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Building a large, realistic and labeled HTTP URI dataset for anomaly-based intrusion detection systems: Biblio-US17

Abstract

This paper introduces Biblio-US17, a labeled dataset collected over 6 months from the log fles of a popular public website at the University of Seville. It contains 47 million records, each including the method, uniform resource identifer (URI) and associated response code and size of every request received by the web server. Records have been classifed as either normal or attack using a comprehensive semi-automated process, which involved signature-based detection, assisted inspection of URIs vocabulary, and substantial expert manual supervision. Unlike comparable datasets, this one ofers a genuine real-world perspective on the normal operation of an active website, along with an unbiased proportion of actual attacks (i.e., non-synthetic). This makes it ideal for evaluating and comparing anomalybased approaches in a realistic environment. Its extensive size and duration also make it valuable for addressing challenges like data shift and insufcient training. This paper describes the collection and labeling processes, dataset structure, and most relevant properties. We also include an example of an application for assessing the performance of a simple anomaly detector. Biblio-US17, now available to the scientifc community, can also be used to model the URIs used by current web servers.

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Building a large, realistic and labeled HTTP URI dataset for anomaly-based intrusion detection systems: Biblio-US17

Author: Díaz Verdejo, Jesús; Estepa Alonso, Rafael María; Estepa Alonso, Antonio José; Muñoz Calle, Francisco Javier; Madinabeitia Luque, Germán
Publisher: Springer
Year: 2025
DOI: 10.1186/s42400-024-00336-3
Source: https://idus.us.es/bitstreams/886500d5-a3bd-4ad1-9dbe-5e0d5195c0b7/download
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
h ps://doi.o g/10.1186/s42400‑024‑00336‑3
RESEARCH Open Access
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Cybe secu i y
Building ala ge, ealis ic andlabeled HTTP
URI da ase o anomaly‑based in usion
de ec ion sys ems: Biblio‑US17
Jesús Díaz‑Ve dejo1 , Ra ael Es epa2 , An onio Es epa2* , Ja ie Muñoz‑Calle2 and
Ge mán Madinabei ia2
Abs ac
This pape in oduces Biblio‑US17, a labeled da ase collec ed o e 6 mon hs om he log iles o a popula public
websi e a he Uni e si y o Se ille. I con ains 47 million eco ds, each including he me hod, uni o m esou ce iden i‑
ie (URI) and associa ed esponse code and size o e e y eques ecei ed by he web se e . Reco ds ha e been
classi ied as ei he no mal o a ack using a comp ehensi e semi‑au oma ed p ocess, which in ol ed signa u e‑based
de ec ion, assis ed inspec ion o URIs ocabula y, and subs an ial expe manual supe ision. Unlike compa able
da ase s, his one o e s a genuine eal‑wo ld pe spec i e on he no mal ope a ion o an ac i e websi e, along wi h an
unbiased p opo ion o ac ual a acks (i.e., non‑syn he ic). This makes i ideal o e alua ing and compa ing anomaly‑
based app oaches in a ealis ic en i onmen . I s ex ensi e size and du a ion also make i aluable o add essing
challenges like da a shi and insu icien aining. This pape desc ibes he collec ion and labeling p ocesses, da ase
s uc u e, and mos ele an p ope ies. We also include an example o an applica ion o assessing he pe o mance
o a simple anomaly de ec o . Biblio‑US17, now a ailable o he scien i ic communi y, can also be used o model
he URIs used by cu en web se e s.
Keywo ds Anomaly de ec ion, In usion de ec ion sys ems, Da a acquisi ion, T aining da ase s, Web applica ion
il e s, Biblio‑US17 da ase
*Co espondence:
An onio Es epa
[email p o ec ed]
Full lis o au ho in o ma ion is a ailable a he end o he a icle
In oduc ion
Mo e han 200 million web se e s a e ope a ional on he
In e ne oday, acili a ing he deli e y o Web Se ices
and websi es. These se e s play a c i ical ole in ena-
bling use s o sha e in o ma ion and accomplish much
o hei daily asks (Fo bes. 2023; Ageed e  al. 2021).
Consequen ly, he Hype ex T ans e P o ocol (HTTP)
now accoun s o a signi ican po ion o In e ne a ic
(Ib ahim e al. 2021). Howe e , his widesp ead use also
makes web se e s equen a ge s o cybe a acks and
malwa e dis ibu ion (Husák e al. 2021). Acco ding o
Apps (2021), some 30,000 websi es we e hacked daily in
2021. Mos o hese a acks a ge ulne abili ies in web-
si e so wa e, especially in con en managemen sys ems,
whe e plugins and ou da ed so wa e play a signi ican
ole in inc easing he isk o b eaches (BenMa in e al.
2022).
Web a ack de ec ion has adi ionally elied on
in usion de ec ion sys ems (IDS) (Aga wal and Hus-
sain 2018) and Web Applica ion Fi ewalls (WAFs)
(Pałka and Zacha a 2011). Many IDS and WAFs iden-
i y a acks by looking o ecognizable pa e ns in da a
o i s ea u es. Fea u e-based in usion de ec ion ech-
niques p ima ily ely on p ede ined in usion cha ac-
e is ics, known as Indica o s o Comp omise (IoCs), o
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Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
iden i y known a ack pa e ns. As such, hey a e well-
sui ed o de ec ing known h ea s. Howe e , despi e
he a ailabili y o open-sou ce eposi o ies (Díaz-Ve -
dejo e al. 2022), IoC da ase s, wi h hei high accu acy,
a e o en conside ed aluable in ellec ual p ope y by
h ea in elligence endo s and a e ypically no made
public. On he o he hand, anomaly-based in usion
de ec ion sys ems (AIDS) ocus on iden i ying de ia-
ions om es ablished no mal beha io , making hem
pa icula ly e ec i e a de ec ing unknown o no el
a acks (which made up o e 42% o a acks in 2021
acco ding o some es ima es ze 2023). AIDS a e no
wi hou challenges, such as alse posi i es (Diaz-Ve -
dejo e al. 2024a) o hei eliance on quali y da ase s
o lea n no mal beha io (Be a e e al. 2018; Hajj e al.
2021; Kh aisa e al. 2019). Using aw da a, such as a -
ic and logs, is mo e app op ia e o anomaly-based
de ec ion because i cap u es he ull spec um o use
and sys em ac i i ies (Ga cia-Teodo o e al. 2009). The
da ase in oduced in his pape is designed speci ically
o anomaly-based IDS e alua ion, which is a eason-
able app oach gi en he na u e o he da a.
The challenges associa ed wi h cons uc ing eal-wo ld
log da ase s a e signi ican . Fi s ly, p i acy conce ns a ise
because hese da ase s o en con ain sensi i e in o -
ma ion, such as IP add esses, which o ganiza ions a e
eluc an o make publicly accessible o a oid p i acy io-
la ions. As a consequence, ep oducibili y issues eme ge
since e alua ions a e equen ly conduc ed on p i a e
da ase s, p e en ing he ep oducibili y o he esul s
and compa abili y o he p oposed app oaches. Secondly,
g ound u h de e mina ion becomes challenging amids
unknown use ac i i ies, as he labeling p ocess ideally
equi es expe examina ion, which is p ohibi i e in la ge
da ase s (Díaz-Ve dejo e al. 2020). On he o he hand,
syn he ic log da ase s gene a ed h ough simula ions
o e some ad an ages. They p o ide a mo e lexible ech-
nical en i onmen , enable he launch o a bi a y a acks,
and ensu e a eliable g ound u h due o he absence
o unknown ac i i ies (Gao e al. 2017). Howe e , he e
a e limi a ions o using syn he ic da a. Fi s ly, eal-wo ld
complexi y may no be ully cap u ed, as simula ions
migh miss e a ic use beha io o neglec ed con igu a-
ions, leading o a lowe a e o alse ala ms (Landaue
e  al. 2022). Secondly, long- e m changes in sys em
in as uc u e o con en a e o en o e looked in sho -
e m simula ions, esul ing in a lack o ep esen a ion o
e ol ing en i onmen s. Las ly, he numbe and con en
o syn he ic a acks may no e lec he p opo ion, di e -
si y and sophis ica ion o eal-wo ld a acks. Gi en hese
challenges, he da ase in oduced in his pape aims o
b idge he gap by p o iding a ealis ic ye manageable
log da ase speci ically designed o anomaly-based IDS
e alua ion.
We p esen a new da ase in ended o scien i ic use in
he con ex o URI-based anomaly de ec ion: Biblio-US17
(b ie ly ou lined in Diaz-Ve dejo e  al. (2024b)). Ou
da ase is de i ed om access log iles o he public web-
si e o he Uni e si y o Se ille Lib a y in Spain and has
h ee majo dis inc i e cha ac e is ics:
• I was collec ed in a eal ope a ing en i onmen .
• I con ains a la ge olume o eco ds (o e 47 million)
spanning a b oad pe iod (six mon hs), allowing o
he s udy o long- e m da a changes o some ex en .
• I is labeled: we conduc ed a ho ough semi-au o-
ma ed p ocess o de e mine g ound u h. This
equi ed a o midable e o , bu as a esul , a ealis-
ic model o no mal websi e beha io can be in e ed.
Addi ionally, he pe o mance o anomaly-based IDS
can be mo e accu a ely assessed using eal a acks
a ge ed a he ac i e websi e.
Biblio-US17 also has some limi a ions (see sec ion"Limi-
a ions and challenges"), he mos no able being ha
da a is es ic ed o he me hod, URI, esponse code,
and esponse size o each eques ecei ed by he se e .
While eco ds a e in ch onological o de , hey lack ull
imes amps, which limi s he use ulness o his da ase
o ime-based anomaly de ec ion echniques. Despi e
i s limi a ions, we a e con iden ha Biblio-US17 will
be a aluable addi ion o public da ase s, imp o ing he
e alua ion o u u e URI- ocused anomaly-based IDS and
enabling compa ison o di e en app oaches wi h ealis-
ic da a. We hope his e o will lead o b oade indus-
y ecogni ion and accep ance o academic esea ch
ou comes.
The emainde o his pape is o ganized as ollows.
Sec ion "Backg ound and ela ed wo k" discusses he
backg ound o c ea ing da ase s o web-a ack de ec ion
and e iews ela ed wo k. Sec ion"Acquisi ion and labe-
ling" desc ibes he p ocesses o da a acquisi ion, p epa a-
ion, and labeling. Sec ion"Da ase desc ip ion" p o ides
a de ailed desc ip ion o he da ase , including he s uc-
u e o iles, di ec o ies, and o ma . Sec ion"Sugges ed
use and illus a i e case s udy" add esses pa i ioning o
esea ch pu poses and includes an illus a i e case s udy.
Sec ion"Limi a ions and challenges" highligh s he main
limi a ions o he da ase , and sec ion"Conclusions" con-
cludes he pape .
Page 3 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
Backg ound and ela ed wo k
The c ea ion o sui able da ase s wi h eal-wo ld p ope -
ies is a challenging endea o (Sha a aldin e al. 2018).
Fo a da ase o be adequa e o AIDS de elopmen i
should a leas be (Viegas e al. 2017):
1. Rep esen a i e. This means ha he da a used should
ha e simila p ope ies o he da a ound in he sys-
em being p o ec ed, such as being alid, cu en , and
speci ic o he deploymen en i onmen . I is wo h
no ing ha using da a om ope a ional en i on-
men s is he mos e ec i e, whe eas syn he ic da a
may no accu a ely ep esen an ealis ic ope a ional
con ex (despi e ecen a emp s o easonably gene -
a e ealis ic log da a (Landaue e al. 2020; Ue z e al.
2021)).
2. Su icien ly la ge. The da ase should include enough
olume o da a o be pa i ioned and s ill be use ul
o aining, e alua ion o alida ion pu poses. The
aining da ase size should su ice o ain he no -
mali y model wi h s a is ical signi icance (Es epa
e al. 2020). Ideally, he collec ion pe iod should also
be long enough o add ess he issue o da a a iabil-
i y o e ime. Syn he ic da a gene a ed wi h sc ip s
usually lacks he a iabili y o da a om ope a ional
in as uc u es (Landaue e al. 2022).
3. Labeled. Each da a eco d should be classi ied as
no mal o an a ack. Howe e , labeling can be chal-
lenging when dealing wi h da ase s collec ed om
public ope a ional deploymen s, as hey may con ain
e o s o a acks (Mahoney and Chan 2003; B ugge
and Chow 2007). Fo his eason, da a sani iza ion is
ad ised (C e u e al. 2008) o iden i y a ack ins ances
in he collec ed da a and educe he numbe o unla-
beled a acks o da a e o s as much as possible. This
p ocess should ideally include manual expe supe -
ision (Paxson 2004), which is no easible o e y
la ge da ase s. A common me hod o educe he
sani iza ion cos is o u ilize a signa u e-based IDS o
iden i y he majo i y o known a acks (Díaz-Ve dejo
e al. 2022). S ill, expe supe ision is una oidable
i one aims o ob ain quali y da ase s ee o a acks
o undesi ed a i ac s (C e u e al. 2008). In con as ,
labeling is s aigh o wa d in a syn he ic da ase s
since all ac i i y is known.
Besides he abo e equi emen s, a da ase collec ed
om a public en i onmen should comply wi h appli-
cable p i acy egula ions, such as he Eu opean Union’s
Gene al Da a P o ec ion Regula ion (Hje ppe e  al.
2019). Fu he mo e, secu i y should also be conside ed
as some da a could po en ially disclose in o ma ion
abou he web se e in as uc u e, acili a ing u u e
a acks. To add ess hese issues, p i acy-p ese ing
echniques such as gene aliza ion, supp ession o pe -
u ba ion should be applied (Fung e al. 2010). The la -
e consis o eplacing he o iginal da a alues wi h
syn he ic ones ha p ese e owne s’ p i acy while s ill
e ain use ul s a is ical in o ma ion (Rei e and Rubin
1998; Salaza -He nández and Díaz-Ve dejo 2010).
Da ase s sui ed o web a ack de ec ion
The URI ield in HTTP eques s plays a cen al ole in
iden i ying web a acks (Saxena e  al. 2022). Indeed,
app oxima ely 85% o a ack signa u es used by com-
mon SIDS/WAF include he URI ield (Díaz-Ve dejo e al.
2022). This sec ion discusses da ase s cu en ly a ailable
o esea che s o de eloping and e alua ing anomaly-
based IDS ha speci ically a ge he con en o HTTP
messages, pa icula ly he URI. I is impo an o no e
ha we ha e excluded da ase s ha do no co e HTTP
messages, e en i hey ha e o he aluable p ope ies o
anomaly de ec ion, such as hose based solely on a ic
lows. I is also impo an o no e ha he e a e unla-
beled da ase s o HTTP messages collec ed om eal-
li e deploymen s, such as Schulz e al. (2021), Chodak
e al. (2020), ha a e a ailable. Howe e , hey ha e been
excluded om his sec ion because hey canno be used
o AIDS e alua ion, due o he absence o labeling.
To he bes o ou knowledge, Table1 lis s all pub-
licly a ailable labeled da ase s con aining he URI ield
o HTTP eques s. The able p o ides in o ma ion on
he sou ce ile o ma , labeling ca ego ies, whe he he
da ase was a i icially gene a ed o collec ed om an
ope a ional en i onmen , whe he labels we e manu-
ally supe ised, he collec ion da e (yea ) and pe iod,
and numbe o usable URIs in each da ase . A no iceable
common cha ac e is ic among hese da ase s is ha hey
ha e been a i icially gene a ed (i.e., syn he ic) and au o-
ma ically labeled a e gene a ion. The only excep ion is
NSL-KDD, whe e labels unde wen a subsequen supe -
ision. Only UNSW-NB15 con ains a mix o syn he ic
packe s (a acks) and li e-cap u ed ones. Howe e , he
labeling has been done wi hou supe ision.
F om he pe spec i e o da ase acquisi ion and con-
s uc ion, ou app oach signi ican ly di e s om hose
u ilized in he da ase s included in Table1. As p e iously
men ioned, o p io i ize e o - ee and ease labeling, all
da ase s in Table1 gene a e a ic syn he ically wi hin
con olled en i onmen s. A basic app oach is exempli ied
by DARPA99, whe e a ic is cap u ed du ing he simu-
la ed no mal ope a ion o use s in a con olled ne wo k.
In his case, all use -gene a ed a ic is assumed o be
no mal, while a ious ypes o a acks a e injec ed om
a speci ic s a ion in a con olled manne , acili a ing easy
Page 4 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
labeling. Con e sely, CIC-IDS2017 ep esen s a mo e
sophis ica ed app oach, whe e use p o iles a e es ab-
lished based on eal a ic moni o ing be o e syn he ic
a ic gene a ion. These p o iles aim o main ain p o-
po ionali y among di e en use ypes du ing da ase
gene a ion, he eby minimizing po en ial biases in he
gene a ed a ic. Howe e , e en in his app oach, a acks
a e syn he ically and con ollably in oduced, which does
no e lec hei ac ual equency in eal-wo ld scena ios.
In con as , ou app oach s a s om he opposi e si u-
a ion: we cap u e eal-wold a ic om an ac i e websi e,
including a i ac s, e o s, and po en ial a acks, and hen
p oceed o iden i y no mal and a ack a ic. Admi edly,
his app oach c ea es a “g ay a ea” whe e some eques s
canno be labeled due o a lack o in o ma ion o ambi-
gui ies. Howe e , simila si ua ions a e also obse ed in
some da ase s om Table1 (e.g., NSL-KDD). The main
ad an age o ou app oach is he ealism and ep esen a-
i eness o he da a, ee om biases associa ed wi h he
gene a ion o bo h clean a ic and a acks.
Rega ding da a olume, we obse e a signi ican di e -
ence in he numbe o eco ds o he da ase s in Table1
compa ed o ou s. A low size may nega i ely impac he
s a is ical signi icance o he no mali y models in e ed.
Fu he mo e, he use o simula ions esul s in educed
di e si y in he URIs, ypically esponding o ela i ely
simple se e s. Fo example, DARPA98 used wo se e s
wi h limi ed ocabula ies and a low numbe o a iables/
alues pe URI. In o he cases (e.g., CIC-IDS2017), a
ela i ely small numbe o URIs appea equen ly, lead-
ing o educed a iabili y. In no case is a con en man-
agemen sys em (CMS) was used a he se e (such as in
ou da ase ), and he complexi y o he se ice and URI
a iabili y is gene ally educed compa ed o ou s. Mo e-
o e , he equency and ype o a acks a e syn he ically
gene a ed in all da ase s, in oducing a signi ican bias in
de ec ion pe o mance assessmen s (TP/FP a es lacking
eal signi icance).
Due o he in a ian a ic gene a ion mechanism and
he cos o simula ion execu ion, he da ase s in Table1
lack su icien empo al du a ion and a iabili y o ana-
lyze issues ela ed o empo al d i . In con as , cap u -
ing a ic in a eal scena io is ela i ely s aigh o wa d,
allowing o ex ended collec ion pe iods, such as in ou
case. In ou app oach, he cos o building he da ase
is associa ed wi h he labeling p ocess a he han da a
gene a ion.
Finally, i is wo h no ing ha some o he da ase s
om Table1 a e e y old (e.g., om 2010 o olde ). As
a esul , he e is a isk ha he URIs (whe he malicious
o benign) may no accu a ely e lec cu en eques pa -
e ns, po en ially limi ing hei e ec i eness in e alua ing
AIDS p oposals in ended o ope a ional en i onmen s.
Biblio-US17, collec ed om a eal-wo ld ac i e web-
si e, is labeled wi h no mal and a ack classes. I ea u es
a la ge olume o eco ds, a collec ion pe iod sui able o
s udying ime shi issues o some ex en , and is ecen
Table 1 Public da ase s used in he li e a u e o web in usion de ec ion sys ems assessmen (Díaz‑Ve dejo e al. 2022)
* Classes: N (no mal), A (a acks), U (unlabeled), An (anomaly)
** Labels: G (As gene a ed), S (Supe ised)
Da ase Fo ma (URIs) Classes Real/Syn h Labels Only HTTP Yea Du a ion
#
URIS Commen s
DARPA’99 PCAP N/A/U S G N 1999 5w 100k Widely used o IDS esea ch, lawed, obso‑
le e, no h p speci ic a ic/a acks, only 2
web se e s
KDD’99 CSV N/A/U S G N 1999 5w – Pa ame e ized lows, di ec ly de i ed
om DARPA’99, inhe i DARPA’99 laws
NSL‑KDD PCAP N/A/U S GS N 2009 5w 10k Sani ized e sion o KDD’99, no o URI
based analysis
CSIC2010 LIST N/An S G Y 2010 – 96k Only URI, mixes a acks wi h anomalies,
single se e
UNSW‑NB15 PCAP N/A RS G N 2015 31h 27k Lack o de ails abou web se e s and a ic
ISCX‑URL2016 CSV N/A S G Y 2016 – – Ou going a ic, 114k URIs, no sui able
o ain a si e, highly unbalanced (only 35k
no mal URIs)
CIC‑IDS2017 PCAP N/A S G N 2017 5d 272k 392k URIs, mix o ou going and incoming
a ic, mos incoming URIS a e a acks, low
a iabili y in URIs (only 6k que ies and many
epea ed URIs)
Biblio‑US17 CSV N/A R S Y 2017 6m 47M HTTP log om public Web Se e wi h no ‑
mal and a acks URIs, high a iabili y in URIs,
wide imespan
Page 5 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
enough o e lec cu en web dynamics. The da ase also
bene i s om being old enough o some 0-day a acks o
now ha e public signa u es, wi h p i acy conce ns mi i-
ga ed as he se e is no longe ac i e.
Acquisi ion andlabeling
The da ase o igina es om he aces gene a ed by he
web se ice o he Lib a y o he Uni e si y o Se ille
(h p:// bib. us. es) be ween Janua y 1s and July 17 h,
2017. The web applica ion, de eloped wi h D upal 7.96
con en managemen sys em and he Apache web se e
( 2.2), p o ides in o ma ion abou bibliog aphic hold-
ings. As such, mos eques s a e bibliog aphic in o ma-
ion sea ches (e.g., book i les, pape s, hesis, au ho s)
and, o a lesse ex en , con en upda es om lib a y s a .
The websi e includes also news and se ice in o ma ion.
The si e, wi h o e 50,000 s uden s, expe iences high
demand and a wide ange o eques s, making i highly
dynamic.
Ou da a sou ce comes om he Apache access log
iles ha span 198 days and con ain a o al o 47,902,323
eques lines. Du ing his pe iod, he po al ansi ioned
be ween HTTP and HTTPS, so bo h se ice po s gene -
a ed log iles. Fo secu i y easons, he uni e si y did no
handed us he o iginal log da a iles bu a p ocessed copy
o hem (one daily ile named a e he da e o collec ion)
wi h he ollowing da a ields o each eques : me hod,
URI, p o ocol e sion, esponse code and esponse size
as shown in he ollowing box.
As illus a ed in Fig.1, we ca ied ou he asks o da a
p epa a ion and labeling wi h he iles ha we e gi en o
us:
(i) Da a p epa a ion, in which we p oduced a unique
ID o each eco d, supp essed unsui able eco ds
(e.g., mal o med messages acco ding o s anda d
syn ax o eques s wi hou he URI ield), and u -
he anonymized sensi i e in o ma ion.
(ii) Labeling, in which we conduc ed a semi-supe -
ised sani iza ion p ocess ha allowed us o de ec
a acks wi h di e en eliabili y le els (labeled as
LVL1 o LVL4) and u he il e unsui able eco ds
ha did no comply wi h s anda d syn ax o a i-
ous easons (labeled as OOS1 o OOS4).
We ob ained a inal coun o 47,402,996 sui able egis e s
om which he clean/a ack da ase s we e gene a ed. The
emainde o his sec ion elabo a es on hese p ocesses.
Da a p epa a ion p ocess
This p ocess aims o ob ain an ini ial, homogeneous,
well- o ma ed se o eco ds ha complies wi h p i acy
and secu i y egula ions and includes, a leas he me hod
and URI o each eco d.
P ep ocessing andcondi ioning
To c ea e a p ope ly-o ganized and well- o med da ase ,
we ca ied ou he ollowing s eps:
1. We elimina ed eques lines ha missed he URI
ield, which led o he emo al o 499,327 lines. Mos
o hese lines we e HEAD eques s wi hou a co e-
sponding esponse code.
2. Each eques was assigned a unique Regis e ID (RID)
label ha shows he collec ion da e and sequence
o de in he o iginal log ile. Fo example, he RID o
he 123 d eco d in he HTTPS log ile da ed Ma ch
1s would be [03-01-S000123]. The RID can also
be seen as a basic empo al e e ence, indica ing he
da e and o de o he eques a he han a ull imes-
amp.
3. We u ilized a pa se o check he con o mi y o
eques s wi h s anda d syn ax. I possible, he issues
iden i ied (e.g., cha ac e ’
’ ending he eques , un-
ca ion e o s, e c.) we e ixed. O he wise, mal o med
eques s we e elimina ed, esul ing in he emo al o
89 egis e s.
Fig. 1 Sani izacion s eps as de i ed om Díaz‑Ve dejo e al. (2020)

Page 6 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
By he end o his ask, he da ase con ained a o al o
47,402,907 eques lines, each wi h a unique RID and he
ields shown in he example abo e. These lines unde -
wen he anonymiza ion p ocess desc ibed nex .
Anonymiza ion
This p ocess aims o de ec and ob usca e sensi i e
in o ma ion ha could comp omise he websi e secu-
i y by disclosing se ice in as uc u e, o iola e da a
p o ec ion egula ions. This s ep was compulso y and
equi ed he coope a ion and app o al o independen
and ex e nal pe sonnel (legal and secu i y eams). To
achie e his, we ca ied ou wo sequen ial asks:
1. Iden i ica ion o Sensi i e In o ma ion: we sea ched
o in o ma ion ela ed o pe sonal da a o se e
secu i y (conside ing pe cen encoding) in he URIs.
To iden i y Pe sonal Da a, we ca e ully sea ched o
speci ic keywo ds and pa e ns, such as login c eden-
ials, passwo ds, names, emails, o use IDs. Al hough
he sea ch was au oma ed, e e y occu ence was
manually inspec ed. As a esul , his p ocess was
ime-consuming and ook abou wo mon hs o com-
ple e. We ound ha mos o he sensi i e in o ma-
ion belonged o he au ho s o he pape s o books
use s we e sea ching o , no he se ice use s. How-
e e , we also iden i ied some sensi i e pe sonal da a,
including 62 di e en Uni e si y o Se ille use IDs
(u us), wo IP add esses, and one email add ess. To
iden i y In as uc u e Sensi i e Da a, we sea ched
o ins i u ional se e names (i.e., domain name us.
es) and ilenames wi h po en ial sensi i e in o ma-
ion. A o al o 4371 di e en ilenames (wi h ex en-
sions such as.doc,.js,.pd ,.pps,.pp ,.sw ,. x and.xls)
we e ound in mo e han 16 million URIs. Rega ding
ins i u ional se e s, we ound 27,187,785 URIs ha
con ained up o 44 di e en se e names. All he
in o ma ion iden i ied in his ask (e.g., ilenames,
se e names, use ’s sensi i e in o ma ion) accoun ed
o 4480 ex s ings, which should be ob usca ed in
he co esponding URIs o p e en u u e a acks on
he in as uc u e and o comply wi h p i acy egula-
ions.
2. Pseudo-anonymiza ion o sensi i e in o ma ion: we
applied a pseudo-anonymiza ion p ocedu e o he
s ings iden i ied in he p e ious ask. To p ese e o
a la ge ex en URI cha ac e is ics, we used a subs i-
u ion echnique wi h he ollowing cha ac e is ics
(Salaza -He nández and Díaz-Ve dejo 2010):
• Dic iona y-based: e e y occu ence o he same
o iginal s ing will unde go he same subs i u ion.
• The subs i u ing s ing keeps he ollowing s a-
is ical p ope ies o he o iginal s ing: leng h,
numbe o alphanume ic cha ac e s, and numbe
o uppe and lowe cha ac e s. Special cha ac e s
(no alphanume ic) a e p ese ed and kep in hei
o iginal posi ion since hey a e commonly pa o
malicious URIs (Saxena e al. 2022).
• Filename ex ensions a e p ese ed.
We s a ed he subs i u ion p ocess by he longes s ing
o a oid i e a i e subs i u ions. Table2 shows some illus-
a i e examples o subs i u ions (in bold).
While ob usca ion a ec s he da a used o lea n-
ing o e alua ing AIDS, i s impac a ies depending on
he ex en and me hod o s ing subs i u ion. We aimed
o minimize his impac on he da ase . Ul ima ely, we
subs i u ed 5.4% o he URIs’ s ings, while p ese ing
mos o he URI cha ac e is ics ele an o classi ica ion
(Saxena e al. 2022; Abad e al. 2023). We conduc ed a
p elimina y assessmen o he impac o he anonymiza-
ion p ocess based on known a ack de ec ion. The non-
anonymized a ack da ase would ha e only 3 addi ional
a acks (ou o +30k), while he clean da ase emains
unchanged.
The da ase ob ained a e he anonymiza ion p ocess
is published unde he name ull da ase (see Fig.1) and
con ains all he egis e s conside ed in he labeling phase.
Table 2 Examples o anonymiza ion
O iginal URI Pseudonymized URI Sensi i e in o ma ion
/a qui ec u a/use s/adol o /a qui ec u a/use s/kx acj u us (use id)
/sea ch/google/adol o /sea ch/google/kx acj
/sea ch/node/beni o2 /sea ch/node/hy3u a
/si es/se e 1.co.es/ iles/Jhon_1234_webSocke .pd /si es/xzy2kpq.kp.le/ iles/nHom_3782_pN dp bq .pd Domain and Filename
/ u ismo/si es/se e 1.co.es/ iles/Viajes20-22.doc / u ismo/si es/xzy2kpq.kp.le/ iles/h Y1gbn7-98.doc
/si es/se e 2.co.es/ iles/ /si es/kjpqb7g.bn.y / iles/
/si es/se e .us.es/ iles/js/js_ p4-pa9.js /si es/pomnh .mn.d / iles/js/js_p8m-n1 .js
Page 7 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
Labeling p ocess
We conduc ed a ho ough s udy o he ull da ase o
es ablish g ound u h and label each eco d as clean,
a ack, o no in compliance wi h s anda d syn ax. The
p ocess is ou lined in Fig.1, and, in gene al e ms, i ol-
lows he sani iza ion me hodology p oposed in Díaz-
Ve dejo e al. (2020). We ha e ollowed h ee sequen ial
s eps: (S1) use o signa u e-based IDSs o de ec known
a acks, (S2) manual supe ision o he ale s p oduced
du ing S1 o de ec alse posi i es and con i m ue posi-
i es, and (S3) inspec ion o he URI ocabula y: a semi-
au oma ed ask o ind new a acks o non-complian
egis e s. The ollowing subsec ions elabo a e on hese
s eps.
Known a acks de ec ion (S1)
We used he signa u e-based IDS ool Inspec o Log
(Díaz-Ve dejo e al. 2024c) (a ool o o line p ocessing),
o de ec a acks in log iles by applying he de ec ion
ulese s om he o icial eposi o ies o he open-sou ce
IDSs Sno (Tal 2023; ETo 2023), Nemesida ( ee e sion)
(Nem 2023), and he WAF ModSecu i y (OWA 2023).
Only ules including he URI ield we e selec ed excep
o hose labeled as DELETED in he Talos and ETOpen
eposi o ies. ModSecu i y was un using pa anoia le els
1, 2 (le el 3 was disca ded due o an o e whelming a e o
alse posi i es). Table3 shows, o each ulese , he num-
be o ules selec ed, he numbe o ale s p oduced, and
he numbe o di e en URIs e e enced in hese ale s.
The esul s in columns wo o ou show ha Sno ’s ules
p oduced mos o he ala ms (83% o hem). A o al o
283,165 URIs (ba ely 0.6% o he URIs in he ull da ase )
we e classi ied as a ack by hese signa u e-based IDSs.
Inspec ion (S2)
Each URI classi ied as an a ack by Inspec o Log was man-
ually inspec ed by an expe o de e mine whe he i was
a ue posi i e (TP) o alse posi i e (FP).1 URIs ound o
be TPs we e u he classi ied and labeled acco ding o
hei soundness and scope as ei he indubi able (labeled
as LVL1) o con ex -dependen (labeled as LVL2). The
la e indica es ha , al hough he URI is conside ed an
a ack in ou pa icula con ex , i could po en ially be
non-malicious in a di e en ope a ing en i onmen . The
igh mos pa o Table3 (columns i e o se en) shows
he esul s ob ained o each ulese . The las column
shows he a ack le el ca ego y (i.e., LVL1 o LVL2) o he
URIs manually e i ied as TP. In he bo om ow, Table3
shows he o e all numbe o a acks: 273,943 (
≥
99%
indubi able), which accoun s o 0.58% o he egis e s.
I is wo h no ing ha , du ing manual examina ion,
we ound ha mos o he a acks de ec ed by Talos and
ETopen ules we e ela ed o a pe cen encoding a ack
using he sequence %252525... wi h inc easing size
( he e we e 314,076 URIs using %252525). Mo e spe-
ci ically, in 257,258 cases, he URI size was g ea e han
1450 cha ac e s (which is a h eshold de ined in he ule
SID=17,410). Mo eo e , only 15,076 o he known a acks
de ec ed in his phase we e no ela ed o his s ing,
which would lead o an a ack a e o 0.03 % o he URIs
in he ull da ase .
Vocabula y inspec ion (S3): segmen a ion‑based analysis
This s ep aims o u he disco e un ecognized a acks
o URIs ou o speci ica ion in o de o build a quali y
da ase . Fo his, we ollowed he semi-supe ised me h-
odology desc ibed in Díaz-Ve dejo e al. (2020) in which
he e ision ocuses on he di e en ex s ings ha
compose he URI (some 80k) a he han he URIs (some
47M). The me hod can be summa ized as ollows:
• Dic iona ies o ma ion. URIs deemed clean (which
also include FP in S2) a e pa sed and segmen ed o
Table 3 A ack de ec ion esul s pe ulese
Rulese #Rules # URIs classi ied as
Ac i a ed #Ale s #URIs #FP #TP (LVL1/LVL2)
Talos (22/02) + ETopen (22/03)(Sno ) 175 287,253 278,404 8571 269,833 (26,8148/1685)
Nemesida (21/11) 125 34,520 19,252 627 18,625 (17,774/851 )
CRS3.3.2‑PL1 (ModSecu i y) (22/04) 43 18,345 9206 108 9098 (9098/0)
CRS3.3.2‑PL2 (ModSecu i y) 75 22,629 10,085 243 9842 (9482/0)
To al 418 344,942 283,165 9222 273,943 (272,103/1840 )
1 A p elimina y ule classi ica ion helped educe he amoun o manual
wo k by deac i a ing hose ules ha gene a ed exclusi ely alse posi i es
(e.g., SIDs 41742 and 1852 om he TALOS eposi o y, which e e o
obo s and gene a ed mo e han 50,000 FPs).
Page 8 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
gene a e h ee dic iona ies om he ex s ings
(i.e., wo ds) ound be ween s anda d delimi e s ha
cons i u e he pa h, key y alue pa s o he URI. To
build he dic iona ies, we used he U ipa se 2 lib a y,
which is RFC 3968 complian . Du ing he segmen a-
ion p ocess, he pa se iden i ied 169 non-complian
URIs due o he p esence o cha ac e s no pe mi ed
o an inco ec sequence o ields. The egis e s con-
aining hese URIs we e labeled as Ou -o -Speci ica-
ion (OOS) and igno ed in pos e io analysis. Table4
shows he numbe o di e en wo ds lea ned in each
dic iona y (size) and numbe o ins ances o such
wo ds in he URIs ha we e subjec o ocabula y
inspec ion (a e S2).
• Vocabula y inspec ion: The ocabula ies in hese dic-
iona ies we e manually supe ised seeking aces o
a acks. This equi ed a o midable amoun o wo k.
URIs con aining wo ds associa ed wi h a acks we e
labeled acco dingly wi h a ce ain le el o con i-
dence. A p elimina y e iew o he h ee dic iona ies
allowed us o iden i y some issues in he segmen a-
ion ca ied ou in he pa h and key dic iona ies,
which led us o u he classi y some URIs as OOS.
The las wo columns o Table4 show he numbe o
di e en wo ds lea ned in each dic iona y (size) and
numbe o ins ances o such wo ds in he URIs a e
excluding he URIs labeled as a ack o OOS in S3.
The main issues ound in each dic iona y a e summa-
ized bellow ( he in e es ed eade is e e ed o A.1
o u he de ails).
– Key Dic iona y. We iden i ied common pa e ns ol-
lowed by 1579 wo ds (86%) o his dic iona y, which
we e ound o be no mal a e inspec ion (seeA.2).
The emaining wo ds we e inspec ed in sea ch o
po en ially oublesome cha ac e s acco ding o
RFC 3986 such as gen-delims: (: / ? # [ ] @)
o sub-delims (! $ & ’ ( ) * +,; =). As
a esul o his e ision, some URIs we e labeled as
OOS ( u he de ails a e inA.2).
– Pa h Dic iona y. As wi h he p e ious dic iona y,
he sea ch o delimi e s and a p elimina y inspec-
ion o he pa h- ela ed ocabula y e ealed some
encoding issues (labeled as OOS) and seman ic
e o s (see A.3 o u he de ails). We disco e ed
e idence o some a acks based on he use o inco -
ec o malicious encoding. The egis e s con aining
hese malicious wo ds we e labeled as a ack LVL3.
Du ing he examina ion o his leng hy dic iona y,
we iden i ied bu s s o eques s wi h an ob ious
in en o scanning. We labeled hese eques s as
a ack LVL2. Simila ly, we also iden i ied one bu s
wi h iden ical eques s (i.e., e e ing o he same
esou ce), whose URLs we e labeled as a ack wi h
a new con idence le el LVL4. The iden i ica ion o
hese malicious eques s was challenging because
male olen en ies we e in e lea ed wi h no -
mal egis e s, and because each indi idual eques
seemed appa en ly non-malicious. As such, LVL4
a acks a e he leas sound ca ego y. We also iden i-
ied ha bu s s associa ed wi h scans p oduced p e-
dominan ly 300- ype esponses. Due o he impos-
sibili y o de ec ing all bu s s in he en ies and
he ac ha he majo i y o hese scans p oduced
a 300- ype esponse, we inally op ed o including
only egis e s wi h esponse code
<300
in he clean
da ase .
– Value Dic iona y. A pa e n analysis allowed us o
iden i y h ee legi ima e p e alen pa e ns (some-
imes wi h hexadecimal cha ac e s). A delimi e -
based sea ch allowed us o iden i y app oxima ely
1000 (mos ly legi ima e) wo ds. We inally classi ied
as LVL2 a ack hose URIs ha con ained wo ds
wi h he cha ac e ’ a he end o in combina ion
wi h newline. Finally, gi en he modes numbe
o anomalies disco e ed in he wo ds o his dic-
iona y, we analyzed he combina ions key= alue
ex ac ed om he que y pa s o he URI (a o al
o 20,039 unique combina ions). The esul s o his
inspec ion allowed us o iden i y h ee combina-
ions as OSS le els 2 and 4, and h ee key- alue
pai s as OOS2 (one wi h 1037 ins ances).
The main indings o he ocabula y inspec ion (S3)
can be summa ized as:
• se e al URIs did no comply wi h s anda d syn ax
o made an inadequa e use o pe cen encoding.
A ec ed egis e s we e classi ied as OOS1 and OOS2
espec i ely.
• se e al pa h segmen s included a acks based on he
mul iple encoding o he cha ac e ’%’ (%252525...) o
Table 4 Dic iona ies o med om he da ase
Segmen Ini ial (A e S2) Final (A e S3)
Size # Wo ds Size # Wo ds
pa h 51,130 275,392,782 26,488 259,515,518
key 1971 6,823,291 1578 6,689,066
alue 28,513 4,732,008 27,559 4,637,691
2 h ps:// gi hub. com/ u ipa se / u ipa se .
Page 9 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
malicious encoding. A ec ed en ies we e classi ied
as a ack (LVL2 o LVL3).
• de ec ion o bu s s p esumably associa ed wi h DoS
and scanning a acks (LVL2 o LVL4).
• De ec ion o a signi ican numbe (mo e han 55k) o
new indubi able a acks unno iced by he signa u e-
based de ec ion om S1 (LVL1).
Table5 summa izes he esul o his phase. We inally
es ablished ou ca ego ies o a acks (LVL1 o LVL4),
and ou ca ego ies o OOS (OOS1 o OOS4) o ge a
ine -g ain labeling o he URIs ha exhibi issues in hei
syn ax o ep esen a ion. Mo eo e , ou ocabula y anal-
ysis has ad ised o supp ess some en ies based on hei
associa ed esponse code in o de o a ain a mo e qual-
i y clean da ase , since a signi ican pa o he egis e s
associa ed o ResponseCode (RC) 300 we e deemed as
ei he a ack o OOS.
I is wo h no ing ha s age S3 added 74,422 new
a acks o hose iden i ied a e S23 (an addi ional 27%)
which highligh s he impo ance o his phase in he sani-
iza ion p ocess.
Da ase desc ip ion
Following he sani iza ion and labeling p ocess desc ibed
abo e, his sec ion de ails he iles and s uc u e o Bib-
lio-US17, which can be downloaded om Díaz Ve dejo
e al. (2023). A b ie summa y o his sec ion is also a ail-
able a Diaz-Ve dejo e al. (2024b).
Files and o ma ing
The da ase comp ises se e al ex iles o ganized in
di ec o ies. Each line in hese iles e e s o a eco d
h ough a eco d iden i ie RID which ela es i o i s
sou ce log ile and can be aken as a empo al e e ence
o he eco d, indica ing he da e and o de o collec ion.
The RID syn ax is as ollows:
‘[’MM-DD-Fnnnnnn’]’
whe e MM-DD s ands o he mon h and day o he
o iginal log ile, F s ands o he web se ice ype (A o
HTTP o S o HTTPS), and nnnnnn is an in ege ha
indica es he o de o he eques in he o iginal log ile.
An example RID would be [02-18-A001234], which
would co espond o he eques line 1234 om he o ig-
inal ile o he HTTP se ice da ed on Feb ua y 18.
Files a e a anged in di ec o ies and mos ile-
names ha e a common o ma : biblio-2017-mm-
dd.
<ex >
, whe e mm and dd e e o he mon h and
day o he o iginal log ile, and he ex ension
<ex >
de e mines he di ec o y and con en o ma as shown in
Table6.
Table 5 Numbe o URIs labeled du ing ocabula y analysis (S3)
A ack (74,422 egis e s) Ou o speci ica ion (10,352 egis e s)
Label Desc ip ion # Regis e s Label Desc ip ion # Regis e s
LVL1 Undubi uous 55,803 OOS1 Ou o speci ica ion (RFC3986) 169
LVL2 Con ex dependen /scanning 8794 OOS2 Ex ended cha s codi ica ion e o s/no
allowed cha s 2021
LVL3 Codi ica ion a ack 5515 OOS3 Ini ial ’//’ 6567
LVL4 Bu s a ack/DoS 4310 OOS4 Seman ic e o s/o he s 1595
Table 6 Di ec o ies, da a o ma and ile ex ensions o Biblio‑US17
Di Fm Ex Con en
RAW Raw . aw Full da ase : All alid egis e s a e anonimiza ion
LABELS Label .lbl Labels assigned du ing sani iza ion, indexed by [RID]
SID SID .sid Ale s gene a ed by signa u e IDSs, indexed by [RID]
CLEAN Raw .cl Regis e s conside ed clean indexed by [RID]
ATTACK Raw .a Regis e s conside ed a ack (only LVL1).
PARTITIONS Raw . ain . es . al Sugges ed pa i ions o clean eques s o use in ain/
es / alida ion.
AUX Va ious – Auxilia iles ( ules, sc ip s, ools)
3 Mos o hem—55,498—co espond o he use o ’%252525.... Excluding
his a ack, S3 added 15,076 new a acks.
Page 16 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
some imes led o he e ision o he URIs whe e he
wo d is ound) allowed us o inden i y he ollowing:
• a o al o 36 LVL2 a acks,
• 25 URIs did no comply wi h he speci ica ion and
we e labeled as OOS2 as he lack o compliance was
due o encoding issues
• 28 URIs seman ically w ong. These URIs had he
s ing *QUERYSTRINGPII_REMOVED* as key,
p esumably due o some kind o il e ing applied by
he se e o e he aces. As a esul , we added a
new ca ego y o he OOS label, OOS4 which was
used o ma k hese URIs.
I is wo h no hing ha he analysis o he keys x1 and
login allowed us o de ec a acks. In bo h cases, hey
a e con ained in bu s s o eques s ecei ed in June 1s
and June 6 h.
Fu he de ails
Ou sea ch o p oblema ic cha ac e s only p oduced
posi i e esul s o ’;’ and ’=’ in h ee si ua ions:
• The key amp;amp;amp;amp;amp;opción,
which ended up being labeled as OOS2.
• q=node/add was encoded as Q%3Dnode/add,
which was labeled as OOS4.
• 24 URIs did no encoded he cha ac e ’=’ a he
end o he alue ield associa ed wi h a couple o
suspicious keys. We labeled hem as OOS2.
Sea ching key wo ds ha e allowed as o de ec some
a acks o ype 2 in he ollowing cases:
• Que y ’login=cmd’. I is ound epea edly wi h
small a ia ions in he pa h add essed o speci ic
loca ions used by wo dp ess, which led us o classi y
i as a scanning.
• URIS p esumably also associa ed wi h wo dp ess
using he o ma /si es/wdn4.mu.xs/ iles//
wp-admin/[PAGE].php?x1
Pa h dic iona y
We ca ied ou se e al analysis gea ed owa d he de ec-
ion o di e en ypes o anomalies in he ocabula ies.
Fi s , and as a esul o he indings du ing S2, we sup-
p essed om subsequen analysis 18,394 s ings ha con-
ained %252525. The URIs ha con ained hese s ings
we e di ec ly labeled as LVL2 a ack.
Sys ema ic analysis: delimi e s
We sea ched and ound 101 wo ds wi h delimi e cha -
ac e s, which a e inspec ed manually. O hese, 2 a e
associa ed wi h LVL1 a acks, 29 wi h coding p oblems
(OOS2) and 49 wi h seman ic e o s (OOS4).
Use o pe cen encoding
We de ec ed issues ela ed wi h he inco ec use
o pe cen encoding. These issues we e ela ed o an
Fig. 4 Tempo al e olu ion o he ocabula ies lea ned
Table 10 Vocabula y de ails o (p esumably) ile names by
ex ension (a e phase 1)
Ex . #Wo ds #Ocu ences
.png 2334 20,548 545
.css 1710 6,529,799
.pd 1704 332,695
.jpg 1467 2,556,541
.h ml 1452 11,979
.js 1324 8 983,781
.asp(?) 353 2371
.php(?) 321 18,005
.h m 254 2311
.xml 126 749,907
.gi 123 1,031,787
.pp (?) 134 7918
.xls(?) 111 5141
.zip 90 1158
.sw 70 1161
.pps(?) 60 3167
o he s 381 27,279,872
To al 12,041 172,619,409

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Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
ex ensi e use o encoded cha ac e s inside he wo ds
lea ned gi ing ise o excessi e wo d sizes o e one-
ous o impossible encoding (e.g., %EF%BF). Thus, we
ex ac ed and analyzed all wo ds ha included pe cen
encoding (10,738 wo ds). To acili a e manual inspec-
ion, we ca ied ou an i e a i e p ocedu e ha con-
sis s o subs i u ing (o ma king i applies) he di e en
encoding ound s a ing by he longes ones (encoding o
a cha ac e using UTF wi h 3 elemen s) un il he e is no
mo e wo ds wi h encoding cha ac e s. On each case, we
classi y he subs i u ed cha ac e as ei he as (associa ed
wi h) a ack, ou o speci ica ion, o admi ed. In he wo
i s cases, we il e ed he wo d and he co esponding
URIs om he clean da ase . The manual inspec ion o
some o hese URI e idences a acks based on he inco -
ec o malicious use o encoding. As such, a e labeled
as LVL3 such as we did wi h he a ack %2525.... An
example o one o hese egis e would be he ollowing:
On he o he hand, some encoded ex gi es ise o cha -
ac e s ha , al hough co ec om he he speci ica ion
poin o iew, a e seman ically inadequa e ega ding he
con en o he web applica ion. Fo example:
whe e %c3ndice is decoded as Ãndice a he han
índice, which does no make sense.
As a esul , we iden i ied 43 encodings o be excluded
(e.g., %EF%81%BD, %C2%A0) and 5 (%3A, %3E,
%7C, %3C and %ED) equi ed addi ional supe ision.
F om hose o be excluded, 22 a e associa ed wi h a acks
LVL3 and 21 cha ac e s seman ically inadequa e (OOS4).
The encodings equi ing u he supe ision ha e 34 di -
e en wo ds associa ed. Mos o hem, 32 wo ds, ended
[01-01-A016709] GET
/comunicacion/no icias/ca %C3%83%C6%92%C3%82%C2%A1l
ogo-
ama? page=2 HTTP/1.1"
200 22376
[01-03-A065330] GET /a qui ec u a/no icias/%c3ndice-jc -2014 HTTP/1.0" 200
135982
up labeled as OOS4 a e inspec ion. In o al, we iden i-
ied 5518 URIs o ype LVL3 and 396 o ype OOS4.
Sea ch o pa e ns
We iden i ied wo pa e ns ollowed by se e al wo ds:
• ’css_([a-Z]|_|-|[0-9]){40,}.css’,
ound in 1612 wo ds, and
• ’js_([a-Z]|_|-|[0-9]){40,}.js’, ound
in 1238 wo ds.
Bo h pa e ns we e conside ed no mal a e he inspec-
ion o a signi ican numbe o wo ds.
Filenames by ex ension
We ex ac ed he ex s ings ha p eceded ce ain ile
ex ensions. We iden i ied 16 ile ex ensions wi h mo e
han 50 occu ences (see Table10) ha added up a o al
o 8745 di e en wo ds. The emaining ex ensions added
273 di e en wo ds. We inspec ed hese wo ds g ouped
by ex ension seeking anomalies o pa e ns. We did no
ind anomalies o wo ds ha equi ed u he in es i-
ga ion, bu in he case o he ex ensions .php, .zip y
.asp, we did in es iga ed in u he de ail.
In he examina ion o he wo ds and URIs ha co e-
spond o hese ex ensions, we ound some eques s ha
could in end o scan since hey cons i u e a sequence o
eques s ha access o known loca ions om o he po -
als o ools h ough a ia ions in he pa h aimed o con-
i m he exis ence o a esou ce. In some cases, hey seem
o be gea ed owa d ins alling a ool o add-on. A e
con i ming wi h he adminis a o s ha hese eques s
Page 18 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
did no come om hem, and due o he di icul y o de e mining all he bu s in ended o scan, we decided o label
hese bu s o eques s as LVL2.
We also iden i ied bu s s o iden ical ( alid) eques s, which could be aced back o DoS a acks. In his case, we c e-
a ed a new ca ego y, LVL4, o label hese sequence o iden ical eques s. Like in he case o scanning eques s, i was
ha d o us o iden i y all hese bu s s because hey we e equen ly in e lea ed wi h no mal a ic.
Addi ional De ails
Fig. 5 Examples o LVL1 a ack
Fig. 6 Examples o LVL1 a ack
Page 19 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
We ha e iden i ied 835 eques s wi h que ies ha con ained z3=[al anume ic cha s]%3d%3d and
z4=z4=Lw%3d%3d ha a e included in bu s s and a e appa en ly associa ed o so wa e upda es.
Appendix B: Examples o a acks andOOS URIs
Examples o LVL1 a ack iden i ied du ingS2
See Fig.5.
Examples o a acks manually iden i ied du ingS3 (LVL1)
See Fig.6.
Examples o LVL2 a acks manually iden i ied du ingS3
See Fig.7.
Example o abu s a ack
See Fig.8.
Appendix C: Main challenges aced
Below, we ou line some o he signi ican challenges we
encoun e ed and how we add essed hem, p o iding
p ac ical guidance o he esea ch communi y in apply-
ing ou me hodology o c ea e new da ase s om eal-
wo ld da a. Some o hese aspec s ha e been discussed in
mo e de ail h oughou his a icle in he co esponding
sec ions o appendices.
• Da a Volume: The la ge numbe o ex eco ds and
successi e p ocessing phases necessi a ed he use o a
ela ional da abase. To uniquely iden i y eco ds and
acili a e indexing, each collec ed URI was assigned a
unique iden i ie . P ocessing was conduc ed acco d-
ing o labels associa ed wi h a ious p ope ies (e.g.,
de ec ion esul s om S-IDS), using di e en da a-
base ables.
Fig. 7 Examples o LVL2 a ack
Fig. 8 Example o a bu s a ack
Page 20 o 21
Díaz‑Ve dejoe al. Cybe secu i y (2025) 8:38
• De ec ion o Known A acks: Two majo challenges
a ose: some signa u e-based IDS equi e ne wo k
aces as inpu , necessi a ing eques gene a ion
and cap u e, which is compu a ionally expensi e
and p one o a i ac s. Addi ionally, some ools lack
co ela ion be ween ou pu s and inpu s, complica -
ing he iden i ica ion o ale - igge ing eco ds. We
add essed hese by de eloping Inspec o Log, a ool
ha p ocesses log iles and implemen s de ec ion
echniques om online ools, ensu ing clea mapping
be ween ou pu s and inpu eco ds ia unique iden i-
ie s.
• P ep ocessing and Condi ioning: Al hough da a
was p o ided by he ope a o om hei log iles,
i equi ed mino co ec ions due o e o s such as
incomple e lines, imp ope ield delimi a ions, and
inapp op ia e URI alues. The da abase and unique
iden i ie s we e c ucial o e iewing a ec ed eco ds
wi hou ep ocessing all da a.
• Anonymiza ion: P i acy conce ns equi ed ex en-
si e anonymiza ion, guided by an ex e nal audi .
We implemen ed an algo i hm ha modi ies sensi-
i e da a while p ese ing ea u es use ul o de ec-
ion (e.g., special cha ac e s, s ing equencies). The
p ocess was in ensi e, equi ing mul iple i e a ions
and independen e i ica ion be o e app o al by he
Uni e si y’s legal and secu i y eams. E alua ing he
impac o anonymiza ion on he esul s is also neces-
sa y.
• Realism: We minimized modi ica ions o p ese e
he ealism o he da a. All alid eco ds a e p o-
ided in he aw da ase , ega dless o ca ego iza ion.
Despi e no maliza ion by Apache p ep ocesso s,
some acqui ed da a lacked expec ed cha ac e is ics.
We p ese ed URIs as s o ed in he ace ile, e lec -
ing he ac ual se e -p ocessed eques s. Reco ds
wi h esponse codes ou side he 200 ange we e
excluded om he clean da ase bu e ained in he
aw da ase .
• Re ision o Ale s: TP/FP Classi ica ion (Phase S2 o
Labeling): We iden i ied signa u e-based IDS ules
gene a ing TP o FP and analyzed all de ec ed URIs.
SID-based analysis acili a ed his p ocess by e eal-
ing he de ec ion a ionale.
• Manual Supe ision (Phase S3): The mos challeng-
ing ask was he manual supe ision o he ocabu-
la y and associa ed URIs. Supe ising +47 M o
indi idual URIs was impossible, bu o e seeing hei
cons i uen s ings (a ound 80k) was easible. We
employed a ious ools, u ili ies, and Py hon sc ip s
o assis wi h he analysis, sea ch, and compa ison o
s ings. His og ams and wo d/pa e n lis s, such as
a ack-associa ed s ings (e.g., /e c/passwo d)
and alid s ings (e.g., 32 consecu i e hexadeci-
mal cha ac e s ollowed by .css), we e gene a ed
o expedi e he p ocess. In some cases, OSINT was
needed o de e mine he na u e o ce ain URIs. The
e o paid o , as i signi ican ly impac ed he clean
da ase by de ec ing mo e han 74k new a acks wi h
a ious con idence le els.
In summa y, his wo k was a challenging ask equi ing
signi ican expe ise and esou ces.
Acknowledgemen s
We would like o hank he lib a y o he Uni e si y o Se illa and, pa icula ly,
o Claudio A jona, and p o esso s Ra ael Ma inez Gasca and Angel Luis Va ela
o hei suppo in his wo k, e iewing he anonymiza ion p ocess and help‑
ing o achie e adminis a i e pe missions. We also ex end ou g a i ude o he
anonymous e iewe s. Thei eedback has been impo an in enhancing his
pape .
Au ho Con ibu ions
JD‑V has conduc ed he esea ch and has la gely aken he wo k o sani iza‑
ion and o ganiza ion o he da ase . He also has e iewed and co ec ed he
pape and w i en he i s d a . RE has co‑conduc ed he esea ch and has
been ac i e in o ganizing he pape con en , supe ising he anonymiza‑
ion job and he empi ical s udy in sec ion "Da ase desc ip ion". He has also
ca ied ou and w i en he illus a i e case s udy and e ised he pape . AE
has pa icipa ed in he esea ch and is he main w i e o mos sec ions o he
pape . He has also suppo ed some echnical aspec s du ing he empi ical
s udy. JM‑C has ca ied ou he anonymiza ion p ocess, p o ided suppo du ‑
ing he sani iza ion, and has placed he da ase in a public eposi o y. GM has
p o ided echnical suppo wi h he in as uc u e used in he expe imen s
and assis ed in he sani iza ion p ocess.
Funding
This wo k has been pa ly unded by he esea ch G an PID2020‑115199RB‑
I00 p o ided by he Spanish Minis y o Indus y unde he con ac MICIN/
AEI/10.13039 /501100011033.
A ailibili y o da a and ma e ials
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Decla a ions
Compe ing in e es s
The au ho s ce i y ha hey ha e no a ilia ions wi h o in ol emen in any
o ganiza ion o en i y wi h any inancial in e es o non‑ inancial in e es in he
subjec ma e o ma e ials discussed in his manusc ip .
Au ho de ails
1 Depa men o Signal Theo y, Telema ics and Communica ions, Uni e si y
o G anada, G anada, Spain. 2 Depa men o Telema ics Enginee ing, Uni e si y
o Se ille, Se ille, Spain.
Recei ed: 7 Ma ch 2024 Accep ed: 4 No embe 2024
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Publishe ’s No e
Sp inge Na u e emains neu al wi h ega d o ju isdic ional claims in pub‑
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