SlideShare a Scribd company logo
Classifying Phishing
URLs Using Recurrent
Neural Networks
Alejandro Correa Bahnsen, PhD
Chief Data Scientist | Easy Solutions
Agosto 25 y 26 | Lima – Perú 2017
#BIGDATASUMMIT2017
2
A leading global provider of electronic fraud
prevention for financial institutions and
enterprise customers
430+ customers
In 30 countries
115 million
Users protected
30 billion
Online connections monitored
Industry recognition
About Easy Solutions®
Phishing
3
Phishing is the act of defrauding an online
user in order to obtain personal information
by posing as a trustworthy institution or
entity.
Typical Phishing Example
4
Why Phishing Detection is Hard
5
Original Website Only Using Images Subtle Changes
Is It Phishing?
Ideal Phishing Detection System
7
Machine
Learning
Algorithm
Ideal Phishing Detection System - Issues
8
Issues with full content
analysis:
• Time consuming
• Impractical to process
millions of websites per day
• Hard to implement for
small devices
There is always the need for an URL
9
Database of URLs
1,000,000 Phishing URLs from PhishTank
10
http://moviesjingle.com/auto/163.com/index.php
1,000,000 Legitimate URLs from Common Crawl
http://paypal.com.update.account.toughbook.cl/8a30e847925afc597516
1aeabe8930f1/?cmd=_home&dispatch=d09b78f5812945a73610edf38
http://msystemtech.ru/components/com_users/Italy/zz/Login.php?run=
_login-submit&session=68bbd43c854147324d77872062349924
https://www.sanfordhealth.org/ChildrensHealth/Article/73980
http://www.grahamleader.com/ci_25029538/these-are-5-worst-super-
bowl-halftime-shows&defid=1634182
http://www.carolinaguesthouse.co.uk/onlinebooking/?industrytype=1&
startdate=2013-09-05&nights=2&location&productid=25d47a24-6b74
CLASSIFYING PHISHING USING
URL LEXICAL AND
STATISTICAL FREQUENCIES
11
URL Lexical and Statistical Frequencies
12
http://www.papaya.com/secure_login.php
URL length Alexa
Ranking
Path length
URL Entropy
# of .com
Punctuation
count
TLD count
Is IP?
Euclidean
distance
KS & KL
distance
URL Lexical and Statistical Frequencies
13
http://www.papaya.com/secure_login.php
URL length Alexa
Ranking
Path length
URL Entropy
# of .com
Punctuation
count
TLD count
Is IP?
Euclidean
distance
KS & KL
distance
Is It Phishing?
URL Lexical and Statistical Frequencies
14
3-Fold CV Accuracy Recall Precision
Average 93.47% 93.28% 93.64%
Deviation 0.01% 0.02% 0.03%
Results:
URL Lexical and Statistical Frequencies
15
Feature
Importance
MODELING PHISHING URLS
WITH RECURRENT
NEURAL NETWORKS
16
Normal Neural Network
17
Source: https://en.wikipedia.org/wiki/Artificial_neural_network
Recurrent Neural Networks RNN
Have loops!
19
The Problem of Long-Term Dependencies
20
Short term dependencies are easy
long term …
Long-Short Term Memory Networks LSTM
21
RNN contains
a single layer
LSTM contains
four interacting
layers
Source: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
Modeling Architecture for URL Classification
27
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
…
Long-Short Term Memory Networks
28
3-Fold CV Accuracy Recall Precision
Average 98.76% 98.93% 98.60%
Deviation 0.04% 0.02% 0.02%
Results:
Models Comparison
29
90%
91%
92%
93%
94%
95%
96%
97%
98%
99%
100%
Accuracy Recall Precision
Long-Short Term Memory Network Random Forest
Models Comparison
30
Model
Random Forest
Long-Short Term
Memory Network
Memory
Consumption (MB)
289
0.56
Evaluation Time
(URLs per sec)
942
281
Training Time
(minutes)
2.95
238.7
What we learned
• Discerning URLs by their patterns is a good predictor of
phishing websites
• LSTM model shows an overall higher prediction
performance without the need of expert knowledge to
create the features
31
Free to use
32
Thank you!
Any questions or comments, please let me know.
Alejandro Correa Bahnsen, PhD
Chief Data Scientist
acorrea@easysol.net

More Related Content

What's hot

How can Insurers Accelerate Digital Transformation with Data Virtualization (...
How can Insurers Accelerate Digital Transformation with Data Virtualization (...How can Insurers Accelerate Digital Transformation with Data Virtualization (...
How can Insurers Accelerate Digital Transformation with Data Virtualization (...
Denodo
 
The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...
The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...
The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...
The Hive
 
Understanding Big Data Analytics - solutions for growing businesses - Rafał M...
Understanding Big Data Analytics - solutions for growing businesses - Rafał M...Understanding Big Data Analytics - solutions for growing businesses - Rafał M...
Understanding Big Data Analytics - solutions for growing businesses - Rafał M...
GetInData
 
Reinvent Your Data Management Strategy for Successful Digital Transformation
Reinvent Your Data Management Strategy for Successful Digital TransformationReinvent Your Data Management Strategy for Successful Digital Transformation
Reinvent Your Data Management Strategy for Successful Digital Transformation
Denodo
 
Analytics Solutions from SAP
Analytics Solutions from SAPAnalytics Solutions from SAP
Analytics Solutions from SAP
SAP Analytics
 
Weiyan Zhao, Nationwide Insurance - A Decade of Data Science. The Nationwide ...
Weiyan Zhao, Nationwide Insurance - A Decade of Data Science. The Nationwide ...Weiyan Zhao, Nationwide Insurance - A Decade of Data Science. The Nationwide ...
Weiyan Zhao, Nationwide Insurance - A Decade of Data Science. The Nationwide ...
Sri Ambati
 
資産運用とビッグデータ解析_2
資産運用とビッグデータ解析_2資産運用とビッグデータ解析_2
資産運用とビッグデータ解析_2
Deep Learning Lab(ディープラーニング・ラボ)
 
Auto ai for skillsfuture
Auto ai for skillsfuture Auto ai for skillsfuture
Auto ai for skillsfuture
Sunny Panjabi
 
Big Data LDN 2017: Creating ROI from Big Data Investments - Monetizing your B...
Big Data LDN 2017: Creating ROI from Big Data Investments - Monetizing your B...Big Data LDN 2017: Creating ROI from Big Data Investments - Monetizing your B...
Big Data LDN 2017: Creating ROI from Big Data Investments - Monetizing your B...
Matt Stubbs
 
Seminario Big Data
Seminario Big DataSeminario Big Data
Seminario Big Data
Roberto Messora
 
Network visualization for financial crime detection
Network visualization for financial crime detectionNetwork visualization for financial crime detection
Network visualization for financial crime detection
Data Driven Innovation
 
Big Data Industry Insights 2015
Big Data Industry Insights 2015 Big Data Industry Insights 2015
Big Data Industry Insights 2015
Den Reymer
 
AI-SDV 2021: Heiko Wongel - Machine learning tools in patent searching - are ...
AI-SDV 2021: Heiko Wongel - Machine learning tools in patent searching - are ...AI-SDV 2021: Heiko Wongel - Machine learning tools in patent searching - are ...
AI-SDV 2021: Heiko Wongel - Machine learning tools in patent searching - are ...
Dr. Haxel Consult
 
DAMA Webinar: Taking Information Governance to the Next Level
DAMA Webinar: Taking Information Governance to the Next LevelDAMA Webinar: Taking Information Governance to the Next Level
DAMA Webinar: Taking Information Governance to the Next Level
DATAVERSITY
 
Fighting financial fraud at Danske Bank with artificial intelligence
Fighting financial fraud at Danske Bank with artificial intelligenceFighting financial fraud at Danske Bank with artificial intelligence
Fighting financial fraud at Danske Bank with artificial intelligence
Ron Bodkin
 
The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutu...
The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutu...The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutu...
The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutu...
Patrick Van Renterghem
 
Eric van tol
Eric van tolEric van tol
Eric van tol
BigDataExpo
 
Neo4j im Fianzsektor: DIVIZEND
Neo4j im Fianzsektor: DIVIZENDNeo4j im Fianzsektor: DIVIZEND
Neo4j im Fianzsektor: DIVIZEND
Neo4j
 
Healthcare fraud detection
Healthcare fraud detectionHealthcare fraud detection
Healthcare fraud detection
Mahdi Esmailoghli
 
Top 20 artificial intelligence companies to watch out in 2022
Top 20 artificial intelligence companies to watch out in 2022Top 20 artificial intelligence companies to watch out in 2022
Top 20 artificial intelligence companies to watch out in 2022
Kavika Roy
 

What's hot (20)

How can Insurers Accelerate Digital Transformation with Data Virtualization (...
How can Insurers Accelerate Digital Transformation with Data Virtualization (...How can Insurers Accelerate Digital Transformation with Data Virtualization (...
How can Insurers Accelerate Digital Transformation with Data Virtualization (...
 
The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...
The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...
The Hive Think Tank: Talk by Mohandas Pai - India at 2030, How Tech Entrepren...
 
Understanding Big Data Analytics - solutions for growing businesses - Rafał M...
Understanding Big Data Analytics - solutions for growing businesses - Rafał M...Understanding Big Data Analytics - solutions for growing businesses - Rafał M...
Understanding Big Data Analytics - solutions for growing businesses - Rafał M...
 
Reinvent Your Data Management Strategy for Successful Digital Transformation
Reinvent Your Data Management Strategy for Successful Digital TransformationReinvent Your Data Management Strategy for Successful Digital Transformation
Reinvent Your Data Management Strategy for Successful Digital Transformation
 
Analytics Solutions from SAP
Analytics Solutions from SAPAnalytics Solutions from SAP
Analytics Solutions from SAP
 
Weiyan Zhao, Nationwide Insurance - A Decade of Data Science. The Nationwide ...
Weiyan Zhao, Nationwide Insurance - A Decade of Data Science. The Nationwide ...Weiyan Zhao, Nationwide Insurance - A Decade of Data Science. The Nationwide ...
Weiyan Zhao, Nationwide Insurance - A Decade of Data Science. The Nationwide ...
 
資産運用とビッグデータ解析_2
資産運用とビッグデータ解析_2資産運用とビッグデータ解析_2
資産運用とビッグデータ解析_2
 
Auto ai for skillsfuture
Auto ai for skillsfuture Auto ai for skillsfuture
Auto ai for skillsfuture
 
Big Data LDN 2017: Creating ROI from Big Data Investments - Monetizing your B...
Big Data LDN 2017: Creating ROI from Big Data Investments - Monetizing your B...Big Data LDN 2017: Creating ROI from Big Data Investments - Monetizing your B...
Big Data LDN 2017: Creating ROI from Big Data Investments - Monetizing your B...
 
Seminario Big Data
Seminario Big DataSeminario Big Data
Seminario Big Data
 
Network visualization for financial crime detection
Network visualization for financial crime detectionNetwork visualization for financial crime detection
Network visualization for financial crime detection
 
Big Data Industry Insights 2015
Big Data Industry Insights 2015 Big Data Industry Insights 2015
Big Data Industry Insights 2015
 
AI-SDV 2021: Heiko Wongel - Machine learning tools in patent searching - are ...
AI-SDV 2021: Heiko Wongel - Machine learning tools in patent searching - are ...AI-SDV 2021: Heiko Wongel - Machine learning tools in patent searching - are ...
AI-SDV 2021: Heiko Wongel - Machine learning tools in patent searching - are ...
 
DAMA Webinar: Taking Information Governance to the Next Level
DAMA Webinar: Taking Information Governance to the Next LevelDAMA Webinar: Taking Information Governance to the Next Level
DAMA Webinar: Taking Information Governance to the Next Level
 
Fighting financial fraud at Danske Bank with artificial intelligence
Fighting financial fraud at Danske Bank with artificial intelligenceFighting financial fraud at Danske Bank with artificial intelligence
Fighting financial fraud at Danske Bank with artificial intelligence
 
The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutu...
The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutu...The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutu...
The Future is Data-Centric (presented by Jan Henderyckx of Inpuls at #TheFutu...
 
Eric van tol
Eric van tolEric van tol
Eric van tol
 
Neo4j im Fianzsektor: DIVIZEND
Neo4j im Fianzsektor: DIVIZENDNeo4j im Fianzsektor: DIVIZEND
Neo4j im Fianzsektor: DIVIZEND
 
Healthcare fraud detection
Healthcare fraud detectionHealthcare fraud detection
Healthcare fraud detection
 
Top 20 artificial intelligence companies to watch out in 2022
Top 20 artificial intelligence companies to watch out in 2022Top 20 artificial intelligence companies to watch out in 2022
Top 20 artificial intelligence companies to watch out in 2022
 

Viewers also liked

BDAS-2017 | Comprendiendo nuestras motivaciones a través de Big Data
BDAS-2017 | Comprendiendo nuestras motivaciones a través de Big DataBDAS-2017 | Comprendiendo nuestras motivaciones a través de Big Data
BDAS-2017 | Comprendiendo nuestras motivaciones a través de Big Data
Big-Data-Summit
 
BDAS-2017 | Lesson learned from the application of data science at BBVA
BDAS-2017 | Lesson learned from the application of data science at BBVABDAS-2017 | Lesson learned from the application of data science at BBVA
BDAS-2017 | Lesson learned from the application of data science at BBVA
Big-Data-Summit
 
BDAS-2017 | Conozca la plataforma ideal para un procesamiento analítico sin p...
BDAS-2017 | Conozca la plataforma ideal para un procesamiento analítico sin p...BDAS-2017 | Conozca la plataforma ideal para un procesamiento analítico sin p...
BDAS-2017 | Conozca la plataforma ideal para un procesamiento analítico sin p...
Big-Data-Summit
 
BDAS-2017 | Big Bilbao: Big Data e Internet of Things para la promoción econó...
BDAS-2017 | Big Bilbao: Big Data e Internet of Things para la promoción econó...BDAS-2017 | Big Bilbao: Big Data e Internet of Things para la promoción econó...
BDAS-2017 | Big Bilbao: Big Data e Internet of Things para la promoción econó...
Big-Data-Summit
 
BDAS-2017 | IoT Analytics: Analítica de datos provenientes del Internet de la...
BDAS-2017 | IoT Analytics: Analítica de datos provenientes del Internet de la...BDAS-2017 | IoT Analytics: Analítica de datos provenientes del Internet de la...
BDAS-2017 | IoT Analytics: Analítica de datos provenientes del Internet de la...
Big-Data-Summit
 
BDAS-2017 | De los Bots a las Arquitecturas Cognitivas
BDAS-2017 | De los Bots a las Arquitecturas CognitivasBDAS-2017 | De los Bots a las Arquitecturas Cognitivas
BDAS-2017 | De los Bots a las Arquitecturas Cognitivas
Big-Data-Summit
 
BDAS-2017 | Deep Learning para Extracción de Valor en Contenidos Digitales
BDAS-2017 | Deep Learning para Extracción de Valor en Contenidos DigitalesBDAS-2017 | Deep Learning para Extracción de Valor en Contenidos Digitales
BDAS-2017 | Deep Learning para Extracción de Valor en Contenidos Digitales
Big-Data-Summit
 
BDAS-2017 | Maximizing a churn campaign’s profitability with cost sensitive m...
BDAS-2017 | Maximizing a churn campaign’s profitability with cost sensitive m...BDAS-2017 | Maximizing a churn campaign’s profitability with cost sensitive m...
BDAS-2017 | Maximizing a churn campaign’s profitability with cost sensitive m...
Big-Data-Summit
 
BDAS-2017 | Evolución de Open Data en el desarrollo de las ciudades inteligentes
BDAS-2017 | Evolución de Open Data en el desarrollo de las ciudades inteligentesBDAS-2017 | Evolución de Open Data en el desarrollo de las ciudades inteligentes
BDAS-2017 | Evolución de Open Data en el desarrollo de las ciudades inteligentes
Big-Data-Summit
 
Proyectos Big Data en Healthcare
Proyectos Big Data en HealthcareProyectos Big Data en Healthcare
Proyectos Big Data en Healthcare
Big-Data-Summit
 
BDAS-2017 | Innovación con base en datos en Silicon Valley y Latino América
BDAS-2017 | Innovación con base en datos en Silicon Valley y Latino AméricaBDAS-2017 | Innovación con base en datos en Silicon Valley y Latino América
BDAS-2017 | Innovación con base en datos en Silicon Valley y Latino América
Big-Data-Summit
 
BDAS-2017 | DMC Challengue concurso satisfacción universidad
BDAS-2017 | DMC Challengue concurso satisfacción universidadBDAS-2017 | DMC Challengue concurso satisfacción universidad
BDAS-2017 | DMC Challengue concurso satisfacción universidad
Big-Data-Summit
 
BDAS-2017 | Organizaciones Orientadas al dato
BDAS-2017 | Organizaciones Orientadas al datoBDAS-2017 | Organizaciones Orientadas al dato
BDAS-2017 | Organizaciones Orientadas al dato
Big-Data-Summit
 
BDAS-2017 | Hanldling Target Bias in Predictive Modelling
BDAS-2017 | Hanldling Target Bias in Predictive ModellingBDAS-2017 | Hanldling Target Bias in Predictive Modelling
BDAS-2017 | Hanldling Target Bias in Predictive Modelling
Big-Data-Summit
 
BDAS-2017 | Convergencia entre Open Data y Big Data, casos y tendencias
BDAS-2017 | Convergencia entre Open Data y Big Data, casos y tendenciasBDAS-2017 | Convergencia entre Open Data y Big Data, casos y tendencias
BDAS-2017 | Convergencia entre Open Data y Big Data, casos y tendencias
Big-Data-Summit
 
BDAS-2017 | Big Data y Retail: fidelizando a mis clientes en un entorno compe...
BDAS-2017 | Big Data y Retail: fidelizando a mis clientes en un entorno compe...BDAS-2017 | Big Data y Retail: fidelizando a mis clientes en un entorno compe...
BDAS-2017 | Big Data y Retail: fidelizando a mis clientes en un entorno compe...
Big-Data-Summit
 

Viewers also liked (16)

BDAS-2017 | Comprendiendo nuestras motivaciones a través de Big Data
BDAS-2017 | Comprendiendo nuestras motivaciones a través de Big DataBDAS-2017 | Comprendiendo nuestras motivaciones a través de Big Data
BDAS-2017 | Comprendiendo nuestras motivaciones a través de Big Data
 
BDAS-2017 | Lesson learned from the application of data science at BBVA
BDAS-2017 | Lesson learned from the application of data science at BBVABDAS-2017 | Lesson learned from the application of data science at BBVA
BDAS-2017 | Lesson learned from the application of data science at BBVA
 
BDAS-2017 | Conozca la plataforma ideal para un procesamiento analítico sin p...
BDAS-2017 | Conozca la plataforma ideal para un procesamiento analítico sin p...BDAS-2017 | Conozca la plataforma ideal para un procesamiento analítico sin p...
BDAS-2017 | Conozca la plataforma ideal para un procesamiento analítico sin p...
 
BDAS-2017 | Big Bilbao: Big Data e Internet of Things para la promoción econó...
BDAS-2017 | Big Bilbao: Big Data e Internet of Things para la promoción econó...BDAS-2017 | Big Bilbao: Big Data e Internet of Things para la promoción econó...
BDAS-2017 | Big Bilbao: Big Data e Internet of Things para la promoción econó...
 
BDAS-2017 | IoT Analytics: Analítica de datos provenientes del Internet de la...
BDAS-2017 | IoT Analytics: Analítica de datos provenientes del Internet de la...BDAS-2017 | IoT Analytics: Analítica de datos provenientes del Internet de la...
BDAS-2017 | IoT Analytics: Analítica de datos provenientes del Internet de la...
 
BDAS-2017 | De los Bots a las Arquitecturas Cognitivas
BDAS-2017 | De los Bots a las Arquitecturas CognitivasBDAS-2017 | De los Bots a las Arquitecturas Cognitivas
BDAS-2017 | De los Bots a las Arquitecturas Cognitivas
 
BDAS-2017 | Deep Learning para Extracción de Valor en Contenidos Digitales
BDAS-2017 | Deep Learning para Extracción de Valor en Contenidos DigitalesBDAS-2017 | Deep Learning para Extracción de Valor en Contenidos Digitales
BDAS-2017 | Deep Learning para Extracción de Valor en Contenidos Digitales
 
BDAS-2017 | Maximizing a churn campaign’s profitability with cost sensitive m...
BDAS-2017 | Maximizing a churn campaign’s profitability with cost sensitive m...BDAS-2017 | Maximizing a churn campaign’s profitability with cost sensitive m...
BDAS-2017 | Maximizing a churn campaign’s profitability with cost sensitive m...
 
BDAS-2017 | Evolución de Open Data en el desarrollo de las ciudades inteligentes
BDAS-2017 | Evolución de Open Data en el desarrollo de las ciudades inteligentesBDAS-2017 | Evolución de Open Data en el desarrollo de las ciudades inteligentes
BDAS-2017 | Evolución de Open Data en el desarrollo de las ciudades inteligentes
 
Proyectos Big Data en Healthcare
Proyectos Big Data en HealthcareProyectos Big Data en Healthcare
Proyectos Big Data en Healthcare
 
BDAS-2017 | Innovación con base en datos en Silicon Valley y Latino América
BDAS-2017 | Innovación con base en datos en Silicon Valley y Latino AméricaBDAS-2017 | Innovación con base en datos en Silicon Valley y Latino América
BDAS-2017 | Innovación con base en datos en Silicon Valley y Latino América
 
BDAS-2017 | DMC Challengue concurso satisfacción universidad
BDAS-2017 | DMC Challengue concurso satisfacción universidadBDAS-2017 | DMC Challengue concurso satisfacción universidad
BDAS-2017 | DMC Challengue concurso satisfacción universidad
 
BDAS-2017 | Organizaciones Orientadas al dato
BDAS-2017 | Organizaciones Orientadas al datoBDAS-2017 | Organizaciones Orientadas al dato
BDAS-2017 | Organizaciones Orientadas al dato
 
BDAS-2017 | Hanldling Target Bias in Predictive Modelling
BDAS-2017 | Hanldling Target Bias in Predictive ModellingBDAS-2017 | Hanldling Target Bias in Predictive Modelling
BDAS-2017 | Hanldling Target Bias in Predictive Modelling
 
BDAS-2017 | Convergencia entre Open Data y Big Data, casos y tendencias
BDAS-2017 | Convergencia entre Open Data y Big Data, casos y tendenciasBDAS-2017 | Convergencia entre Open Data y Big Data, casos y tendencias
BDAS-2017 | Convergencia entre Open Data y Big Data, casos y tendencias
 
BDAS-2017 | Big Data y Retail: fidelizando a mis clientes en un entorno compe...
BDAS-2017 | Big Data y Retail: fidelizando a mis clientes en un entorno compe...BDAS-2017 | Big Data y Retail: fidelizando a mis clientes en un entorno compe...
BDAS-2017 | Big Data y Retail: fidelizando a mis clientes en un entorno compe...
 

Similar to BDAS-2017 | Deep Neural Networks Para la Detección de Phishing

Classifying Phishing URLs Using Recurrent Neural Networks
Classifying Phishing URLs Using Recurrent Neural NetworksClassifying Phishing URLs Using Recurrent Neural Networks
Classifying Phishing URLs Using Recurrent Neural Networks
Alejandro Correa Bahnsen, PhD
 
Industrial Control System Network Cyber Security Monitoring Solution (SCAB)
Industrial Control System Network Cyber Security Monitoring Solution (SCAB)Industrial Control System Network Cyber Security Monitoring Solution (SCAB)
Industrial Control System Network Cyber Security Monitoring Solution (SCAB)
Enrique Martin
 
Nasscom how can you identify fraud in fintech lending using deep learning
Nasscom how can you identify fraud in fintech lending using deep learningNasscom how can you identify fraud in fintech lending using deep learning
Nasscom how can you identify fraud in fintech lending using deep learning
Ratnakar Pandey
 
Cyber security
Cyber securityCyber security
Cyber security
Aman Pradhan
 
Real-life Application of a Decentralized System
Real-life Application of a Decentralized SystemReal-life Application of a Decentralized System
Real-life Application of a Decentralized System
IRJET Journal
 
Disruptionware-TRustedCISO103020v0.7.pptx
Disruptionware-TRustedCISO103020v0.7.pptxDisruptionware-TRustedCISO103020v0.7.pptx
Disruptionware-TRustedCISO103020v0.7.pptx
Debra Baker, CISSP CSSP
 
BLOCKHUNTER.pptx
BLOCKHUNTER.pptxBLOCKHUNTER.pptx
BLOCKHUNTER.pptx
BhanuCharan9
 
IRJET - Improving Password System using Blockchain
IRJET - Improving Password System using BlockchainIRJET - Improving Password System using Blockchain
IRJET - Improving Password System using Blockchain
IRJET Journal
 
Blockchain startup
Blockchain startupBlockchain startup
Blockchain startup
Sota Watanabe
 
Hhewitt Networksecurity ...
Hhewitt Networksecurity                                                      ...Hhewitt Networksecurity                                                      ...
Hhewitt Networksecurity ...
Errol Hewitt. MSCP, BA
 
AuthentiThings: The Pitfalls and Promises of Authentication in the IoT
AuthentiThings: The Pitfalls and Promises of Authentication in the IoTAuthentiThings: The Pitfalls and Promises of Authentication in the IoT
AuthentiThings: The Pitfalls and Promises of Authentication in the IoT
TransUnion
 
CLASS 2018 - Palestra de Edgard Capdevielle (Presidente e CEO – Nozomi)
CLASS 2018 - Palestra de Edgard Capdevielle (Presidente e CEO – Nozomi)CLASS 2018 - Palestra de Edgard Capdevielle (Presidente e CEO – Nozomi)
CLASS 2018 - Palestra de Edgard Capdevielle (Presidente e CEO – Nozomi)
TI Safe
 
InfiniteChain White Paper
InfiniteChain White Paper InfiniteChain White Paper
InfiniteChain White Paper
InfiniteChain
 
Kudler Fine Foods Network Analysis
Kudler Fine Foods Network AnalysisKudler Fine Foods Network Analysis
Kudler Fine Foods Network Analysis
Kristen Stacey
 
Creating an Effective Network Sniffer
Creating an Effective Network SnifferCreating an Effective Network Sniffer
Creating an Effective Network Sniffer
ijtsrd
 
Network security monitoring elastic webinar - 16 june 2021
Network security monitoring   elastic webinar - 16 june 2021Network security monitoring   elastic webinar - 16 june 2021
Network security monitoring elastic webinar - 16 june 2021
Mouaz Alnouri
 
Shaping a Digital Vision
Shaping a Digital VisionShaping a Digital Vision
Shaping a Digital Vision
DataWorks Summit/Hadoop Summit
 
Blockchain and its impact on Data Science and Financial Services
Blockchain and its impact on Data Science and Financial ServicesBlockchain and its impact on Data Science and Financial Services
Blockchain and its impact on Data Science and Financial Services
Ratnakar Pandey
 
IRJET- Secure Online Voting Systems using Block of Chunks
IRJET-  	  Secure Online Voting Systems using Block of ChunksIRJET-  	  Secure Online Voting Systems using Block of Chunks
IRJET- Secure Online Voting Systems using Block of Chunks
IRJET Journal
 
Introduction to Ion – a layer 2 network for Decentralized Identifiers with Bi...
Introduction to Ion – a layer 2 network for Decentralized Identifiers with Bi...Introduction to Ion – a layer 2 network for Decentralized Identifiers with Bi...
Introduction to Ion – a layer 2 network for Decentralized Identifiers with Bi...
SSIMeetup
 

Similar to BDAS-2017 | Deep Neural Networks Para la Detección de Phishing (20)

Classifying Phishing URLs Using Recurrent Neural Networks
Classifying Phishing URLs Using Recurrent Neural NetworksClassifying Phishing URLs Using Recurrent Neural Networks
Classifying Phishing URLs Using Recurrent Neural Networks
 
Industrial Control System Network Cyber Security Monitoring Solution (SCAB)
Industrial Control System Network Cyber Security Monitoring Solution (SCAB)Industrial Control System Network Cyber Security Monitoring Solution (SCAB)
Industrial Control System Network Cyber Security Monitoring Solution (SCAB)
 
Nasscom how can you identify fraud in fintech lending using deep learning
Nasscom how can you identify fraud in fintech lending using deep learningNasscom how can you identify fraud in fintech lending using deep learning
Nasscom how can you identify fraud in fintech lending using deep learning
 
Cyber security
Cyber securityCyber security
Cyber security
 
Real-life Application of a Decentralized System
Real-life Application of a Decentralized SystemReal-life Application of a Decentralized System
Real-life Application of a Decentralized System
 
Disruptionware-TRustedCISO103020v0.7.pptx
Disruptionware-TRustedCISO103020v0.7.pptxDisruptionware-TRustedCISO103020v0.7.pptx
Disruptionware-TRustedCISO103020v0.7.pptx
 
BLOCKHUNTER.pptx
BLOCKHUNTER.pptxBLOCKHUNTER.pptx
BLOCKHUNTER.pptx
 
IRJET - Improving Password System using Blockchain
IRJET - Improving Password System using BlockchainIRJET - Improving Password System using Blockchain
IRJET - Improving Password System using Blockchain
 
Blockchain startup
Blockchain startupBlockchain startup
Blockchain startup
 
Hhewitt Networksecurity ...
Hhewitt Networksecurity                                                      ...Hhewitt Networksecurity                                                      ...
Hhewitt Networksecurity ...
 
AuthentiThings: The Pitfalls and Promises of Authentication in the IoT
AuthentiThings: The Pitfalls and Promises of Authentication in the IoTAuthentiThings: The Pitfalls and Promises of Authentication in the IoT
AuthentiThings: The Pitfalls and Promises of Authentication in the IoT
 
CLASS 2018 - Palestra de Edgard Capdevielle (Presidente e CEO – Nozomi)
CLASS 2018 - Palestra de Edgard Capdevielle (Presidente e CEO – Nozomi)CLASS 2018 - Palestra de Edgard Capdevielle (Presidente e CEO – Nozomi)
CLASS 2018 - Palestra de Edgard Capdevielle (Presidente e CEO – Nozomi)
 
InfiniteChain White Paper
InfiniteChain White Paper InfiniteChain White Paper
InfiniteChain White Paper
 
Kudler Fine Foods Network Analysis
Kudler Fine Foods Network AnalysisKudler Fine Foods Network Analysis
Kudler Fine Foods Network Analysis
 
Creating an Effective Network Sniffer
Creating an Effective Network SnifferCreating an Effective Network Sniffer
Creating an Effective Network Sniffer
 
Network security monitoring elastic webinar - 16 june 2021
Network security monitoring   elastic webinar - 16 june 2021Network security monitoring   elastic webinar - 16 june 2021
Network security monitoring elastic webinar - 16 june 2021
 
Shaping a Digital Vision
Shaping a Digital VisionShaping a Digital Vision
Shaping a Digital Vision
 
Blockchain and its impact on Data Science and Financial Services
Blockchain and its impact on Data Science and Financial ServicesBlockchain and its impact on Data Science and Financial Services
Blockchain and its impact on Data Science and Financial Services
 
IRJET- Secure Online Voting Systems using Block of Chunks
IRJET-  	  Secure Online Voting Systems using Block of ChunksIRJET-  	  Secure Online Voting Systems using Block of Chunks
IRJET- Secure Online Voting Systems using Block of Chunks
 
Introduction to Ion – a layer 2 network for Decentralized Identifiers with Bi...
Introduction to Ion – a layer 2 network for Decentralized Identifiers with Bi...Introduction to Ion – a layer 2 network for Decentralized Identifiers with Bi...
Introduction to Ion – a layer 2 network for Decentralized Identifiers with Bi...
 

More from Big-Data-Summit

SafeHomeFace - Sistema de reconocimiento facial.
SafeHomeFace - Sistema de reconocimiento facial.SafeHomeFace - Sistema de reconocimiento facial.
SafeHomeFace - Sistema de reconocimiento facial.
Big-Data-Summit
 
Las 10 tendencias principales de BI para el 2018 - Carloz Díaz
Las 10 tendencias principales de BI para el 2018 - Carloz DíazLas 10 tendencias principales de BI para el 2018 - Carloz Díaz
Las 10 tendencias principales de BI para el 2018 - Carloz Díaz
Big-Data-Summit
 
El big data analytics donde menos te lo esperas - Alex Rayón
El big data analytics donde menos te lo esperas - Alex RayónEl big data analytics donde menos te lo esperas - Alex Rayón
El big data analytics donde menos te lo esperas - Alex Rayón
Big-Data-Summit
 
Big Data en el sector inmobiliario - Gonzalo Martín
Big Data en el sector inmobiliario - Gonzalo MartínBig Data en el sector inmobiliario - Gonzalo Martín
Big Data en el sector inmobiliario - Gonzalo Martín
Big-Data-Summit
 
Modelo Operativo para grandes proyectos de AI - Ignacio Marrero
Modelo Operativo para grandes proyectos de AI - Ignacio MarreroModelo Operativo para grandes proyectos de AI - Ignacio Marrero
Modelo Operativo para grandes proyectos de AI - Ignacio Marrero
Big-Data-Summit
 
La evolución de la analítica descriptiva - Diego Aguirre
La evolución de la analítica descriptiva - Diego AguirreLa evolución de la analítica descriptiva - Diego Aguirre
La evolución de la analítica descriptiva - Diego Aguirre
Big-Data-Summit
 
El dato tiene forma y la forma significado - Josep Curto
El dato tiene forma y la forma significado - Josep CurtoEl dato tiene forma y la forma significado - Josep Curto
El dato tiene forma y la forma significado - Josep Curto
Big-Data-Summit
 
BDAS-2017 | sanselix jobranke_rpptx
BDAS-2017 | sanselix jobranke_rpptxBDAS-2017 | sanselix jobranke_rpptx
BDAS-2017 | sanselix jobranke_rpptx
Big-Data-Summit
 
BDAS-2017 | Analitica visual presentación mlms2
BDAS-2017 | Analitica visual presentación mlms2BDAS-2017 | Analitica visual presentación mlms2
BDAS-2017 | Analitica visual presentación mlms2
Big-Data-Summit
 
BDAS-2017 | Comunidad Data Science
BDAS-2017 | Comunidad Data ScienceBDAS-2017 | Comunidad Data Science
BDAS-2017 | Comunidad Data Science
Big-Data-Summit
 
Modelos Predictivos, Big Data Retos y Generación de nuevas soluciones
Modelos Predictivos, Big Data Retos y Generación de nuevas solucionesModelos Predictivos, Big Data Retos y Generación de nuevas soluciones
Modelos Predictivos, Big Data Retos y Generación de nuevas soluciones
Big-Data-Summit
 
Estrategias omnicanal para la mejora de los procesos de comunicación y marke...
	Estrategias omnicanal para la mejora de los procesos de comunicación y marke...	Estrategias omnicanal para la mejora de los procesos de comunicación y marke...
Estrategias omnicanal para la mejora de los procesos de comunicación y marke...
Big-Data-Summit
 
Convergencia de Analítica con la Experiencia Digital
Convergencia de Analítica con la Experiencia DigitalConvergencia de Analítica con la Experiencia Digital
Convergencia de Analítica con la Experiencia Digital
Big-Data-Summit
 

More from Big-Data-Summit (13)

SafeHomeFace - Sistema de reconocimiento facial.
SafeHomeFace - Sistema de reconocimiento facial.SafeHomeFace - Sistema de reconocimiento facial.
SafeHomeFace - Sistema de reconocimiento facial.
 
Las 10 tendencias principales de BI para el 2018 - Carloz Díaz
Las 10 tendencias principales de BI para el 2018 - Carloz DíazLas 10 tendencias principales de BI para el 2018 - Carloz Díaz
Las 10 tendencias principales de BI para el 2018 - Carloz Díaz
 
El big data analytics donde menos te lo esperas - Alex Rayón
El big data analytics donde menos te lo esperas - Alex RayónEl big data analytics donde menos te lo esperas - Alex Rayón
El big data analytics donde menos te lo esperas - Alex Rayón
 
Big Data en el sector inmobiliario - Gonzalo Martín
Big Data en el sector inmobiliario - Gonzalo MartínBig Data en el sector inmobiliario - Gonzalo Martín
Big Data en el sector inmobiliario - Gonzalo Martín
 
Modelo Operativo para grandes proyectos de AI - Ignacio Marrero
Modelo Operativo para grandes proyectos de AI - Ignacio MarreroModelo Operativo para grandes proyectos de AI - Ignacio Marrero
Modelo Operativo para grandes proyectos de AI - Ignacio Marrero
 
La evolución de la analítica descriptiva - Diego Aguirre
La evolución de la analítica descriptiva - Diego AguirreLa evolución de la analítica descriptiva - Diego Aguirre
La evolución de la analítica descriptiva - Diego Aguirre
 
El dato tiene forma y la forma significado - Josep Curto
El dato tiene forma y la forma significado - Josep CurtoEl dato tiene forma y la forma significado - Josep Curto
El dato tiene forma y la forma significado - Josep Curto
 
BDAS-2017 | sanselix jobranke_rpptx
BDAS-2017 | sanselix jobranke_rpptxBDAS-2017 | sanselix jobranke_rpptx
BDAS-2017 | sanselix jobranke_rpptx
 
BDAS-2017 | Analitica visual presentación mlms2
BDAS-2017 | Analitica visual presentación mlms2BDAS-2017 | Analitica visual presentación mlms2
BDAS-2017 | Analitica visual presentación mlms2
 
BDAS-2017 | Comunidad Data Science
BDAS-2017 | Comunidad Data ScienceBDAS-2017 | Comunidad Data Science
BDAS-2017 | Comunidad Data Science
 
Modelos Predictivos, Big Data Retos y Generación de nuevas soluciones
Modelos Predictivos, Big Data Retos y Generación de nuevas solucionesModelos Predictivos, Big Data Retos y Generación de nuevas soluciones
Modelos Predictivos, Big Data Retos y Generación de nuevas soluciones
 
Estrategias omnicanal para la mejora de los procesos de comunicación y marke...
	Estrategias omnicanal para la mejora de los procesos de comunicación y marke...	Estrategias omnicanal para la mejora de los procesos de comunicación y marke...
Estrategias omnicanal para la mejora de los procesos de comunicación y marke...
 
Convergencia de Analítica con la Experiencia Digital
Convergencia de Analítica con la Experiencia DigitalConvergencia de Analítica con la Experiencia Digital
Convergencia de Analítica con la Experiencia Digital
 

Recently uploaded

一比一原版(UofT毕业证)多伦多大学毕业证如何办理
一比一原版(UofT毕业证)多伦多大学毕业证如何办理一比一原版(UofT毕业证)多伦多大学毕业证如何办理
一比一原版(UofT毕业证)多伦多大学毕业证如何办理
exukyp
 
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative ClassifiersML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
MastanaihnaiduYasam
 
社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .
NABLAS株式会社
 
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
mkkikqvo
 
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docxDATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
SaffaIbrahim1
 
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
Vietnam Cotton & Spinning Association
 
A gentle exploration of Retrieval Augmented Generation
A gentle exploration of Retrieval Augmented GenerationA gentle exploration of Retrieval Augmented Generation
A gentle exploration of Retrieval Augmented Generation
dataschool1
 
一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
bmucuha
 
DSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelinesDSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelines
Timothy Spann
 
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCAModule 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
yuvarajkumar334
 
一比一原版卡尔加里大学毕业证(uc毕业证)如何办理
一比一原版卡尔加里大学毕业证(uc毕业证)如何办理一比一原版卡尔加里大学毕业证(uc毕业证)如何办理
一比一原版卡尔加里大学毕业证(uc毕业证)如何办理
oaxefes
 
[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024
Vietnam Cotton & Spinning Association
 
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
nyvan3
 
Drownings spike from May to August in children
Drownings spike from May to August in childrenDrownings spike from May to August in children
Drownings spike from May to August in children
Bisnar Chase Personal Injury Attorneys
 
Template xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptxTemplate xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptx
TeukuEriSyahputra
 
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptxREUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
KiriakiENikolaidou
 
一比一原版马来西亚博特拉大学毕业证(upm毕业证)如何办理
一比一原版马来西亚博特拉大学毕业证(upm毕业证)如何办理一比一原版马来西亚博特拉大学毕业证(upm毕业证)如何办理
一比一原版马来西亚博特拉大学毕业证(upm毕业证)如何办理
eudsoh
 
一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理
一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理
一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理
1tyxnjpia
 
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
osoyvvf
 
Cell The Unit of Life for NEET Multiple Choice Questions.docx
Cell The Unit of Life for NEET Multiple Choice Questions.docxCell The Unit of Life for NEET Multiple Choice Questions.docx
Cell The Unit of Life for NEET Multiple Choice Questions.docx
vasanthatpuram
 

Recently uploaded (20)

一比一原版(UofT毕业证)多伦多大学毕业证如何办理
一比一原版(UofT毕业证)多伦多大学毕业证如何办理一比一原版(UofT毕业证)多伦多大学毕业证如何办理
一比一原版(UofT毕业证)多伦多大学毕业证如何办理
 
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative ClassifiersML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
ML-PPT-UNIT-2 Generative Classifiers Discriminative Classifiers
 
社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .社内勉強会資料_Hallucination of LLMs               .
社内勉強会資料_Hallucination of LLMs               .
 
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
原版一比一多伦多大学毕业证(UofT毕业证书)如何办理
 
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docxDATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
DATA COMMS-NETWORKS YR2 lecture 08 NAT & CLOUD.docx
 
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics March 2024
 
A gentle exploration of Retrieval Augmented Generation
A gentle exploration of Retrieval Augmented GenerationA gentle exploration of Retrieval Augmented Generation
A gentle exploration of Retrieval Augmented Generation
 
一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理一比一原版(UO毕业证)渥太华大学毕业证如何办理
一比一原版(UO毕业证)渥太华大学毕业证如何办理
 
DSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelinesDSSML24_tspann_CodelessGenerativeAIPipelines
DSSML24_tspann_CodelessGenerativeAIPipelines
 
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCAModule 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
Module 1 ppt BIG DATA ANALYTICS NOTES FOR MCA
 
一比一原版卡尔加里大学毕业证(uc毕业证)如何办理
一比一原版卡尔加里大学毕业证(uc毕业证)如何办理一比一原版卡尔加里大学毕业证(uc毕业证)如何办理
一比一原版卡尔加里大学毕业证(uc毕业证)如何办理
 
[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024
[VCOSA] Monthly Report - Cotton & Yarn Statistics May 2024
 
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
一比一原版英国赫特福德大学毕业证(hertfordshire毕业证书)如何办理
 
Drownings spike from May to August in children
Drownings spike from May to August in childrenDrownings spike from May to August in children
Drownings spike from May to August in children
 
Template xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptxTemplate xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptx
 
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptxREUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
REUSE-SCHOOL-DATA-INTEGRATED-SYSTEMS.pptx
 
一比一原版马来西亚博特拉大学毕业证(upm毕业证)如何办理
一比一原版马来西亚博特拉大学毕业证(upm毕业证)如何办理一比一原版马来西亚博特拉大学毕业证(upm毕业证)如何办理
一比一原版马来西亚博特拉大学毕业证(upm毕业证)如何办理
 
一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理
一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理
一比一原版(Sheffield毕业证书)谢菲尔德大学毕业证如何办理
 
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
一比一原版(uom毕业证书)曼彻斯特大学毕业证如何办理
 
Cell The Unit of Life for NEET Multiple Choice Questions.docx
Cell The Unit of Life for NEET Multiple Choice Questions.docxCell The Unit of Life for NEET Multiple Choice Questions.docx
Cell The Unit of Life for NEET Multiple Choice Questions.docx
 

BDAS-2017 | Deep Neural Networks Para la Detección de Phishing

  • 1. Classifying Phishing URLs Using Recurrent Neural Networks Alejandro Correa Bahnsen, PhD Chief Data Scientist | Easy Solutions Agosto 25 y 26 | Lima – Perú 2017 #BIGDATASUMMIT2017
  • 2. 2 A leading global provider of electronic fraud prevention for financial institutions and enterprise customers 430+ customers In 30 countries 115 million Users protected 30 billion Online connections monitored Industry recognition About Easy Solutions®
  • 3. Phishing 3 Phishing is the act of defrauding an online user in order to obtain personal information by posing as a trustworthy institution or entity.
  • 5. Why Phishing Detection is Hard 5 Original Website Only Using Images Subtle Changes
  • 6.
  • 7. Is It Phishing? Ideal Phishing Detection System 7 Machine Learning Algorithm
  • 8. Ideal Phishing Detection System - Issues 8 Issues with full content analysis: • Time consuming • Impractical to process millions of websites per day • Hard to implement for small devices
  • 9. There is always the need for an URL 9
  • 10. Database of URLs 1,000,000 Phishing URLs from PhishTank 10 http://moviesjingle.com/auto/163.com/index.php 1,000,000 Legitimate URLs from Common Crawl http://paypal.com.update.account.toughbook.cl/8a30e847925afc597516 1aeabe8930f1/?cmd=_home&dispatch=d09b78f5812945a73610edf38 http://msystemtech.ru/components/com_users/Italy/zz/Login.php?run= _login-submit&session=68bbd43c854147324d77872062349924 https://www.sanfordhealth.org/ChildrensHealth/Article/73980 http://www.grahamleader.com/ci_25029538/these-are-5-worst-super- bowl-halftime-shows&defid=1634182 http://www.carolinaguesthouse.co.uk/onlinebooking/?industrytype=1& startdate=2013-09-05&nights=2&location&productid=25d47a24-6b74
  • 11. CLASSIFYING PHISHING USING URL LEXICAL AND STATISTICAL FREQUENCIES 11
  • 12. URL Lexical and Statistical Frequencies 12 http://www.papaya.com/secure_login.php URL length Alexa Ranking Path length URL Entropy # of .com Punctuation count TLD count Is IP? Euclidean distance KS & KL distance
  • 13. URL Lexical and Statistical Frequencies 13 http://www.papaya.com/secure_login.php URL length Alexa Ranking Path length URL Entropy # of .com Punctuation count TLD count Is IP? Euclidean distance KS & KL distance Is It Phishing?
  • 14. URL Lexical and Statistical Frequencies 14 3-Fold CV Accuracy Recall Precision Average 93.47% 93.28% 93.64% Deviation 0.01% 0.02% 0.03% Results:
  • 15. URL Lexical and Statistical Frequencies 15 Feature Importance
  • 16. MODELING PHISHING URLS WITH RECURRENT NEURAL NETWORKS 16
  • 17. Normal Neural Network 17 Source: https://en.wikipedia.org/wiki/Artificial_neural_network
  • 18.
  • 19. Recurrent Neural Networks RNN Have loops! 19
  • 20. The Problem of Long-Term Dependencies 20 Short term dependencies are easy long term …
  • 21. Long-Short Term Memory Networks LSTM 21 RNN contains a single layer LSTM contains four interacting layers Source: http://colah.github.io/posts/2015-08-Understanding-LSTMs/
  • 22. Modeling Architecture for URL Classification 27 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 …
  • 23. Long-Short Term Memory Networks 28 3-Fold CV Accuracy Recall Precision Average 98.76% 98.93% 98.60% Deviation 0.04% 0.02% 0.02% Results:
  • 24. Models Comparison 29 90% 91% 92% 93% 94% 95% 96% 97% 98% 99% 100% Accuracy Recall Precision Long-Short Term Memory Network Random Forest
  • 25. Models Comparison 30 Model Random Forest Long-Short Term Memory Network Memory Consumption (MB) 289 0.56 Evaluation Time (URLs per sec) 942 281 Training Time (minutes) 2.95 238.7
  • 26. What we learned • Discerning URLs by their patterns is a good predictor of phishing websites • LSTM model shows an overall higher prediction performance without the need of expert knowledge to create the features 31
  • 28. Thank you! Any questions or comments, please let me know. Alejandro Correa Bahnsen, PhD Chief Data Scientist acorrea@easysol.net