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DeepPhish
Simulating Malicious AI
IvanTorroledo – Lead Data Scientist
Alejandro Correa Bahnsen –VP, Research
Luis Camacho – Lead Research Data Architect
CYXTERA TECHNOLOGIES 2
 Portfolio of cybersecurity software and services
 Intelligent and adaptive
 Cloud-native and hybrid-ready
 Global colocation leader
 57 data centers in 29 global markets
 2.6M sq. feet of data center space
 195 megawatts of power
 3,500 customers
 1,100 employees
 Headquartered in Miami with offices globally
 Experienced leadership in infrastructure
and security
CyxteraTechnologies
CYXTERA TECHNOLOGIES 3
80 % of cyber
crimes are being
committed by
sophisticated
attackers
The total
USA market
for cyber
insurance is
3B in 2017
CYXTERA TECHNOLOGIES 4
CYXTERA TECHNOLOGIES 5
CYXTERA TECHNOLOGIES
AI to Classify Phishing URLs
6
 Identify & Classify Malicious URLs and Domains with
Prediction - Not Blacklists.
 The system calculates the probability of a URL being used to
host a phishing attacks using Deep Neural Networks. It
correctly classify URLs with over 98% of accuracy.
CYXTERA TECHNOLOGIES
Long-Short Term Memory Networks
7
URL
h
t
t
p
:
/
/
w
w
w
.
p
a
p
a
y
a
.
c
o
m
One hot
Encoding
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
Embedding
3.2 1.2 … 1.7
6.4 2.3 … 2.6
6.4 3.0 … 1.7
3.4 2.6 … 3.4
2.6 3.8 … 2.6
3.5 3.2 … 6.4
1.7 4.2 … 6.4
8.6 2.4 … 6.4
4.3 2.9 … 6.4
2.2 3.4 … 3.4
3.2 2.6 … 2.6
4.2 2.2 … 3.5
2.4 3.2 … 1.7
2.9 1.7 … 8.6
3.0 6.4 … 2.6
2.6 6.4 … 3.8
3.8 3.4 … 3.2
3.3 2.6 … 2.2
3.1 2.2 … 2.9
1.8 3.2 … 3.0
2.5 6.4 … 2.6
LSTM
LSTM
LSTM
LSTM
Sigmoid
…
CYXTERA TECHNOLOGIES 8
CYXTERA TECHNOLOGIES 9
Asthreatactorsimprovetheirattacks,isAIthe
newtechnologytheywilluse?
CYXTERA TECHNOLOGIES
The Experiment
Process
Identify
individual
threat actors
Ran them through
our own AI
detection system
Improved
their attacks
using AI
CYXTERA TECHNOLOGIES
Uncovering Threat Actors
12
 Objective: We want to understand effective patterns of
each attacker to improve them through a AI model
 As we can not know them directly, we must learn from
them through their attacks
 Database with 1.1M confirm phishing URLs collected from
Phishtank
CYXTERA TECHNOLOGIES
Threat Actor 1
13
naylorantiques.com
406 URLs
http://naylorantiques.com/components/com_contact/vi
ews/contact/tmpl/62
http://naylorantiques.com/docs/Auto/Atendimento/5BB
ROPI6S3
http://naylorantiques.com/Atualizacao
Segura/pictures/XG61YYMT_FXW0PWR8_5P2O7T2U_P9H
NDPQR/
http://naylorantiques.com/zifn3p72bsifn9hx9ldecd8jzl2f0
xlwf8f
http://www.naylorantiques.com/JavaScript/charset=iso-
8859-1/http-equiv/margin-bottom
Keywords
atendimento, jsf, identificacao, ponents, views, TV, mail, SHOW,
COMPLETO, VILLA, MIX, ufi, pnref, story, tryy2ilr, Autentico
106 domains
naylorantiques.com, netshelldemos.com, debbiebright.co.z,
waldronfamilygppractice.co.uk , avea-vacances.com ,
psncodes2013.com uni5.net , 67.228.96.204,
classificadosmaster.com.br, ibjjf.org
Visual Check
Check in
database
Visual Check
CYXTERA TECHNOLOGIES
Threat Actor 2
14
vopus.org
13 URLs
http://www.vopus.org/es/images/cursos/thumbs/tdcanadatr
ust
http://www.vopus.org/ru/media/tdcanadatrust/index.html
http://vopus.org/common/index.htm
http://www.vopus.org/es/images/cursos/thumbs/tdcanadatr
ust/index.html
http://vopus.org/descargas/otros/tdcanadatrust/index.html
Keyword
tdcanadatrust/index.html
19 domains
friooptimo.com, kramerelementary.org, kalblue.com, vopus.org,
artwood.co.kr, stephenpizzuti.com, heatherthinks.com,
corvusseo.com, natikor.by, optioglobal.com, backfire.se, fncl.ma,
greenant.de, mersintenisakademisi.com, cavtel.net
Visual Check
Check in
database
Visual Check
CYXTERA TECHNOLOGIES
Threat Actors Efficiency
15
0.24% 0.69%
4.91%
All Attacks (1,146,441) Threat Actor 1 (1,009) Threat Actor 2 (102)
SimulatingMaliciousAI
CYXTERA TECHNOLOGIES
DeepPhish Algorithm - Training
17
Non Effective URLs
Effective URLs
Encoding
…
…
…
…
…
Model
Az
Rolling
Window
Concatenate
andcreate
Transform
Train
http://www.naylorantiques.com/content/centrais/fone_facil
http://kisanart.com/arendivento/menu-opcoes-fone-facil/
http://naylorantiques.com/atendimento/menu-opcoes-fone-facil/3
http://www.naylorantiques.com/content/centr
ais/fone_facilhttp://kisanart.com/arendivento/
menu-opcoes-fone-
facil/http://naylorantiques.com/atendimento/
menu-opcoes-fone-facil/3
CYXTERA TECHNOLOGIES
DeepPhish LSTM Network
18
URL
h
t
t
p
:
/
/
w
w
w
.
p
a
p
a
y
a
.
c
o
m
One hot
Encoding
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
…
LSTM
LSTM
LSTM
LSTM
Softmax
…
tanH
tanH
tanH
tanH
…
CYXTERA TECHNOLOGIES
DeepPhish Algorithm – Prediction
19
Compromised
Domains
Allowed
Paths
+
Model
Filterpaths
Predict
Next
Character
Iteratively
Synthetic URLs
/arendipemto/nenu-opcines-fone-facil vfone/faci/Atondime+ http:// + www.naylorantiques.com + /arendipemto/nenu-opcines-fone-facilvone/facil/Atondime
Create
CYXTERA TECHNOLOGIES
Simulating Malicious AI using DeepPhish
20
 We selected the two most effective threat
actors.With each subsample of effective URLs
by threat actor, we implemented DeepPhish
algorithm.
CYXTERA TECHNOLOGIES
TraditionalAttacksvs.AI-DrivenAttacks
21
0.69%
20.90%
Traditional DeepPhish
4.91%
36.28%
Traditional DeepPhish
Threat Actor 1 Threat Actor 2
CYXTERA TECHNOLOGIES
Takeaways!
22
AIenhancesattackersefficiencies
ML and AI driven
detection systems
Deep Adversarial
Learning
Relentless
monitoring
Multi-layered
approach to anti-
fraud
CYXTERA TECHNOLOGIES 23
The Power of Adversary AI
 More & Better
Phishing
Attacks
Increasingly Powerful
Self-Spreading Malware
Weaken Authentication
Controls
Cheat Rule-based
Transaction Monitoring
CYXTERA TECHNOLOGIES 24
 1-Minute ResearchVideo Brief
 2 Page Research Summary
 Slides (Extended Version)
 Academic paper
AIvs.AI:CanPredictiveModelsStoptheTideofHackerAI?
www.easysol.net/ai-project
www.cyxtera.com
IvanTorroledo – ivan.torroledo@cyxtera.com
Alejandro Correa Bahnsen – alejandro.correa@cyxtera.com
Luis Camacho – luis.camacho@cyxtera.com

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DeepPhish: Simulating malicious AI

  • 1. DeepPhish Simulating Malicious AI IvanTorroledo – Lead Data Scientist Alejandro Correa Bahnsen –VP, Research Luis Camacho – Lead Research Data Architect
  • 2. CYXTERA TECHNOLOGIES 2  Portfolio of cybersecurity software and services  Intelligent and adaptive  Cloud-native and hybrid-ready  Global colocation leader  57 data centers in 29 global markets  2.6M sq. feet of data center space  195 megawatts of power  3,500 customers  1,100 employees  Headquartered in Miami with offices globally  Experienced leadership in infrastructure and security CyxteraTechnologies
  • 3. CYXTERA TECHNOLOGIES 3 80 % of cyber crimes are being committed by sophisticated attackers The total USA market for cyber insurance is 3B in 2017
  • 6. CYXTERA TECHNOLOGIES AI to Classify Phishing URLs 6  Identify & Classify Malicious URLs and Domains with Prediction - Not Blacklists.  The system calculates the probability of a URL being used to host a phishing attacks using Deep Neural Networks. It correctly classify URLs with over 98% of accuracy.
  • 7. CYXTERA TECHNOLOGIES Long-Short Term Memory Networks 7 URL h t t p : / / w w w . p a p a y a . c o m One hot Encoding … … … … … … … … … … … … … … … … … … … … … Embedding 3.2 1.2 … 1.7 6.4 2.3 … 2.6 6.4 3.0 … 1.7 3.4 2.6 … 3.4 2.6 3.8 … 2.6 3.5 3.2 … 6.4 1.7 4.2 … 6.4 8.6 2.4 … 6.4 4.3 2.9 … 6.4 2.2 3.4 … 3.4 3.2 2.6 … 2.6 4.2 2.2 … 3.5 2.4 3.2 … 1.7 2.9 1.7 … 8.6 3.0 6.4 … 2.6 2.6 6.4 … 3.8 3.8 3.4 … 3.2 3.3 2.6 … 2.2 3.1 2.2 … 2.9 1.8 3.2 … 3.0 2.5 6.4 … 2.6 LSTM LSTM LSTM LSTM Sigmoid …
  • 11. CYXTERA TECHNOLOGIES The Experiment Process Identify individual threat actors Ran them through our own AI detection system Improved their attacks using AI
  • 12. CYXTERA TECHNOLOGIES Uncovering Threat Actors 12  Objective: We want to understand effective patterns of each attacker to improve them through a AI model  As we can not know them directly, we must learn from them through their attacks  Database with 1.1M confirm phishing URLs collected from Phishtank
  • 13. CYXTERA TECHNOLOGIES Threat Actor 1 13 naylorantiques.com 406 URLs http://naylorantiques.com/components/com_contact/vi ews/contact/tmpl/62 http://naylorantiques.com/docs/Auto/Atendimento/5BB ROPI6S3 http://naylorantiques.com/Atualizacao Segura/pictures/XG61YYMT_FXW0PWR8_5P2O7T2U_P9H NDPQR/ http://naylorantiques.com/zifn3p72bsifn9hx9ldecd8jzl2f0 xlwf8f http://www.naylorantiques.com/JavaScript/charset=iso- 8859-1/http-equiv/margin-bottom Keywords atendimento, jsf, identificacao, ponents, views, TV, mail, SHOW, COMPLETO, VILLA, MIX, ufi, pnref, story, tryy2ilr, Autentico 106 domains naylorantiques.com, netshelldemos.com, debbiebright.co.z, waldronfamilygppractice.co.uk , avea-vacances.com , psncodes2013.com uni5.net , 67.228.96.204, classificadosmaster.com.br, ibjjf.org Visual Check Check in database Visual Check
  • 14. CYXTERA TECHNOLOGIES Threat Actor 2 14 vopus.org 13 URLs http://www.vopus.org/es/images/cursos/thumbs/tdcanadatr ust http://www.vopus.org/ru/media/tdcanadatrust/index.html http://vopus.org/common/index.htm http://www.vopus.org/es/images/cursos/thumbs/tdcanadatr ust/index.html http://vopus.org/descargas/otros/tdcanadatrust/index.html Keyword tdcanadatrust/index.html 19 domains friooptimo.com, kramerelementary.org, kalblue.com, vopus.org, artwood.co.kr, stephenpizzuti.com, heatherthinks.com, corvusseo.com, natikor.by, optioglobal.com, backfire.se, fncl.ma, greenant.de, mersintenisakademisi.com, cavtel.net Visual Check Check in database Visual Check
  • 15. CYXTERA TECHNOLOGIES Threat Actors Efficiency 15 0.24% 0.69% 4.91% All Attacks (1,146,441) Threat Actor 1 (1,009) Threat Actor 2 (102)
  • 17. CYXTERA TECHNOLOGIES DeepPhish Algorithm - Training 17 Non Effective URLs Effective URLs Encoding … … … … … Model Az Rolling Window Concatenate andcreate Transform Train http://www.naylorantiques.com/content/centrais/fone_facil http://kisanart.com/arendivento/menu-opcoes-fone-facil/ http://naylorantiques.com/atendimento/menu-opcoes-fone-facil/3 http://www.naylorantiques.com/content/centr ais/fone_facilhttp://kisanart.com/arendivento/ menu-opcoes-fone- facil/http://naylorantiques.com/atendimento/ menu-opcoes-fone-facil/3
  • 18. CYXTERA TECHNOLOGIES DeepPhish LSTM Network 18 URL h t t p : / / w w w . p a p a y a . c o m One hot Encoding … … … … … … … … … … … … … … … … … … … … … LSTM LSTM LSTM LSTM Softmax … tanH tanH tanH tanH …
  • 19. CYXTERA TECHNOLOGIES DeepPhish Algorithm – Prediction 19 Compromised Domains Allowed Paths + Model Filterpaths Predict Next Character Iteratively Synthetic URLs /arendipemto/nenu-opcines-fone-facil vfone/faci/Atondime+ http:// + www.naylorantiques.com + /arendipemto/nenu-opcines-fone-facilvone/facil/Atondime Create
  • 20. CYXTERA TECHNOLOGIES Simulating Malicious AI using DeepPhish 20  We selected the two most effective threat actors.With each subsample of effective URLs by threat actor, we implemented DeepPhish algorithm.
  • 22. CYXTERA TECHNOLOGIES Takeaways! 22 AIenhancesattackersefficiencies ML and AI driven detection systems Deep Adversarial Learning Relentless monitoring Multi-layered approach to anti- fraud
  • 23. CYXTERA TECHNOLOGIES 23 The Power of Adversary AI  More & Better Phishing Attacks Increasingly Powerful Self-Spreading Malware Weaken Authentication Controls Cheat Rule-based Transaction Monitoring
  • 24. CYXTERA TECHNOLOGIES 24  1-Minute ResearchVideo Brief  2 Page Research Summary  Slides (Extended Version)  Academic paper AIvs.AI:CanPredictiveModelsStoptheTideofHackerAI? www.easysol.net/ai-project
  • 25. www.cyxtera.com IvanTorroledo – ivan.torroledo@cyxtera.com Alejandro Correa Bahnsen – alejandro.correa@cyxtera.com Luis Camacho – luis.camacho@cyxtera.com

Editor's Notes

  • #4: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #5: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #7: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #10: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #13: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #14: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #15: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #16: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #18: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #20: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #21: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.
  • #23: Phishing is a form of fraud in which the attacker tries to learn information such as login credentials or account information by masquerading as a reputable entity or person in email, IM or other communication channels.