
Xiao et al. [55]	2016	✔		✔
PowerShell [56]	2017	✔				Dialogs/scripts
ProPrint [57]	2017	✔				Fingerprints
ProHacker [58]	2017	✔				Keywords
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	✔		✔

Tested protocols ↑

Name	Year	Text-based	Binary-based	Hybrid	Other Protocols
PIP [1]	2004	HTTP
GAPA [2]	2005	HTTP
ScriptGen [3]	2005	HTTP	NetBIOS		DCE
RolePlayer [4]	2006	HTTP, FTP, SMTP, NFS, TFTP	DNS, BitTorrent, QQ, NetBios	SMB, CIFS
Ma et al. [5]	2006	HTTP, FTP, SMTP, HTTPS (TCP-Protos)	DNS, NetBIOS, SrvLoc (UDP-Protos)
FFE/x86 [6]	2006
Replayer [7]	2006
Discoverer [8]	2007	HTTP	RPC	SMB, CIFS
Polyglot [9]	2007	HTTP, Samba, ICQ	DNS, IRC
PEXT [10]	2007	FTP
Rosetta [11]	2007
AutoFormat [12]	2008	HTTP, SIP	DHCP, RIP, OSPF	SMB, CIFS
Tupni [13]	2008	HTTP, FTP	RPC, DNS, TFTP		WMF, BMP, JPG, PNG, TIF
Boosting [14]	2008		DNS
ConfigRE [15]	2008
ReFormat [16]	2009	HTTP, MIME	IRC		One unknown protocol
Prospex [17]	2009	SMTP, SIP	SMB		Agobot (C&C)
Xiao et al. [18]	2009	HTTP, FTP, SMTP
Trifilo et al. [19]	2009		TCP, DHCP, ARP, KAD
Antunes and Neves [20]	2009	FTP
Dispatcher [21]	2009	HTTP, FTP, ICQ	DNS
Fuzzgrind [22]	2009
REWARDS [23]	2010
MACE [24]	2010
Whalen et al. [25]	2010
AutoFuzz [26]	2010
ReverX [27]	2011	FTP
Veritas [28]	2011	SMTP	PPLIVE, XUNLEI
Biprominer [29]	2011		XUNLEI, QQLive, SopCast
ASAP [30]	2011	HTTP, FTP, IRC, TFTP
Howard [31]	2011
ProDecoder [32]	2012	SMTP, SIP	SMB
Zhang et al. [33]	2012	HTTP, SNMP, ISAKMP
Netzob [34]	2012	FTP, Samba	SMB		Unknown P2P & VoIP protocol
PRISMA [35]	2012
ARTISTE [36]	2012
Wang et al. [37]	2013	ICMP	ARP
Laroche et al. [38]	2013	FTP	DHCP
AutoReEngine [39]	2013	HTTP, FTP, SMTP, POP3	DNS, NetBIOS
Dispatcher2 [40]	2013	HTTP, FTP, ICQ	DNS	SMB
ProVeX [41]	2013	HTTP, SMTP, IMAP	DNS, VoIP, XMPP		Malware Family Protocols
Meng et al. [42]	2014		TCP, ARP
AFL [43]	2014
Proword [44]	2014
ProGraph [45]	2015	HTTP	DNS, BitTorrent, WeChat
FieldHunter [46]	2015	MSNP	DNS		SopCast, Ramnit
RS Cluster [47]	2015	FTP, SMTP, POP3, HTTPS	DNS, XunLei, BitTorrent, BitSpirit, QQ, eMule		MSSQL, Kugoo, PPTV
UPCSS [48]	2015	HTTP, FTP, SMTP, POP3, IMAP	DNS, SSL, SSH	SMB
ARGOS [49]	2015
PULSAR [50]	2015
Li et al. [51]	2015
Cai et al. [52]	2016	HTTP, SSDP	DNS, BitTorrent, QQ, NetBios
WASp [53]	2016				IEEE 802.15.4 proprietary protocols, Smart plug & PSD systems
PRE-Bin [54]	2016
Xiao et al. [55]	2016
PowerShell [56]	2017		ARP, OSPF, DHCP, STP		CDP/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]	2019	HTTP	DNS
Universal Radio Hacker [63]	2019				proprietary wireless protocols of IoT devices
Luo et al. [64]	2019
Sun et al. [65]	2019
Yang et al. [66]	2020		IPv4, TCP
Sun et al. [67]	2020
Shim et al. [68]	2020	FTP	Modbus/TCP, Ethernet/IP
IPART [69]	2020		Modbus, IEC104, Ethernet/IP

Source Code ↑

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

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

References ↑

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[11]

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Y. Wang, N. Zhang, Y.-M. Wu, B.-B. Su, and Y.-J. Liao, “Protocol formats reverse engineering based on association rules in wireless environment,” in Proceedings of the 12th IEEE International Conference on Trust, Security and Privacy in Computing and Communications (TrustCom ’13), pp. 134–141, Melbourne, Australia, July 2013.

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[40]

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[41]

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[42]

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[43]

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