DIFT-VAR: A DYNAMIC MULTI-LAYER FRAMEWORK FOR DEVICE FINGERPRINTING OF IDENTICAL DEVICES
Abstract
Device fingerprinting is a powerful technique for identifying devices in an IoT environment, offering multiple advantages such as enhanced security through device authentication, improved network management by monitoring device behaviors, and anomaly detection for identifying unauthorized or compromised devices. The majority of recent fingerprinting schemes consider a heterogeneous device environment and use different machine learning techniques to identify devices using network traffic, signal-level information, radio frequency characteristics, etc. However, fingerprinting devices of the same make and model is a significant challenge in modern IoT environments, where many devices often share identical hardware and software configurations. Existing techniques cannot reliably differentiate identical devices as they lack sufficient data. This paper proposes a novel approach for Device Identification and Fingerprinting with Time-Variant Adaptive Recognition (DIFT-VAR) based on multi-layer, time-varying feature extraction. We construct dynamic fingerprints that uniquely identify each device by monitoring and fusing features such as probe request behavior, clock skew, transport layer characteristics, and radio signal metrics over time. We utilize machine learning algorithms such as Random Forests to classify devices based on these dynamic fingerprints. We further propose the use of dynamic time warping (DTW) for feature alignment and classification. Experimental results demonstrate the efficacy of our approach in distinguishing identical devices with an accuracy of over 97% using standard machine learning metrics.
Full text
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4210
DIFT-VAR: A DYNAMIC MULTI-LAYER FRAMEWORK
FOR DEVICE FINGERPRINTING OF IDENTICAL DEVICES
MANOJ KUMAR VEMULA1, KAILA SHAHU CHATRAPATI 2
1Resea ch Schola , Depa men o CSE, JNTUH, Hyde abad, India
2P o esso , Depa men o CSE, JNTUH, Hyde abad, India
E-mail: 1manojkuma [email protected], 2shahujn[email p o ec ed]om
ABSTRACT
De ice inge p in ing is a powe ul echnique o iden i ying de ices in an IoT en i onmen , o e ing
mul iple ad an ages such as enhanced secu i y h ough de ice au hen ica ion, imp o ed ne wo k
managemen by moni o ing de ice beha io s, and anomaly de ec ion o iden i ying unau ho ized o
comp omised de ices. The majo i y o ecen inge p in ing schemes conside a he e ogeneous de ice
en i onmen and use di e en machine lea ning echniques o iden i y de ices using ne wo k a ic, signal-
le el in o ma ion, adio equency cha ac e is ics, e c. Howe e , inge p in ing de ices o he same make
and model is a signi ican challenge in mode n IoT en i onmen s, whe e many de ices o en sha e iden ical
ha dwa e and so wa e con igu a ions. Exis ing echniques canno eliably di e en ia e iden ical de ices as
hey lack su icien da a. This pape p oposes a no el app oach o De ice Iden i ica ion and Finge p in ing
wi h Time-Va ian Adap i e Recogni ion (DIFT-VAR) based on mul i-laye , ime- a ying ea u e
ex ac ion. We cons uc dynamic inge p in s ha uniquely iden i y each de ice by moni o ing and using
ea u es such as p obe eques beha io , clock skew, anspo laye cha ac e is ics, and adio signal me ics
o e ime. We u ilize machine lea ning algo i hms such as Random Fo es s o classi y de ices based on
hese dynamic inge p in s. We u he p opose he use o dynamic ime wa ping (DTW) o ea u e
alignmen and classi ica ion. Expe imen al esul s demons a e he e icacy o ou app oach in
dis inguishing iden ical de ices wi h an accu acy o o e 97% using s anda d machine lea ning me ics.
Keywo ds: De ice inge p in ing, IoT Secu i y, Dynamic ime wa ping (DTW), Time- a ian ea u e
ex ac ion, Machine lea ning o IoT secu i y.
1. INTRODUCTION
An IoT (In e ne o Things) de ice is a physical
objec embedded wi h senso s connec ed o he
in e ne . This physical de ice di e s om any
ypical compu e whose p ima y unc ionali y is no
compu ing. IoT de ices connec ed o he in e ne
ha e many ad an ages, including con enience,
com o , sa e y, secu i y, eliabili y, e c. The IoT
ma ke is a apidly expanding indus y ha co e s a
wide ange o applica ions ac oss di e en business
e icals, om e ail o heal h, anspo ,
manu ac u ing, en e ainmen e c. Ensu ing he
secu i y o IoT de ices is essen ial o hei
widesp ead adop ion. Due o de ice cons ain s,
adi ional secu i y solu ions o con en ional
compu ing pa adigms canno be di ec ly applied o
IoT. Addi ional challenges include scalabili y,
ope a ing en i onmen s, di e se de ice
a chi ec u es, pla o ms, and p o ocols wi hin he
IoT ecosys em. No ably, S a is a p ojec s ha by he
end o 2024, o e 50 billion IoT de ices will be
connec ed o he in e ne . P o ocols, a chi ec u es,
and pla o ms employed by hese de ices a y
g ea ly, and wi h a sho p oduc de elopmen li e-
cycle, he numbe o endo s manu ac u ing hese
p oduc s is nume ous. These challenges and IoT's
open ope a ing en i onmen pose se e al secu i y
challenges. Au hen ica ing a de ice be o e
p o iding i wi h ne wo k access is he i s line o
de ense o p o ec agains secu i y a acks in he
cybe wo ld. E en hough many c yp og aphic
schemes ailo ed o he IoT en i onmen ha e been
de eloped, hese schemes (excep hose ha use
PKI ce i ica es) a e ulne able o node o ge y o
impe sona ion a acks, whe e he iden i y o he
o he legi ima e de ices is employed [1], wi h
sec e -keys (as secu i y c eden ials) being he mos
popula way o au hen ica ing a de ice, weak
passwo ds ollowed by unpa ched de ices wi h
epo ed secu i y ulne abili ies subjec IoT de ices
o comp omise. Once comp omised, hese de ices
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can be exploi ed o leak con iden ial in o ma ion,
used as launch pads o launch la ge-scale a acks
( o example, he ecen Mi ai Bo ne ), a ge
c i ical in as uc u e, e c. The isk o a acks is
signi ican ly heigh ened as de ices a e suscep ible
o hacking, comp omising, and e e se enginee ing,
as well as inadequa e secu i y managemen
mechanisms in wi eless ne wo k sys ems. Thus,
mul iple app oaches a e equi ed o secu e IoT
de ices, anging om design conside a ions o
moni o ing o po en ial a acks and implemen ing
e ec i e mi iga ion s a egies.
In a ne wo k, de ices a e usually iden i ied using
IP add esses, MAC add esses, de ice se ial
numbe s, e c. The limi a ion o hese iden i ie s is
ha hey a e suscep ible o spoo ing. The abili y o
spoo add esses o mul iple legi ima e de ices
allows he a acke o launch mo e sophis ica ed
a acks wi hou being de ec ed. Recen ly, de ice
inge p in ing has eme ged as an al e na i e
app oach o iden i y de ices, in which unique
cha ac e is ics o a de ice a e used o gene a e
de ice signa u es and used o iden i y de ices [6].
The p emise is o ex ac de ice ea u es o o
de i e pa e ns h ough communica ions wi h he
de ices. Di e en ne wo k laye s can con ibu e o
his ea u e se , which can help in de eloping a
inge p in o he de ice. De ice inge p in ing can
be used no only o iden i y de ices, bu also o
au hen ica e hem, p o ided he inge p in is
unique [2]. Fu he mo e, a ew wo ks [3], [4], [5]
ha e also employed inge p in ing o de ec hidden
ea esd oppe s like a hidden came a, a ne wo k
ea esd oppe , o any such passi e wi eless de ices
o p o ec p i acy. Also, iden i ying a de ice ype
helps in moni o ing he beha io o a pa icula
de ice, he eby di e en ia ing he anomalous
beha io om no mal beha io . I also helps in
isola ing malicious nodes by main aining an asse
lis . Unique inge p in s employed o
au hen ica ion p e en he de ice iden i ies om
being o ged.
The majo s eps in ol ed in he inge p in ing
p ocess a e: (1) iden i ying he ele an ea u es, (2)
ex ac ing and modelling hem, and (3) iden i ying
he de ice. Employing sui able machine lea ning
algo i hms o achie e he abo e has made de ice
inge p in ing an e ec i e echnique o add ess he
unique secu i y challenges posed by IoT ne wo ks.
The exis ing inge p in ing echniques can be
ca ego ized based on he ea u es employed o
iden i ying de ices: o example, ne wo k-based
inge p in ing echniques ely on ne wo k a ic
pa e ns [4], Wi-Fi based echniques use medium
access con ol (MAC) sub-laye in o ma ion [5],
clock-skew based me hods use sligh a ia ions in
clock o each de ice[8], me hods based on
elec omagne ic emana ions (EME) use unique
signals om de ice componen s [7], and adio
equency (RF) based echniques use physical laye
in o ma ion like signal- o-noise a io and o he
simila ac o s [8]. All hese echniques ely on
some ype o machine lea ning algo i hm o classi y
and iden i y de ices.
Howe e , he majo challenge is o di e en ia e
de ices ha a e iden ical in e ms o ha dwa e and
so wa e con igu a ions. As he majo i y o he IoT
p oduc s a e mass-manu ac u ed, hey end o be o
iden ical con igu a ion, which makes he issue o
de ice iden i ica ion mo e challenging [9].
1.1. Mo i a ion And P oblem S a emen
Typically, IoT de ices deployed o a speci ic
applica ion, such as in sma homes, indus ial
moni o ing, o heal hca e en i onmen s, a e
comp ised o de ices ha may be iden ical in e ms
o ha dwa e and so wa e con igu a ions. The
limi a ions o s a ic ne wo k iden i ie s c ea e a
signi ican challenge in iden i ying and
di e en ia ing hese de ices once hey a e
ope a ional wi hin he same ne wo k. He e, he ask
is o uniquely inge p in IoT de ices o he same
make and model wi hou elying on EME, which
o en equi es specialized ha dwa e and is
imp ac ical in la ge-scale deploymen s. E en
hough he de ices a e iden ical in bo h ha dwa e
and so wa e, hey s ill exhibi sub le di e ences in
ne wo k beha io , communica ion iming, and
usage pa e ns due o na u al a ia ions in
manu ac u ing, en i onmen al ac o s, and in e nal
p ocessing s a es. The majo i y o he exis ing
wo ks ocused on he he e ogeneous ype o de ices
and ha e p oduced signi ican de ice p o iling and
au hen ica ion app oaches. Mo eo e , we highligh
he wo k by collec ing minu e di e ences in
signi ican pa ame e s wi h he ne wo k is in
ope a ion om he a ious homogeneous de ices o
add ess he au hen ica ion issues. In addi ion o his,
he p oposed model has p oduced angible esul s in
e ms o inge p in ing iden ical de ices. As he
model ocused on ime se ies da a, which is
collec ed wi h a ia ion in ime slo s.
This wo k aims o design a no el
inge p in ing me hod o cap u e and analyze hese
sub le, na u ally occu ing a ia ions in de ice
beha io o uniquely iden i y each de ice. This
inge p in ing me hod should in eg a e mul i-laye
ea u es o iden i y and inge p in de ices.
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1.2 Con ibu ions
This pape makes he ollowing con ibu ions:
We in oduce a no el mul i-laye app oach o
inge p in ing iden ical de ices, le e aging
ime- a ying ea u es ac oss he MAC,
anspo , and physical laye s.
A dynamic ime wa ping (DTW)-based
algo i hm is p oposed o empo al alignmen
and classi ica ion o de ice inge p in s.
We demons a e h ough ex ensi e expe imen s
ha ou app oach can success ully di e en ia e
de ices o he same make and model, using
eal-wo ld IoT da a.
2. RELATED WORKS
De ice iden i ica ion and inge p in ing
a e key o imp o e he secu i y pos u e in an IoT
en i onmen whe e de ices a e suscep ible o
comp omise. In his pape , we p esen di e en
wo ks in his di ec ion o iden i ying IoT de ices,
ocusing on hei e ec i eness, echnical de ails,
s eng hs, and limi a ions. One o he simple
app oaches o iden i ying de ices is based on
de ice signa u es. These me hods c ea e ules based
on known de ice beha io s, which can be e ec i e
o iden i ica ion [10], [11]. Howe e , hese
echniques equi e cons an upda es o adap o new
de ices and h ea s. Al e na i ely, he ne wo k
a ic gene a ed by IoT de ices can be used o
lea n unique pa e ns ha can be used o classi y
and iden i y de ices. Mul iple such wo ks use
ne wo k a ic o de ice iden i ica ion [12], [13],
[14], [15], [16]. Fo example, he au ho s o [12]
use machine lea ning-based me hods o iden i y IoT
de ices h ough ne wo k a ic analysis om a es
se o nine de ices. The model achie es 99.28%
accu acy in dis inguishing de ices u ilizing ne wo k
ea u es. I pe o ms session-le el analysis and uses
a mul is age classi ie o speci ic IoT
iden i ica ion.
Simila ly, he wo k p esen ed in [13] also
uses di e en ne wo k a ic pa e ns such as ypes
o a ic (e.g. di e en p o ocols), packe sizes, he
equency o packe s along wi h hei s a is ical
measu es, packe in e -a i ing imes, e c. o
iden i y de ices using machine lea ning algo i hms
and hen apply p ede ined secu i y measu es
ollowing a secu i y policy. In [14], he au ho s
add ess he challenge o managing a la ge numbe
o IoT de ices in a la ge-scale IoT en i onmen
such as a sma ci y. The p esen ed wo k uses
ne wo k a ic analy ics o cha ac e ize and
moni o IoT de ice beha io by collec ing a ic
aces om a di e se se o IoT de ices ha include
sma came as, sma ligh s, and heal h moni o s.
Fea u es such as da a a es, ac i i y cycles, and
signaling pa e ns we e employed o i s
dis inguish IoT om non-IoT a ic and hen
iden i y speci ic IoT de ices wi h mo e han 95%
p ecision. O he wo ks based on ne wo k
cha ac e is ics, like [15], use gene ic algo i hms o
ea u e selec ion and di e en machine lea ning
algo i hms o de ice classi ica ion. On he o he
hand, he au ho s in [16] add ess he p oblem o
da a imbalance in de ice classi ica ion. All o he
abo e-men ioned app oaches use di e en ne wo k
cha ac e is ics and ailo hei ML app oaches o
de ice classi ica ion and iden i ica ion.
O he app oaches o de ice iden i ica ion
a e ei he RF-based, clock-skew based, o
specialized ha dwa e-based echniques ha ha e
hei espec i e ad an ages and limi a ions. RF-
based de ice iden i ica ion schemes le e age he
unique cha ac e is ics o he adio signals, such as
signal's ampli ude, equency, and phase
cha ac e is ics emi ed by IoT de ices, hen apply
ML algo i hms o c ea e a dis inc inge p in o
each de ice. The au ho s o [17], [18] p esen a
su ey o such app oaches. In [19], he au ho s
p opose o exploi he unique cha ac e is ics ound
in he ene gy spec um o ansmi e u n-on
ansien signals. These ansien signals con ain
unique, ha dwa e-speci ic a ia ions ha se e as
inge p in s o de ice iden i ica ion. The me hod
ex ac s he ene gy dis ibu ion ac oss di e en
equency componen s om hese ansien s, which
helps o dis inguish be ween de ices, e en a low
signal- o-noise a ios. In [20], he au ho s p esen
an app oach ha con e s he ime se ies da a in o
images ins ead o di ec ly using aw signals o
ex ac ing s a is ical ea u es om hem. This
ans o ma ion allows he use o well-es ablished
image-p ocessing echniques and machine lea ning
models ha a e pa icula ly powe ul o pa e n
ecogni ion. App oaches based on clock skew ake
ad an age o minu e di e ences in he in e nal
clock a es o de ices o c ea e unique iden i ie s
[21], [8]. This is done by moni o ing he ne wo k
packe s sen by de ices o e ime o calcula e he
de ia ion o each de ice's clock om a e e ence
clock. Then, a unique p o ile is c ea ed based on he
consis en skew pa e ns obse ed. A su ey o
such app oaches is p esen ed in [18]. Mo e
ecen ly, a clock skew-based de ice iden i ica ion
scheme combined wi h a emo e a es a ion
p o ocol has been p esen ed in [22] o class-I IoT
de ices (de ices wi h andom access memo y less
han 10 KB and code size less han 100 KB). The
majo challenges wi h he clock skew-based
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app oaches a e ha hey need con inuous
moni o ing o e an ex ended pe iod o ime and a e
a ec ed by ne wo k la ency and ji e .
Apa om he a o emen ioned de ice
iden i ica ion app oaches, ew o he wo ks conside
a hyb id app oach [23], ha is o combine mul iple
de ice cha ac e is ics and o he uses de ice senso s
o inge p in ing [24]. Howe e , none o he abo e
app oaches conside mul iple iden ical de ices wi h
he same ha dwa e and i mwa e in hei es se up
and e alua e he de ice iden i ica ion and
inge p in ing app oaches. In his wo k, we p opose
a no el app oach o De ice Iden i ica ion and
Finge p in ing wi h Time-Va ian Adap i e
Recogni ion (DIFT-VAR) ha is based on mul i-
laye , ime- a ying ea u e ex ac ion. we cons uc
dynamic inge p in s ha uniquely iden i y each
de ice by moni o ing and using ea u es such as
p obe eques beha io , clock skew, anspo laye
cha ac e is ics, and adio signal me ics o e ime.
Al e na i ely, o he wo ks ha e explo ed he issue
o inge p in ing in a he e ogeneous en i onmen .
Fo example, he wo k p esen ed in [25] p esen s a
me hod o classi y IoT de ice ypes (e.g., came as,
ou e s, p in e s) using Shodan me ada a. I
emphasizes on he e ec i eness o using cu a ed
me ada a and machine lea ning o accu a e
iden i ica ion o IoT de ice ypes. The pape [26]
explo es he use o ICMP and IP imes amp
esponses o dis inguish be ween physical And oid
de ices and i ual machines. Designed o de ec
VM-based malwa e e asion, he s udy e eals
consis en iming disc epancies ha se e as
passi e indica o s o he execu ion en i onmen .
The pape in [27] employs inge p in s o IOT
de ices. I is ne wo k beha io -based, ex ac ing
ea u es om he ne wo k, T anspo , and
applica ion laye s, and has also employed neu al
ne wo ks o classi ica ion. Employed neu al
ne wo k o classi y IoT de ices by u ilizing 3
s ages, i.e, de ice ype, endo , and p oduc . He has
employed a glo e and a Bi-LSTM o he
applica ion laye da a. De ice beha io , howe e ,
a ies as when i mwa e upda es. The pape [28]
u ilizes he MRFE deep lea ning app oach o
ecognize IoT de ices using RF inge p in s. The
model imp o es he iden i ica ion accu acy wi h
mul i-dimensional ea u es. The da a se was
acqui ed in noise noise- ee en i onmen . The
model uses a inge p in -ampli ying laye , h ee-
channel inpu , an A en ion mechanism, and
esidual connec ions and ully connec ed laye s.
The model depends mo e on he beha io o he RF
da a. Howe e , he pe o mance o he model d ops
o below 8dB o SNR
3. DIFT-VAR: THE PROPOSED
FINGERPRINTING TECHNIQUE
De ice iden i ica ion in a he e ogeneous
IoT en i onmen is compa a i ely simple as he
de ices exhibi su icien ly a ied cha ac e is ics
ha can be cap u ed ia ne wo k, RF, de ice
beha io , o clock skew, and o he simila
app oaches. Howe e , in an IoT en i onmen whe e
mul iple de ices sha e he same de ice a chi ec u e
in e ms o bo h ha dwa e and i mwa e, he issue
o de ice iden i ica ion becomes a challenge. The
in e ac ion o hese de ices in a ne wo k exhibi s
almos simila pa e ns ha a e ha d o dis inguish,
especially when he de ices a e ope a ional on he
same applica ion. A ypical use-case scena io is a
su eillance sys em whe e a se o sma secu i y
came as can belong o a single manu ac u e . The
su eillance applica ion necessi a es ha all de ices
epo su eillance da a ha can exhibi simila
communica ion pa e ns. In such a scena io, he key
is o exploi sub le a ia ions in hei beha io ha
a ise om ha dwa e impe ec ions, ne wo k
condi ions, and en i onmen al ac o s. In his wo k,
we p opose a no el me hod o p ocessing he da a
by le e aging mul i-laye da a usion combined
wi h ime- a ying ea u e ex ac ion o c ea e
unique de ice inge p in s o e ime. The co e idea
is o design a mul i-laye , ime- a ying inge p in
Cons uc ion me hod. Ra he han elying on a
s a ic ea u e se , he p oposed app oach ocuses on
he empo al e olu ion o ea u es ac oss mul iple
laye s (MAC laye , adio cha ac e is ics, anspo
laye , and iming da a) o di e en ia e de ices. The
no el y is in dynamically combining empo al
ends and sub le a ia ions in ne wo k and iming
beha io o e ime, a he han s a ic snapsho s o
ea u es.
The p oposed app oach is named DIFT-
VAR, whe e a ime- a ying mul i-laye
inge p in ing app oach ha uses ea u es ac oss
he MAC, anspo , and iming laye s. Ou
app oach consis s o h ee main componen s: mul i-
laye ea u e ex ac ion, ime- a ying ea u e
agg ega ion, and classi ica ion using dynamic ime
wa ping (DTW). The uniqueness lies in he
p ocessing me hod, speci ically in di e en ia ing
iden ical de ices based on a empo al dependency
analysis using dynamic ime wa ping (DTW) and
ecu en neu al ne wo ks (RNN) wi h an emphasis
on sequence p ocessing.
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3.1 Mul i-Laye Fea u e Fusion and Tempo al
Va ia ion T acking.
As iden ical de ices may exhibi sligh
ha dwa e-induced a ia ions o e ime due o
en i onmen al ac o s (like dis ance om he Wi-Fi
access poin ), small a ia ions in he in e nal clock,
o p ocessing speed di e ences ha a ise om
manu ac u ing impe ec ions, we do no ely on a
ixed da ase o ea u es collec ed a one poin in
ime. We conside he e olu ion o ea u es o e
ime and use da a ac oss mul iple laye s (MAC,
adio, anspo , and iming) o c ea e a highly
unique inge p in .
A he MAC laye , we conside he MAC add ess
and he ecei ed signal s eng h indica o (RSSI) o
p obe eques s. The MAC add ess is conside ed a
basic iden i ie . E en hough i can easily be
spoo ed, i is conside ed a s a ing poin . We
conside he Wi-Fi communica ion among de ices
whe e de ices pe iodically send p obe eques s o
ne wo k disco e y and e-associa ion. Le
( )
k
i
T
ep esen he in e al be ween he
i- h
and
(i+1)- h
p obe eques s o he de ice
k
. To compa e
de ices, we cons uc a ime-se ies ec o and use
Dynamic Time Wa ping (DTW) o calcula e he
simila i y be ween wo ime se ies. ( ) ( )
( ( , ) :
k m
ST T
( ) ( ) ( ) ( )
1
( , ) min ( , )
n
k m k m
i j
i
DTW d T T
T T
whe e
( , )
d
I is a dis ance me ic ( ypically
Euclidean dis ance).
The ime in e als be ween consecu i e
p obe eques s can a y sligh ly due o in e nal
ha dwa e di e ences and he iming p ecision o
each de ice. The p obe eques s a e analyzed o
suppo ed da a a es, SSID, and capabili ies. E en
iden ical de ices can ha e sub le di e ences based
on i mwa e o en i onmen al adap a ions. The
iming o p obe eques s is used o gene a e a
empo al pa e n o each de ice, which is hen
compa ed using a dynamic ime-wa ping app oach
o iden i y sub le di e ences in eques in e als.
The RSSI alues a e cap u ed o packe s
be ween he de ices and he Wi-Fi access poin .
These alues a e collec ed con inuously since
physical placemen and an enna di e ences cause
a ia ions. Fo signal- o-noise a io (SNR), he
signal quali y ela i e o he backg ound noise is
measu ed. The di e ences in he de ice's RF
componen s can esul in sligh bu de ec able
a ia ions in SNR. Also, as each de ice may ha e
sligh a ia ions in ansmission powe , i can lead
o di e ences in RSSI o e ime. We se up
mul iple access poin s o measu e how equen ly
he de ices swi ch channels when nume ous access
poin s a e a ailable, conside ing hei channel
u iliza ion capabili ies.
Le
( ) ( ) ( ) ( )
1 2
[ , , ,
]
k k k k
n
RSSI be
he RSSI eadings o he de ice
k
. Fo
di e en ia ion, calcula e he a iance o e a sliding
window o size
w :
( ) ( ) 2 ( )
1 1
1 1
( ) , whe e
w w
k k k
RSSI i RSSI RSSI i
i i
w w
To cap u e changes o e ime, gene a e an
agg ega ed ea u e ec o o each ime window:
( ) (1) (2) ( )
[ , , , ]
k m
RSSI RSSI RSSI RSSI
F
The RSSI and SNR a e collec ed as ime-
se ies da a. This in o ma ion is p ocessed using
sequence models o iden i y he d i in signal
s eng h o noise condi ions, p o iding unique
iden i ie s o each de ice ega ding hei physical
connec i i y o he ne wo k.
We conside he TCP window size a he
anspo laye le el and moni o he changes
du ing da a exchanges. TCP sequence numbe s a e
obse ed du ing he handshake and communica ion
phases. The di e ence be ween sequence numbe s,
o he sequence numbe gaps, can a y depending
on how he de ice's ne wo k s ack handles
e ansmissions and acknowledgmen s.
Le he TCP sequence numbe s be
ep esen ed as
( ) ( ) ( ) ( )
1 2
,[ , , ]
k k k k
n
seq seq seq SEQ
Using DTW o alignmen o sequence
numbe s o iden i y sub le de ice-speci ic
a ia ions, i is ep esen ed as ollows:
( ) ( ) ( ) ( )
pa hs
1
( , ) min
n
k m k m
i j
i
DTW seq seq
SEQ SEQ
‖ ‖
We conside he ound- ip ime (RTT) o
TCP connec ions and consis en ly measu e he RTT
be ween he de ices and a se e on he ne wo k.
I s alue a ies based on clock skew and p ocessing
speed di e ences. The TCP sequence numbe and
window size a e modeled as ea u es con ibu ing o
ne wo k beha io di e ences. The RTT is ea ed
as empo al da a and is con inuously moni o ed o
build a clock skew p o ile o e ime.
The RTT is ep esen ed as
( )
k
i
RTT o
de ice
k
. Using he ime-a e aged mean and
a iance:
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4215
( ) ( ) ( ) ( ) ( ) 2
1 1
1 1
, ( )
n n
k k k k k
RTT i RTT i RTT
i i
RTT RTT
n n
Clock skew
C( )
is he de ia ion o he
clock o e ime. The clock skew is measu ed by
sending ICMP pings o each de ice and eco ding
he ound- ip ime (RTT). The d i o e ime is
calcula ed o p oduce a clock skew alue ha is
unique o each de ice. On he o he hand, he in e -
a i al ime (IAT) o packe s, pa icula ly du ing
high ac i i y, is measu ed. The clock skew unc ion
o he de ice
k
can be ep esen ed as:
( )
k k k
C
whe e
k
is he d i a e and
k
ep esen s
he ini ial o se . Compa ing clock skews o wo
de ices in ol es compu ing he di e ence:
,
( ) | ( ) ( ) |
k m k m
C C C
Di e ences in clock d i and packe
p ocessing speed esul in de ec able a ia ions in
packe iming. The clock skew and IAT a e
analyzed as ime-se ies ea u es, p o iding a unique
empo al signa u e o each de ice.
3.2 Time-Va ying Fea u e Agg ega ion
The p oposed app oach, algo i hm 1,
conside s ea u es collec ed o e ime a he han
s a ic ea u es collec ed a a single ime ins ance.
This enables us o cap u e empo al pa e ns in
ne wo k beha io ha a e sub le bu consis en
ac oss iden ical de ices. We show ha ime- a ying
pa e ns o en e eal mino di e ences due o
en i onmen al o ha dwa e ac o s. To achie e ha ,
we con e key ea u es in o ime se ies da a and
apply ime se ies analysis echniques like mo ing
a e ages, Fou ie ans o ms, and wa ele analysis
o cap u e empo al ends in ea u e beha io . The
me hod chosen is dependen on he ea u e ype.
Fo example, o analyze he RSSI alues ha
luc ua e due o en i onmen al condi ions, we
conside mo ing a e ages o smoo h ou he signal
s eng h a ia ions.
On he o he hand, o analyze p obe
eques in e als and suspec ha a de ice sends
eques s a egula in e als, we apply a Fou ie
ans o m ha will highligh his cyclical pa e n.
The Fou ie spec um will peak a he
co esponding equency, hus allowing he
di e en ia ion o de ices based on hei pe iodic
beha io . Fo ea u es such as clock skew ha
exhibi a long- e m d i sp ead ac oss wi h
occasional a ia ions due o in e nal p ocessing
delays, we employ wa ele analysis ha allows us
o cap u e bo h he g adual changes and he sudden
shi s in iming beha io .
Algo i hm-1 Mul i-Laye De ice Finge p in ing
wi h Tempo al Sequence Analysis
1. Inpu :
2. Se o
N
de ices 1 2
{ , , , }
N
D D
D
3. Fo each de ice
i
D
, collec ime-se ies
da a
i
X :
4. MAC laye ea u es
i
MAC
5. Radio cha ac e is ics ,
i i
RSSI SNR
6. T anspo laye ea u es
, ,
i i i
TCPSeq RTT TCPWnd
7. Timing ea u es
,
i i
P obe In e al Clock Skew
8. Fea u e Ex ac ion:
9. Fo each de ice
i
D ,
ex ac ime-se ies
ea u es:
10. MAC laye ea u es:
,
i i
MAC P obe In e al
11. Radio cha ac e is ics: ,
i i
RSSI SNR
12. T anspo laye ea u es:
, ,
i i i
TCPSeq RTT TCPWnd
13. Timing ea u es:
i
Clock Skew
14. Time-Se ies Alignmen Using DTW:
15. Fo each de ice
i
D
and ea u e
i
X
,
pe o m DTW on sequences o :
16. ,
i i
P obe In e al
TT
R
17. Compu e he DTW dis ance o align iming
a ia ions:
18.
( )
DTW i
S X
19. Tempo al Modeling Using LSTM:
20. T ain an LSTM on ime-se ies da a
Xi
o
each ea u e:
21. , , ,
i i i i
RSSI RTT TCPSeq Clock Skew
22. Ou pu a sequence embedding
( )
LSTM i
E X
o each de ice
23. Fea u e Fusion:
24. Fuse all ea u es in o a ea u e ec o
Fi
:
, , ,
( ), ( ),
, ,
i i i
i DTW i LSTM i
i i i
MAC RSSI SNR
F S X E X
TCPSeq RTT TCPWnd
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4216
25. Classi ica ion:
26. T ain a Random Fo es classi ie using he
ea u e ec o s
i
F
27. Au hen ica ion:
28. Gi en an unknown de ice
j
D
, ex ac he
ea u e ec o
j
F
29. Classi y
j
F
using he ained classi ie o
au hen ica e
j
D .
30. Ou pu : De ice classi ica ion and unique
inge p in o au hen ica ion.
Fu he , we employ a sliding window
app oach whe e he key ea u es a e calcula ed
dynamically wi hin ime windows a he han
s a ic ea u e ec o s, hus c ea ing a empo al
inge p in wi h dynamic windows. Fo a ime-
a ying ea u e ( )
( )
k
X
, we ex ac ea u es using
a sliding window app oach o size
w
.
( ) ( )
1
( ) ( )
w
k k
window
i
X X i
w
Fo each ime window, we cap u e and
summa ize a ia ions in key ea u es like clock
skew, RSSI, TCP beha io , and p obe eques
iming. Fo each o he de ice, he agg ega e
ea u es om mul iple laye s wi hin a dynamic ime
window c ea es a composi e inge p in . We
employ a weigh ed app oach o gi e mo e
impo ance o laye s ha exhibi g ea e a iabili y
be ween de ices. Fea u es calcula ed o each
sliding window a e used o gene a e dynamic
inge p in s ha emphasize a iabili y o e ime.
The key componen s o he p oposed a chi ec u e
a e shown in Figu e 1.
3.3 Mul i-Resolu ion Fea u e Ma ching
To uniquely di e en ia e de ices, he
p oposed app oach pe o ms a mul i- esolu ion
analysis, whe e ea u es a e compa ed a di e en
le els o g anula i y. To begin wi h, o each o he
de ices, we c ea e bo h sho - e m and long- e m
ea u e p o iles. The sho - e m p o iles cap u e
immedia e luc ua ions, while long- e m p o iles
ack cumula i e beha io o e an ex ended pe iod
o ime. Then, we aim o di e en ia e de ices based
on a combina ion o sho - e m and long- e m
p o iles using echniques like dynamic ime
wa ping (DTW) o align and compa e ime se ies
da a, accoun ing o sligh ime shi s o phase
di e ences.
We de ine he sho - e m
( )
k
S and long-
e m
( )
k
L
ea u e ec o s o each de ice
k
as
ollows:
( ) ( ) ( ) ( ) ( ) ( ) ( ) ( )
1 2 1 2
[ , , , ], [ , , , ]
k k k k k k k k
n m
s s s l l l
S L
DTW is used o measu e he simila i y be ween
empo al sequences o di e en de ices. DTW
allows o alignmen o ime-se ies da a ha migh
be ou o phase bu s ill ep esen simila pa e ns.
We apply DTW o align and compa e he de ice
p o iles using:
( ) ( ) ( ) ( )
( , ) ( , )
k m k m
DTW DTWS S L L
3.4 Fea u e Fusion and Classi ica ion
Fo di e en ia ing and iden i ying de ices
we employ a ea u e usion app oach. In his
me hod, we combine ea u es om all laye s
(MAC, adio, anspo , and iming) o c ea e a
comp ehensi e ea u e ec o o each de ice. Fo
any ea u e ec o , le
( )
k
F
ep esen he ea u e
ec o o de ice
k
, con aining ea u es om
MAC, adio, anspo , and iming laye s:
( ) ( ) ( ) ( ) ( ) ( )
[ , , , , ]
k k k k k k
MAC adio anspo iming LSTM
F F F F hF
The weigh ed usion o ea u es, whe e weigh
i
w
co esponds o he a iabili y o each ea u e is
ep esen ed as:
( ) ( )
, 1
k k
composi e i i i
i i
F w F wi h w
Nex , we employ he Random Fo es
classi ie s o ain on he used ea u e se . The
LSTM ou pu ( empo al analysis) is also included
in he ea u e se , p o iding addi ional empo al
con ex o he classi ie . The used ea u es a e used
o c ea e a unique inge p in o each de ice. The
aining o he Random Fo es classi ie on he
used ea u e ec o s
( )
k
F
is ep esen ed as ollows,
whe e
ˆ
y
is he p edic ed class:
( )
ˆ
( )
k
y Classi ie F
The combined use o ime-se ies alignmen
(DTW), empo al sequence modeling (LSTM), and
laye -speci ic ea u es makes he p oposed
app oach unique.
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4217
3.5 De ice Iden i ica ion
The p oposed app oach iden i ies de ices
by collec ing da a o e an ex ended pe iod and
c ea ing a de ice p o ile. Con inuous da a
collec ion in ol es con inuously ga he ing ea u es
ac oss all ele an laye s o main ain bo h sho -
e m and long- e m p o iles o each de ice. In he
ma ching and classi ica ion p ocess, DTW is
applied o align newly collec ed ime-se ies da a
wi h s o ed p o iles. An LSTM model is used o
in e empo al dependencies in eal- ime ea u es.
The aligned da a and LSTM ou pu s a e hen used
and classi ied using a ained Random Fo es
classi ie o au hen ica e he de ice. In he decision-
making s age, au hen ica ion is de e mined based
on he DTW alignmen sco e and he Random
Fo es classi ica ion. I is ep esen ed as ollows:
De ice au hen ica ion is based on compa ing he
DTW alignmen sco e DTW
Sco e
and classi ica ion
ou come:
ˆ
DTW ue
Au hen ica e i Sco e and y y
I he compu ed sco e is below he es ablished
h eshold, he de ice is conside ed un ecognized o
spoo ed.
Fig. 1 The p oposed de ice iden i ica ion model
4. EXPERIMENTAL RESULTS
To analyze he pe o mance o he
p oposed de ice iden i ica ion and inge p in ing
algo i hm, we se up a ne wo k o 10 ESP32-
WROOM-32 mic ocon olle s, all unning he same
i mwa e and communica ing o e a Wi-Fi
ne wo k. Each de ice has he same se o senso s
( empe a u e, humidi y, ligh ), which epo da a
e e y 60 seconds. This expe imen aims o e alua e
he abili y o uniquely inge p in iden ical de ices
using mul i-laye ea u e ex ac ion and ime-se ies
p ocessing echniques. The expe imen will cap u e
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4218
he MAC laye , adio cha ac e is ics, anspo
laye , and iming ea u es, hen p ocess he da a
using Dynamic Time Wa ping (DTW) and LSTM-
based empo al modeling.
4.1 Ha dwa e Se -up
Table 1 p o ides a de ailed ESP32-
WROOM-32 mic ocon olle speci ica ion
summa y. The boo p ocess in ol es he i s -s age
boo loade , pa o he ROM. The second-s age
boo loade is s o ed in Flash and can be
cus omized. The pa i ion able de ines he lash
memo y layou , and he applica ion is pa o he
main i mwa e code. The ESP32 so wa e
de elopmen ki SDK, also known as he ESP-IDF
(Esp essi IoT De elopmen F amewo k), p o ides
a comp ehensi e se o ools, lib a ies, and
applica ion p og amming in e aces (APIs) ha
allow us o access and manipula e a ious ha dwa e
ea u es o he ESP32. The SDK p o ides APIs o
di ec access o ha dwa e ea u es like ADC
(Analog- o-Digi al Con e e ), ime s, and RF
componen s. This low-le el access allows o
p ecise measu emen s o ha dwa e cha ac e is ics.
The ESP-IDF includes a ull- ea u ed Wi-Fi and
Blue oo h s ack, enabling ine-g ained con ol o e
wi eless communica ions. This can be used o
implemen cus om ansmission pa e ns o analyze
ecep ion cha ac e is ics. The SDK is buil on
F eeRTOS, enabling p ecise iming con ol and ask
managemen . I can be le e aged o c ea e unique
beha io al pa e ns o measu e sys em esponse
imes accu a ely. Las ly, ESP-IDF p o ides
ad anced powe managemen capabili ies, allowing
o measu ing and con olling powe consump ion
pa e ns, which can a y be ween de ices.
The main CPU clock speed is up o 240
MHz wi h an in e nal 150 kHz RC oscilla o and an
ex e nal c ys al o 40 MHz.. The in e nal oscilla o
is 8 MHz wi h calib a ion. The in e nal RC
oscilla o and PLL ci cui s can a y sligh ly due o
manu ac u ing p ocesses, a ec ing hei exac
equencies. Also, empe a u e and ol age
luc ua ions can cause sub le clock d i pa e ns
unique o each de ice. The ESP32's abili y o
dynamically adjus clock speeds can be used o
c ea e unique beha io al pa e ns. The de ice
suppo s IEEE 802.11 b/g/n (2.4 GHz) p o ocols
wi h an adjus able ansmi powe o up o +20
dBm o a ecei e sensi i i y o up o -98 dBm. All
he de ices a e connec ed o he same Wi-Fi access
poin (TP-Link A che C7). The de ices use HTTP
as he communica ion p o ocol o ansmi senso
da a.
Table 1: Key Speci ica ions o Esp32-W oom-32.
Fea u e Speci ica ion
P ocesso
Dual
-
co e X ensa LX6, 32
-
bi
Clock Speed
Up o 240 MHz
RAM
520 KB SRAM
ROM
448 KB
Flash Memo y
4 MB (expandable o 16 MB)
Wi
-
Fi
802.11 b/g/n (2.4 GHz)
Blue oo h
4.2 BR/EDR and BLE
GPIO Pins
34 p og ammable
ADC
12
-
bi , up o 18 channels
DAC
2x 8
-
bi channels
Ha dwa e C yp o
AES, SHA, RSA, ECC
RTC
150 kHz in e nal oscilla o
Ex e nal C ys al
40 MHz
Powe
Consump ion
A e age 80mA
Ope a ing Vol age
3.0V o 3.6V
Ope a ing
Tempe a u e
-40°C o +85°C
4.2 Da a Collec ion and Da a P ocessing
In his expe imen , we collec da a om 10
iden ical ESP32-WROOM-32 mic o-con olle s.
Each de ice sends senso da a pe iodically. The
ollowing da a is collec ed as shown in he Table.2
The ea u es a e collec ed o e mul iple
sessions o ensu e su icien da a is cap u ed o
analysis. The a ia ions in he da a a e expec ed due
o he mino ha dwa e and en i onmen al
di e ences ac oss de ices, e en hough he de ices
a e iden ical.
The da a p ocessing pipeline is comp ised
o wo main s ages, i.e., ime-se ies alignmen using
DTW and empo al modeling using LSTM o
cap u e he empo al dependencies o each o he
de ice beha io . Time-se ies da a o ea u es like
P obe Reques In e als, RTT, and IAT a e
ex ac ed om each de ice. The aim o using DTW
is o align sequences o ime-se ies da a o accoun
o any a ia ion due o di e ences in clock skew,
p ocessing delays, o en i onmen al ac o s. Fo
each ea u e (e.g., P obe Reques In e al), we
compu e he DTW dis ance be ween each de ice’s
ime-se ies da a and a e e ence sequence. We hen
align he ime-se ies da a based on he minimum
DTW dis ance o ob ain a simila i y sco e
( ( )
DTW i
S X
o each de ice
i
X
. The ou come is a
se o aligned sequences ha cap u e he iming
a ia ions ac oss de ices.
To achie e empo al modeling using
LSTM, we conside he ime-se ies da a om
mul iple sessions o ea u es like RSSI, RTT, TCP
sequence numbe s, and clock skew as inpu . The
LSTM ne wo k con igu a ion has an inpu laye ,
whe e he inpu is he ime-se ies da a om each
Jou nal o Theo e ical and Applied In o ma ion Technology
31s May 2025. Vol.103. No.10
© Li le Lion Scien i ic
ISSN: 1992-8645 www.ja i .o g E-ISSN: 1817-3195
4225
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