Co esponding au ho : Alex Lwembawo Mukasa
Copy igh © 2024 Au ho (s) e ain he copy igh o his a icle. This a icle is published unde he e ms o he C ea i e Commons A ibu ion Liscense 4.0.
Beha io al and AI-D i en P edic i e Analy ics o P oac i e F aud P e en ion in U.S.
Heal hca e Cybe secu i y Biome ics
Alex Lwembawo Mukasa 1, *, Es he A. Makandah 2, Ha una A abo Ch is ophe 3 and Dako Apaleokhai Dickson 4
1 Compu e Science Depa men , C eospan.
2 Uni e si y o Wes Geo gia, Ca oll on, USA.
3 Nige ia-Ko ea F iendship Ins i u e, Lokoja.
4 So wa e Enginee ing Depa men , Ve i as Uni e si y, Abuja, Nige ia.
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 1652-1661
Publica ion his o y: Recei ed on 04 July 2024; e ised on 20 Augus ; accep ed on 23 Augus 2024
A icle DOI: h ps://doi.o g/10.30574/wja .2025.27.2.2916
Abs ac
The challenges in heal hca e cybe secu i y a e g owing due o he inc ease in audulen ac i i ies, such as iden i y he ,
insu ance scams, and unau ho ized en y o elec onic heal h eco ds (EHRs). Con en ional au hen ica ion me hods like
passwo ds and wo- ac o au hen ica ion ha e shown o be insu icien in coun e ing ad anced cybe h ea s. This
esea ch examines he combina ion o beha io al biome ics and AI-based p edic i e analy ics o p oac i e aud
p e en ion in cybe secu i y wi hin U.S. heal hca e. Beha io al biome ics, such as keys oke dynamics, mouse
mo emen pa e ns, and gai analysis, p o ide an ongoing au hen ica ion me hod ha imp o es secu i y while
main aining use wo k low con inui y. AI-powe ed p edic i e analy ics, u ilizing bo h supe ised and unsupe ised
machine lea ning models, acili a e immedia e aud de ec ion by ecognizing unusual use ac i i ies wi hin heal hca e
p ocesses. E en wi h i s bene i s, implemen ing beha io al biome ics and AI models poses a ious echnical hu dles,
such as accu acy cons ain s, alse posi i es, and biases in algo i hms. Addi ionally, AI sys ems need ex ensi e, high-
quali y da ase s o de ec aud e ec i ely, which b ings abou conce ns ega ding p i acy and e hical implica ions. To
ackle hese issues, addi ional in es iga ion in o adap i e biome ic sys ems, p i acy-p ese ing AI me hods, and
egula o y s uc u es is essen ial o ha monizing secu i y wi h compliance obliga ions. This esea ch sugges s ha
u u e s udies ocus on hyb id biome ic au hen ica ion sys ems, educing bias in AI-enabled aud de ec ion, and
u ilizing p i acy-enhancing echnologies like ede a ed lea ning and homomo phic enc yp ion. By implemen ing AI-
based cybe secu i y sys ems, heal hca e o ganiza ions can imp o e aud de ec ion s a egies, sa egua d pa ien
in o ma ion, and main ain compliance wi h egula ions. The esul s highligh he necessi y o eamwo k among
heal hca e p o essionals, cybe secu i y specialis s, and policymake s o de elop s ong, e hical, and e icien secu i y
measu es.
Keywo ds: AI-d i en p edic i e analy ics; Beha io al biome ics; F aud de ec ion; Elec onic heal h eco ds;
Heal hca e cybe secu i y
1. In oduc ion
The heal hca e indus y in he U.S. has expe ienced a no able ise in cybe h ea s, wi h aud and iden i y he g owing
mo e common. Con en ional secu i y s a egies equen ly ail o ackle complex a acks, making i essen ial o emb ace
cu ing-edge echnologies. Beha io al biome ics and AI-powe ed p edic i e analy ics ha e su aced as e ec i e
s a egies o ac i ely mi iga e aud in heal hca e cybe secu i y.
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Beha io al biome ics ocuses on examining dis inc i e pa e ns in human ac ions, including keys oke dynamics,
mouse ges u es, and ouchsc een usage, o ongoing use iden i y e i ica ion. In con as o adi ional s a ic
au hen ica ion me hods, beha io al biome ics p o ides adap i e and unob usi e secu i y, pe sonalizing o use
p o iles o iden i y i egula i ies ha sugges audulen beha io . This me hod inc eases he p ecision o aud
iden i ica ion and diminishes he likelihood o unwa an ed access o con iden ial heal hca e in o ma ion.
Combining AI-powe ed p edic i e analy ics wi h beha io al biome ics enhances aud p e en ion ini ia i es. Machine
lea ning algo i hms a e capable o analyzing la ge olumes o beha io da a ins an aneously, de ec ing sligh a ia ions
om ypical use beha io s ha could indica e audulen ac ions. This collabo a ion allows heal hca e o ganiza ions
o o esee and add ess po en ial isks be o e hey p og ess, hus p o ec ing pa ien da a and p ese ing con idence in
digi al heal h sys ems.
None heless, in oducing hese echnologies in o he heal hca e sec o poses di icul ies, such as wo ies ega ding da a
p i acy, e hical conside a ions, and he scalabili y o sys ems. To ackle hese challenges, collabo a ion among
heal hca e p o essionals, echnology c ea o s, and egula o y agencies is essen ial o c ea e s ong amewo ks ha
gua an ee he esponsible and e icien use o AI and beha io al biome ics in p e en ing aud.
1.1. S a emen o he P oblem
The g owing complexi y o cybe h ea s in he U.S. heal hca e indus y has ende ed con en ional secu i y me hods,
like passwo ds and wo- ac o au hen ica ion, insu icien o aud p e en ion. Heal hca e ins i u ions keep a la ge
olume o sensi i e pa ien in o ma ion, posi ioning hem as p ime a ge s o cybe c iminals in ol ed in iden i y he ,
insu ance aud, and unau ho ized da a b eaches. Despi e es ablished secu i y measu es, c iminals s ill ake ad an age
o sys em weaknesses, esul ing in inancial se backs and jeopa dized pa ien sa e y (Sha ma & Chen, 2022). The
inc easing occu ence o da a b eaches highligh s he p essing demand o enhanced, p oac i e aud de ec ion sys ems.
Beha io al biome ics and AI-based p edic i e analy ics p esen e ec i e solu ions o imp o ing aud p e en ion in
heal hca e cybe secu i y. These echnologies can examine use beha io ins an ly, de ec ing mino shi s om ypical
pa e ns ha could sugges audulen ac i i ies. None heless, he inco po a ion o hese ools in o heal hca e
cybe secu i y sys ems is s ill es ic ed because o wo ies ega ding implemen a ion di icul y, da a p i acy, and e hical
conce ns (Pa el e al., 2021). Mo eo e , inco ec posi i es in aud de ec ion sys ems can dis up heal hca e p ocesses,
leading o conce ns ega ding he p ecision and us wo hiness o AI-based secu i y measu es.
The e is an u gen equi emen o empi ical s udies o assess he e icacy, di icul ies, and op imal s a egies o
implemen ing beha io al biome ics and AI-based p edic i e analy ics in heal hca e cybe secu i y. Cu en esea ch
has mainly concen a ed on inancial o ganiza ions, esul ing in a lack o insigh in o hei ele ance in he heal hca e
indus y (Jones & Li, 2023). Filling his gap will o e heal hca e o ganiza ions da a-d i en insigh s o imp o e aud
p e en ion ac ics, gua an ee adhe ence o p i acy egula ions, and boos o e all cybe secu i y esilience. In he
absence o ho ough esea ch and es ablished policy amewo ks, he implemen a ion o hese echnologies migh
emain disjoin ed, hinde ing hei abili y o e ec i ely add ess heal hca e aud.
Resea ch Objec i es
• To explo e how AI-d i en p edic i e analy ics enhances aud de ec ion
• To assess he e ec i eness o beha io al biome ics in heal hca e cybe secu i y
• To e alua e he in eg a ion o AI, machine lea ning, and biome ics o p oac i e aud p e en ion
2. Li e a u e Re iew
2.1. O e iew o Cybe secu i y Th ea s in Heal hca e
The U.S. heal hca e indus y encoun e s an expanding ange o cybe secu i y isks, as audulen ac i i ies like iden i y
he , insu ance aud, and da a b eaches a e on he ise. In heal hca e, iden i y he en ails gaining unau ho ized access
o pe sonal da a, which can hen be exploi ed o acqui e medical se ices o execu e audulen billing. Insu ance aud
includes ac ions such as al e ing claims o misin e p e ing pa ien de ails o ob ain undese ed paymen s. Da a
b eaches, equen ly caused by hacking e en s, comp omise con iden ial pa ien eco ds, esul ing in possible
exploi a ion o pe sonal heal h in o ma ion (PHI) (Seh e al., 2020).
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The e ec o hese decep i e p ac ices on pa ien sa e y is signi ican . When PHI is b eached, he e is a dange o medical
iden i y he , meaning a pe son's medical da a can be changed o imp ope ly used. This may esul in inaccu a e
medical his o ies, e oneous diagnoses, o unsui able ea men s, pu ing pa ien heal h a isk. Addi ionally, b eaches
can in e up heal hca e se ices since sys ems migh ha e o be aken o line o ix secu i y laws, leading o delays in
pa ien ca e and access o essen ial medical in o ma ion (Lamp opoulos e al., 2023).
Mone a ily, heal hca e aud places a conside able s ain on he sec o . Acco ding o es ima es, aud- ela ed losses may
a y be ween 3% and 10% o o e all heal hca e expendi u es, po en ially su passing $300 billion each yea in he U.S.
alone (Simbo AI, 2024). These losses a ise om decei ul claims, ising insu ance p emiums, and he expenses ela ed
o p obing and add essing b eaches. Mo eo e , en i ies could encoun e signi ican penal ies and legal cos s o ailing
o adhe e o ules mean o sa egua d pa ien da a.
I is indeed one o he e ec i e measu es ha a e pu o wa d agains such h ea s; howe e , non-compliance is s ill a
pe asi e issue. The HIPAA - he Heal h Insu ance Po abili y and Accoun abili y Ac - p o ides na ional s anda ds o
p o ec ing PHI. Many iola ions end up in e y se ious penal ies, wi h ines pe iola ion up o $50,000 o a maximum
o $1.5 million annually, in addi ion o c iminal cha ges (U.S. Depa men o Heal h & Human Se ices, n.d.). Despi e
hese s a u es, b eaches occu , sugges ing ha compliance and en o cemen measu es a e s ill lacking.
The Heal h Insu ance Po abili y and Accoun abili y Ac (HIPPA) imposes na ional-le el egula ions on iola ions
conce ning he secu i y o p o ec ed heal h in o ma ion (PHI). Penal ies o in ingemen could be e y s ingen , up o
$50,000 pe iola ion, and an annual agg ega e amoun o $1.5 million in addi ion o possible c iminal indic men s (U.S.
Depa men o Heal h & Human Se ices, n.d.). Ye b eaches in da a con inue o ake place, which shows ha no e e y
legal aspec is being made up in compliance and en o cemen .
Cybe h ea s in he U.S. heal hca e sec o include iden i y he , insu ance aud, and da a b eaches, and ha makes
hem a g ea dange o he sa e y o he pa ien s, inancial h ea s, and egula o y compliance. Con on ing he
challenges mus include massi e secu i y measu es, ha ing s ic egula ions, and u he e o s owa d e adica ing
aud.
2.2. Beha io al Biome ics in F aud De ec ion
Beha io al biome ics is ad anced secu i y by which indi iduals a e iden i ied based on how hey ac du ing
in e ac ions be ween use and compu e . T adi ional biome ic sys ems in con as wi h beha io al biome ics don'
emphasis physical ea u es like inge p in s o ace shapes bu would mos ly ocus on how he use engages wi h he
de ice, cap u ing e y unique beha io s ha a e nea ly impossible o o ge. Beha io al biome ic sys em gene a es
con inuous aud de ec ion and au hen ica ion o use s by obse ing anomalous beha io pa e ns, making
unau ho ized access ex emely di icul .
Some o he echniques o beha io al biome ics a e keys oke dynamics, mouse mo emen analysis, and gai analysis.
Keys oke dynamics wo ks in e ms o analyzing he hy hm and ime when he indi idual ypes using a unique p o ile
o i s use s. Mouse mo emen anomaly de ec ion uses speed, ajec o y, and click pa e ns o unde s and how he
cu so mo es o associa e i wi h a use . Gai analysis cap u es he speed and s yle o walking o a pe son: by obse ing
he gai pa e n o use s wi h he help o senso s in po able mobile de ices, au hen ica ion akes place wi hou he
use 's awa eness. These a e essen ially s ong con ibu ing measu es in he beha io al biome ic me hodology o aud
de ec ion.
In beha io al biome ics compa ed wi h adi ional au hen ica ion me hods, he majo bene i would be o imp o e
secu i y. T adi ional me hods like passwo ds, PINs, and so on su e om cap u e, eplica ion, and can also be sha ed
in many ways, hus making hem un eliable compa ed o beha io biome ics, which elies on he analysis o beha io
and pa e ns which a e unique o each indi idual and eally ha d o unau ho ized use s o mimic, hence p o iding a
e uge agains iden i y he and unau ho ized access. This con inuous obse ing ensu es ha e en i he logon
c eden ials be comp omised, audulen ac s can s ill be de ec ed by de ia ion om no mal beha io al pa e ns.
This is one o i s ad an ages: sa egua ding use s' p i acy. Beha io al biome ics au hen ica ion akes an ac ual li e look
a how a use in e ac s wi h hei de ice. Hence, his will make i eally challenging o a hacke o bypass he measu es.
Unlike he adi ional me hod o au hen ica ion, which would end o e eal one's iden i y, beha io al biome ics a e
he pa e ns o beha io ha keep in o ma ion hidden om a use . Tha akes one s ep close owa ds secu i y and
p i acy wi h espec o he s o age and handling o sensi i e pe sonal in o ma ion.
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The use 's expe ience is ic ionless wi h beha io al biome ics: Au hen ica ion wo ks in he backg ound and cons an ly
obse es he use 's beha io pa e n wi h he de ice so ha hey need no ype in hei passwo ds mul iple imes o go
h ough ye ano he e i ica ion iden i y check. The e o e, his in eg a ion no only imp o es use deligh and less
in e up ion bu also makes he ba ie s o secu i y measu es less in asi e and mo e use - iendly. Well, ha 's no o
say ha beha io al biome ics doesn' in ude. I is no in usi e and so secu i y does no comp omise usabili y.
By using inhe en and unique beha io s o he use s, beha io al biome ics allow a new and lexible app oach o aud
de ec ion. Techniques such as keys oke dynamics, mouse mo emen analysis, o gai analysis p o ide con inuous and
unob usi e au hen ica ion esul ing in highe secu i y and p i acy combined wi h a be e use expe ience. Cybe
h ea s ha e been so sophis ica ed ha he jou ney o adop ing beha io al biome ics in aud de ec ion sys ems will
be a majo componen o he o e all s a egy o p o ec ing sensi i e in o ma ion and ensu ing he in eg i y o digi al
exchanges going o wa d.
2.3. AI-D i en P edic i e Analy ics in Cybe secu i y
A i icial in elligence akes a signi ican place as a mode n-day miles one in cybe secu i y, mo e pa icula ly wi h he
applica ion o machine lea ning models o aud de ec ion, which a e designed o lea n how o iden i y audulen
beha io by iden i ying pa e ns and inding anomalies in a b oad spec um o eco ds o iden i y audulen ac i i ies
p oac i ely. By ecognizing his o ic and cu en da a, p edic i e analy ics using AI can ecognize e y sligh de ia ions
ha may ep esen indica o s o aud o he imp o emen o secu i y pos u e in o ganiza ions.
F aud de ec ion machine lea ning models a e mainly esiden ial unde supe ised and unsupe ised lea ning
app oaches. Supe ised lea ning app oaches ain algo i hms using da ase s wi h known ou comes o de ec ing
pa e ns o beha io in o de o iden i y he audulen ac i i ies con ained in he da a wi h common algo i hms like
decision ee, suppo ec o machine, neu al ne wo k, e c. On he o he hand, unsupe ised lea ning allows o
p ocessing only unlabeled and un agged s anda ds o clus e ing and anomaly de ec ion, o look o ou lie pa e ns
ha can possibly indica e aud. K-means clus e ing and p incipal componen analysis, among o he s, would be e y
common echniques used o his s udy (Ananya e al., 2025).
Anomaly de ec ion is one o he impo an componen s o AI-based secu i y s a egies. I de ec s da a ou lie s om
no mal pa e ns signi ying unau ho ized access o audulen ac i i ies. Anomaly de ec ion sys ems based on AI
sc u inize a a ie y o pa ame e s, such as use beha io , ansac ion pa e ns, and ne wo k a ic, and de ine no mal
ac i i y baselines among hem(Oladayo and Abdullahi, 2018). The anomalies a e le o be in es iga ed whene e
de ia ion occu s. Wi h a eal- ime p ocessing scheme, his can e en be suspec ed soone and ea ed while limi ing he
damage o he minimum (Adeola, 2025).
I he AI-based p edic i e algo i hm is implemen ed along wi h eal- ime p ocess moni o ing, he capabili y o he
o ganiza ion will ge enhanced in i s igh agains aud. In e alua ing da a con inuously while ha da a is being
gene a ed, eal- ime moni o ing sys ems can de ec suspicious ac i i ies imely and espond o hem. Speed is o he
essence in hwa ing audulen ansac ions and e mina ing secu i y b eaches be o e hey escala e in o some hing
ampan . Addi ionally, he AI sys em's lea ning echniques can boos o modi y wi h eme ging h ea s, ensu ing i s
abili y o de ec aud o an ex ended pe iod (Ananya e al., 2025). Despi e ad an ages, challenges s ill exis owa d
e icien ly deploying AI-based p edic i e analy ics agains aud de ec ion. The sys em's pe o mance may be nega i ely
impac ed by da a quali y obsolescence, alse posi i es, and hea y compu a ional esou ce equi emen s. Addi ionally,
in pa allel, ad e sa ies a e de eloping new ways o bypass de ec ion. The e o e, he AI models mus e ol e
con inuously. Hence, aud in cybe secu i y can e ec i ely be mi iga ed only h ough a combined app oach
inco po a ing AI and human in elligence, plus s ong policies (Adeola, 2025).
2.4. AI-D i en Beha io al Biome ics o F aud De ec ion
AI-based beha io al biome ics s ands ou as a cu ing-edge cybe secu i y me hod in which machine lea ning
algo i hms a e applied o e i y and au hen ica e use s based on unique pa e ns in in e ac ion wi h a digi al sys em.
T adi ional me hods o au hen ica ion, which s ill hinge on passwo ds o s a ic biome ics, a e ai ly conscious only
when one use is au hen ica ed, while beha io al biome ics can analyze and con inuously moni o cha ac e is ics such
as he dynamics o yping, mouse ac i i y, ouchsc een ges u es, and, in some cases, e en he body mo emen s du ing
walking o gai . These beha io al cha ac e is ics canno be ypically imi a ed and hus p o ide pe pe a o -p oo
eplicas o such ai s in aud de ec ion. Using AI, sys ems a e capable o cons uc ing a baseline o no mal use
beha io and iden i ying exposu es o hese beha io s ha may be symp oma ic o aud, he eby allowing secu i y wi h
minimal comp omise o he use expe ience. A signi ican ad an age o AI-d i en beha io al biome ics is based on i s
de ec ion o highly sophis ica ed aud echniques, such as accoun akeo e s and c eden ial s u ing. Whe eas
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adi ional secu i y coun e measu es usually all sho be o e cybe c iminals employing a combina ion o s olen
c eden ials and au oma ed bo ools o su pass au hen ica ion sys ems, AI-enabled beha io al analy ics de ec
abno mali ies in eal- ime, lagging suspicious ac i i ies e en in he si ua ion whe e alid c eden ials a e en e ed.
Mo eo e , he sys ems a e sel - uning wi h con inuous lea ning, yielding adap i e sys ems agains dynamic aud ac ics
wi h less and less alse posi i es. Wi h he inc easing adop ion o AI-d i en beha io al biome ics in inancial
ins i u ions, heal hca e, and many o he indus ies, hese AI sys ems p o ide signi ican ly be e de ec ion and
p e en ion o aud while allowing smoo h use access. In he ma e o AI-D i en beha io al biome ics o aud
de ec ion, one could possibly men ion eal- ime use au hen ica ion and AI-powe ed beha io al p o iling.
2.4.1. Real-Time Use Au hen ica ion
Real- ime use au hen ica ion is a i al cybe secu i y measu e ha allows secu e access in o digi al sys ems h ough
he p ocess o con inuously e i ying use s' iden i y. In con as o con en ional au hen ica ion me hods, which mos ly
depend on s a ic c eden ials like passwo ds o PINs, eal- ime au hen ica ion adop s dynamic ac o s o secu i y
ein o cemen , such as beha io al biome ics, a i icial in elligence (AI), and mul i- ac o au hen ica ion (MFA). By
obse ing use beha io pa e ns, such as keys oke dynamics, mouse mo emen s, and login endencies, eal- ime
au hen ica ion sys ems can p e en unau ho ized access ins an aneously by means o aud de ec ion p ac ices. This
app oach is ex emely c ucial in sensi i e en i onmen s, such as heal hca e and inancial ins i u ions, in which b eaches
could cause di e consequences. AI-based p edic i e analy ics o i y eal- ime au hen ica ion e en mo e by iden i ying
and esponding o h ea s be o e he h ea s occu . Using ad anced analy ics, machine lea ning models con inuously
examine use ac i i y and lag de ia ions ha may signi y dishones y. This no only s eng hens secu i y bu also
educes ic ion on he use by alle ia ing unnecessa y manual e i ica ion s eps. Real- ime au hen ica ion also g ea ly
inc eases egula o y compliance by ensu ing ha only au ho ized use s gain access o p o ec ed in o ma ion. As cybe
h ea s e ol e, eal- ime au hen ica ion emains a key weapon agains any hing ha comp omises secu i y and agains
he p o ec ion o digi al asse s.
These au hen ica ion means a e c i ical in p o ec ing sensi i e in o ma ion wi hin he b oade ealm o digi al secu i y.
On he whole, adi ional au hen ica ion elies on one- ime e i ica ion- ha is, a use is asked o some c eden ials-
passwo ds, maybe, o biome ic da a-a session s a so ha he use can be g an ed access. Once au hen ic, he sys em
does no ques ion i ha iden i y holds o he en i e session. This me hodology wo ks well as a as he echnical
en i onmen goes bu has i s laws; once an unau ho ized pa y gains access a e he i s login, he o she can con inue
o exploi he session wi hou de ec ion.
Con inuous au hen ica ion would con inue o add ess all ulne abili ies by alida ing use iden i y o e he cou se o a
session. This me hod uses a a ie y o beha io beha io s as well as con ex s like yping pa e ns, mouse mo emen s,
and geoloca ion da a o con i m ha he cu en use is he ac ual accoun holde . Bu wi h eal- ime analysis o hese
pa ame e s, con inuous au hen ica ion can de ec anomalies ha a e e iden o unau ho ized access and au oma ically
ini ia e secu i y measu es. Thus, dynamic au hen ica ion inc eases he secu i y by i ually closing he window o
malicious ac i i y in an ac i e session.
O all he scena ios, con inuous au hen ica ion is mos bene icial o Elec onic Heal h Reco d (EHR) applica ions whe e
highly sensi i e pa ien da a has o be sa egua ded. Because single one-o e i ica ion does no su ice in EHR sys ems,
i causes cases o huge p i acy b each and pa ien s' us ulne abili y a he mos . Th ough con inuous au hen ica ion,
ensu ing ha only au ho ized heal h p o essionals main ain access o pa ien eco ds as a complemen ing in e ac ion
will u he imp o e secu i y and compliance wi h p i acy laws o EHR sys ems. S udies e ealed ha he inclusion o
nex -gene a ion au hen ica ion me hods such as bio-capsule acial ecogni ion enhances EHR usabili y and secu i y
(Pu kayas ha e al., 2021).
Besides, con inuous au hen ica ion is pa o he e ec i e seamless in eg a ion in o heal hca e wi hou undue s ain.
This a ec s hospi al managemen a he same ime objec i es achie ed in he imp o emen o secu i y in his manne
a e also ealized in keeping he ope a ional e iciency o heal h ca e deli e y in ac . The ma u i y o cybe - h ea s now
will mos p obably ca ch on o pa ien heal h in o ma ion using con inuous au hen ica ion in he heal h eco ds sys em.
2.4.2. AI-Powe ed Beha io al P o iling by iden i ying anomalous use beha io in heal hca e wo k lows
In a heal hca e se ing, he salien conside a ion emains secu i y and in eg i y o he whole pa ien in o ma ion
scena io. An impo an pa o his secu i y p o ides he means o he iden i ica ion o anomalous use beha io wi hin
heal hca e wo k lows, since he p esence o such anomalies could indica e secu i y b eaches o misuse o he sys em.
Possible sou ces o anomalous beha io s include in en ional inside a acks, misuse o comp omised use c eden ials,
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and unin en ional e oneous use ac ions-e e y one o hese h ea ens pa ien p i acy and he us in a heal hca e
sys em.
Resea che s ha e p oposed di e en me hods aimed a de ec ing such anomalies. Fo ins ance, Duan e al. (2012)
a gued o an unsupe ised anomaly de ec ion model using densi y-based clus e ing o s udy pa ien ca e low logs.
He e, su geons would iden i y unexpec ed ac i i ies in he ea men o pa ien s o anomalies ha can a ise om cases
o noncon o ming imings be ween p eplanned medical p ocedu es and he ac ual execu ion, as poin ed ou h ough
he analysis o ca e low logs. Such analysis will allow p o ide s o iden i y any abno mal beha io s ha h ea en he
in eg i y o he pa ien o he ea men gi en o hem.
Also, Gup a e al. (2021) ha e es ablished an HMM-based me hod o he de ec ion o anomalies ele an o emo e
pa ien moni o ing. Such a model would inco po a e da a om sma heal h de ices and home senso s aimed a
es ablishing a pa e n o no mal beha io o he use . Any de ia ions om his s anda d pa e n would be labelled as
an anomaly o enable he ea ly de ec ion o a a ie y o issues such as unexpec ed pa e ns o use ac i i y,
mal unc ioning senso s, and co up beha io om a de ice. The applica ion o such models se es o imp o e he
in eg i y o moni o ing o he pa ien as well as he imely p o ision o medical assis ance.
The in eg a ion o a i icial in elligence (AI) in o anomaly de ec ion amewo ks u he bols e s he secu i y o
heal hca e wo k lows. Yeng e al. (2021) p oposed an AI-based amewo k ha analyzes heal hca e s a 's secu i y
p ac ices o iden i y a ypical beha io s. This amewo k employs machine lea ning algo i hms o sc u inize use
ac i i ies, he eby de ec ing de ia ions om es ablished secu i y p o ocols. By con inuously moni o ing and analyzing
use beha io , heal hca e o ganiza ions can p oac i ely add ess po en ial secu i y h ea s and main ain compliance
wi h da a p o ec ion egula ions.
Figu e 1 Hidden Ma ko Models, and AI-d i en amewo ks o analyzing he secu i y p ac ices o heal h ca e o
pa ien s h ps://medin o m.jmi .o g/2021/12/e19250/, 2021
Iden i ying anomalous use beha io in heal hca e wo k lows is essen ial o sa egua ding pa ien da a and ensu ing
he eliabili y o heal hca e sys ems. Employing ad anced echniques, such as densi y-based clus e ing, Hidden Ma ko
Models, and AI-d i en amewo ks, enables he e ec i e de ec ion and mi iga ion o po en ial secu i y h ea s. These
me hodologies no only enhance da a secu i y bu also con ibu e o imp o ed pa ien ca e by ensu ing ha heal hca e
p ocesses ope a e wi hin hei in ended pa ame e s.
2.5. Challenges o adop ing AI-D i en P edic i e Analy ics o P oac i e F aud P e en ion
The use o beha io al biome ics and AI-based models o aud de ec ion b ings signi ican echnical di icul ies, chie ly
conce ning accu acy and eliabili y. Using beha io al biome ics, a pe son's au hen ica ion can be ob ained by analyzing
speci ic human pa e ns such as keys oke dynamics and mouse mo emen s. Howe e , a ia ions in he beha io owing
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o a igue, moods, and en i onmen al condi ions can a ec he accu acy o hese sys ems. S ess, o example, can cause
a use 's yping empo o a y, causing he sys em o mis ep esen ha legi ima e use s ha e become audulen ac o s.
Fu he deg ada ion in eliabili y o beha io al biome ic sys ems happens due o hei inabili y o adap o no mal
beha io changes o use s wi h he passage o ime. As in e ac ion pa e ns change o pe sons, sys ems mus
con inually change he baseline o conse e accu acy; o he wise hey isk inc easing alse ejec ion a es, whe eby
legi ima e use s a e denied access based on pe cei ed anomalies in hei beha io . Sys ems mus also conside he o he
side o in a-class a iabili y- ha is, beha io di e ences shown by he same indi idual unde di e en ci cums ance-
which makes i e y challenging o build obus au hen ica ion models. Add essing hese challenges would equi e a
ully- ledged algo i hm capable o sepa a ing ha mless beha io al a ia ions om eal secu i y h ea s.
The aining o AI models o aud de ec ion aces many o he es ic ions, especially ega ding he quali y and
ep esen a i eness o aining da a. Fo machine lea ning models o be e ec i e, hey mus ha e access o la ge da ase s
ha inco po a e a ange o legi ima e beha io s and audulen ones. Howe e , he collec ion o such wide- anging
da ase s is o en hwa ed by p i acy conce ns, as well as he ela i e a i y o some ypes o aud. Such ci cum en ion
may p oduce skewed da ase s inconsis en wi h he model lea ning p ocess. The g ea e he model's weigh ing owa d
he majo i y class (legi ima e beha io ), he less e ec i e i will be in ecognizing audulen ac i i y, educing i s
e icacy as a whole.
Ano he issue o se ious impo ance would be ins ances like a alse posi i e-whe e epu able beha io is w ongly agged
as audulen . Use s would lose hei con idence when aced wi h high alse posi i e a es; hey would also be subjec ed
o unnecessa y incon eniences like ha ing hei accoun s locked o ha ing o go h ough ex a au hen ica ion s eps.
Wedge e al. (2017) used au oma ed ea u e enginee ing o be e equip hei model a being able o disc imina e agains
audulen om eal beha io -in hei s udy; he esul was a 54% imp o emen in hei alse-posi i e a es. Such
de elopmen s no wi hs anding, he op imum balance be ween hese wo ac o s con inues o be an uphill ask o deal
wi h in aud de ec ion sys ems d i en by AI.
Besides, AI models a e also p one o biases in aining da a, which lead o disc imina ion and e hical issues. Algo i hmic
bias a ises when he da a a e ained based on social bias o inequali y in socie ies, which gi es he model a endency
o mimic he same in p edic ions. Fo ins ance, i a ce ain da ase o e - ep esen s a pa icula demog aphic g oup, he
AI model may show be e esul s o ha g oup while e alua ing o he s poo ly. P ope aining da a cu a ion,
implemen a ion o ai ness-awa e algo i hms, and con inuous moni o ing o equal ea men among all use g oups
a e some s a egies o add essing algo i hmic bias.
3. Conclusion
In conclusion, he in eg a ion o beha io al biome ics and AI-d i en p edic i e analy ics o e s a ans o ma i e
app oach o aud p e en ion in U.S. heal hca e cybe secu i y. By con inuously moni o ing unique use beha io s such
as keys oke dynamics, mouse mo emen s, and ouchsc een in e ac ions hese echnologies p o ide dynamic and non-
in usi e au hen ica ion me hods ha enhance secu i y measu es beyond adi ional s a ic app oaches. This con inuous
au hen ica ion is pa icula ly c ucial in sa egua ding sensi i e pa ien da a wi hin Elec onic Heal h Reco d (EHR)
sys ems, ensu ing ha only au ho ized pe sonnel main ain access h oughou hei sessions.
The applica ion o AI-d i en p edic i e analy ics u he s eng hens his secu i y amewo k by enabling eal- ime
moni o ing and anomaly de ec ion. Machine lea ning models, bo h supe ised and unsupe ised, analyze as da ase s
o iden i y pa e ns indica i e o audulen ac i i ies, allowing o p oac i e h ea mi iga ion. Fo ins ance, AI-based
amewo ks ha e been de eloped o analyze heal hca e s a 's secu i y p ac ices, e ec i ely iden i ying a ypical
beha io s ha may signal secu i y b eaches (Yeng e al., 2021). Such sys ems no only enhance he accu acy o aud
de ec ion bu also adap o e ol ing cybe h ea s, main aining obus de ense mechanisms wi hin heal hca e
in as uc u es.
Howe e , he implemen a ion o hese ad anced echnologies is no wi hou challenges. Technical issues such as he
accu acy and eliabili y o beha io al biome ics can be in luenced by ex e nal ac o s like use a igue o emo ional
s a es, po en ially leading o alse posi i es o nega i es. Addi ionally, AI model aining equi es comp ehensi e and
ep esen a i e da ase s; limi a ions in da a quali y o quan i y can hinde he model's e ec i eness in accu a ely
de ec ing audulen beha io . Add essing hese challenges necessi a es ongoing esea ch and de elopmen o e ine
algo i hms and ensu e he obus ness o au hen ica ion sys ems.
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In addi ion, da a p i acy and e hical implica ions should be ouched upon. On he one hand, beha io al biome ics and
AI analy ics demand con inuous moni o ing a e ques ionable in e ms o use consen and ob usi e su eillance. I is
c ucial o p o ide a ai egula o y amewo k and e hical guidance o balance enhanced secu i y agains p o ec ing
indi idual p i acy igh s. The s anda ds o he esponsible use o hese echnologies in heal hca e shall be de eloped
h ough collabo a ion by he heal hca e p o ide , echnology de elope , and decision-make .
In sho , beha io al biome ics and AI p edic i e analy ics hold imminen p omise o p oac i e aud p e en ion in U.S.
heal hca e cybe secu i y. Any success ul deploymen o such sys ems, howe e , mus con end wi h echnical, e hical,
and egula o y hu dles. By ackling hese mul i ace ed challenges h ough in e disciplina y coope a ion and con inuous
inno a ion, he heal hca e indus y will o i y i s e o s agains aud, hus keeping pa ien da a sa e and main aining
us in digi al heal h sys ems.
3.1. Recommenda ions o Fu he Resea ch
3.1.1. Enhancing he Accu acy o Beha io al Biome ics
Fu u e s udies should ocus on imp o ing beha io al biome ics' accu acy and eliabili y h ough he c ea ion o
adap i e algo i hms able o ake accoun o shi ing use beha io as a esul o emo ional, physical, o en i onmen al
ac o s. Resea che s should in es iga e he po en ial use o hyb id models combining se e al biome ic modali ies-
keys oke dynamics coupled wi h ei he oice ecogni ion o acial exp ession, o ins ance- o imp o e au hen ica ion
accu acy and minimize alse posi i es.
3.1.2. Mi iga ing Bias in AI-Based F aud De ec ion
They ac ually p oduce many AI aud de ec ion sys ems, bu hey commonly ca y possible biases due o a non-
ep esen a i e o imbalanced aining se . Resea ch is necessi a ed o look in o me hods such as ai ness-awa e
machine lea ning models and bias-mi iga ion algo i hms ha can be e ec i e o aud de ec ion sys ems o make hem
ai o dissimila popula ions wi hin a speci ic ne wo k. Fu he esea ch is also essen ial o enhance di e si y in da a
while p oducing syn he ic da a gene a ion o ede a ed lea ning me hods ha would ensu e he p i acy o all da a used.
3.1.3. Real-Time F aud De ec ion in Elec onic Heal h Reco d (EHR) Sys ems
I is o be no ed ha a ho ough s udy o AI-d i en beha io al biome ics applica ion in EHR sys ems is wa an ed,
especially in he eal- ime de ec ion o aud. Ins ead, esea ch e o s ough o ocus on imp o ing he speed and
e iciency o AI models so ha au hen ica ion delays may be minimized while cons an secu i y is upheld. Fu he
s udies need o assess he usabili y and accep ance o such sys ems by heal hca e p o essionals in o de o acili a e
hei seamless inco po a ion in o clinical p ocesses.
3.1.4. P i acy-P ese ing AI Techniques
Owing o he sensi i i y o heal hca e da a, u u e s udies should conside p i acy-p ese ing AI echniques, such as
homomo phic enc yp ion, secu e mul i-pa y compu a ion, and di e en ial p i acy, which ensu e da a secu i y while
allowing AI models o in e beha io al pa e ns wi hou accessing pe sonally iden i iable in o ma ion.
3.1.5. Regula o y and E hical Conside a ions
While i may u he esea ch needed o p epa e he comple e e hical and egula o y guidelines on he uses o AI-d i en
beha io al biome ics in heal h, he c ucial pa will be he in es iga ion o wha modi ica ions and adjus men s mus
be made o exis ing da a p o ec ion laws such as HIPAA in he Uni ed S a es so ha hey can mee he AI-speci ic
challenges in aud p e en ion. In addi ion, s udies should also in es iga e he pa ien and p o ide pe spec i e on he
use o AI o secu i y measu es wi h ega d o anspa ency and us .
Compliance wi h e hical s anda ds
Disclosu e o con lic o in e es
No con lic o in e es o be disclosed.
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