SlideShare a Scribd company logo
Cyber Attacks
Analysis
Shwetha Narayanan
Insight Data Engineering Fellow
New York – Summer 2017
Real Time Analysis of Cyber Attack Hotspots
Motivation
Streaming Data Source
• Anti-virus software companies
• Augmented data to scale
• 4000 - 6000 events per minute
• Scaled up to 100,000 events per minute
Streaming Data Source
• Content
–Attack Type
•Malware
•DDOS
•Backdoor
–Location
Information of
victim and attacker
Metric for Hotspot
Analysis
Getis - Ord
Getis - Ord
• Used when you have geospatial data
• Calculates statistical significant clusters based
on a feature
• Estimate a Gi Score for every space in the
region
– Higher Gi score => Significant Hotspot
• Compares the feature score of current cell and
it’s neighbors with sum of all feature values
Getis Ord – Gi Score
• Steps to Calculate
– Divide the space into
cells
– Accumulate attack
counts in each cell
– Calculate Gi Score
• Blue vs Green
– Blue is surrounded
by cells of higher
attack count
5
3 2
4
1
5
10
14
9
9
10
1
2
Interactive Query
• Find events within a
radius of 10 miles
– Calculate Bounding
box
Bounding box
• min(x), max(x), min(y), max(y)
• Based on earth’s spherical
radius at that point
Data Pipeline
Cyber Attacks
Streaming Data
Source
Demo
Kafka Streams Technical Challenges
• Streams application should provide Serializers
and Deserializers to materialize the data
– Read input from stream / Write to stream
• Built in serializers are: String, Integer, Long,
Double
How to work with other data
formats?
Deserializer for other data formats
Creating a Serde - SerializerDeserializer
Kafka Streams Technical Challenges
• Kafka Streams Errors
– Internal Topic Error - Cannot create internal
topics
• User permissions to create topics - Stack Overflow
• Set Group ID and Application ID
• Used for co-ordinating between instances
About Me - Shwetha Narayanan
• Recently graduated with
Masters in Computer Science
• Worked for 2 years as a
Software Engineer
• Co-authored a paper on
“Enabling Real time crime
intelligence using mobile GIS and
prediction methods”, EISIC, 2013
Screenshots - Hotspots
Screenshots - Cyber Attack Trends
Getis Score - Calculation
Bounding Box calculation
acos(sin(input_lat) * sin(Lat) + cos(input_lat) *
cos(Lat) * cos(Lon - (input_lon))) * 6371 <=
1000;

More Related Content

What's hot

Khan farhan cv
Khan farhan cvKhan farhan cv
Khan farhan cv
farhan0039
 
Deep Learning in Security—An Empirical Example in User and Entity Behavior An...
Deep Learning in Security—An Empirical Example in User and Entity Behavior An...Deep Learning in Security—An Empirical Example in User and Entity Behavior An...
Deep Learning in Security—An Empirical Example in User and Entity Behavior An...
Databricks
 
Lakesh_resume_02-07
Lakesh_resume_02-07Lakesh_resume_02-07
Lakesh_resume_02-07
LakeshBiyala
 
Event streaming pipeline with Windows Azure and ArcGIS Geoevent extension
Event streaming pipeline with Windows Azure and ArcGIS Geoevent extensionEvent streaming pipeline with Windows Azure and ArcGIS Geoevent extension
Event streaming pipeline with Windows Azure and ArcGIS Geoevent extension
Roberto Messora
 
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Clou...
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Clou...Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Clou...
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Clou...
JAYAPRAKASH JPINFOTECH
 
Deep Learning for Public Safety in Chicago and San Francisco
Deep Learning for Public Safety in Chicago and San FranciscoDeep Learning for Public Safety in Chicago and San Francisco
Deep Learning for Public Safety in Chicago and San Francisco
Sri Ambati
 
October 2014 Webinar: Cybersecurity Threat Detection
October 2014 Webinar: Cybersecurity Threat DetectionOctober 2014 Webinar: Cybersecurity Threat Detection
October 2014 Webinar: Cybersecurity Threat Detection
Sqrrl
 
Complex Event Processing with Esper
Complex Event Processing with EsperComplex Event Processing with Esper
Complex Event Processing with Esper
Matthew McCullough
 
Deploy Deep Learning Models with TensorFlow + Lambda
Deploy Deep Learning Models with TensorFlow + LambdaDeploy Deep Learning Models with TensorFlow + Lambda
Deploy Deep Learning Models with TensorFlow + Lambda
Greg Werner
 
VeriSign iDefense Security Intelligence Services
VeriSign iDefense Security Intelligence ServicesVeriSign iDefense Security Intelligence Services
VeriSign iDefense Security Intelligence Services
TechBiz Forense Digital
 
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...
ijngnjournal
 
Random4 and hirshberg algorithm
Random4 and hirshberg algorithmRandom4 and hirshberg algorithm
Random4 and hirshberg algorithm
nishant kumar
 
Large-Scale Malicious Domain Detection with Spark AI
Large-Scale Malicious Domain Detection with Spark AILarge-Scale Malicious Domain Detection with Spark AI
Large-Scale Malicious Domain Detection with Spark AI
Databricks
 
Bioinformatics Data Pipelines built by CSIRO on AWS
Bioinformatics Data Pipelines built by CSIRO on AWSBioinformatics Data Pipelines built by CSIRO on AWS
Bioinformatics Data Pipelines built by CSIRO on AWS
Lynn Langit
 
XGSN: An Open-source Semantic Sensing Middleware for the Web of Things
XGSN: An Open-source Semantic Sensing Middleware for the Web of ThingsXGSN: An Open-source Semantic Sensing Middleware for the Web of Things
XGSN: An Open-source Semantic Sensing Middleware for the Web of Things
Jean-Paul Calbimonte
 
Distributed Near Real-Time Processing of Sensor Network Data Flows for Smart ...
Distributed Near Real-Time Processing of Sensor Network Data Flows for Smart ...Distributed Near Real-Time Processing of Sensor Network Data Flows for Smart ...
Distributed Near Real-Time Processing of Sensor Network Data Flows for Smart ...
Otávio Carvalho
 
Event Processing Using Semantic Web Technologies
Event Processing Using Semantic Web TechnologiesEvent Processing Using Semantic Web Technologies
Event Processing Using Semantic Web Technologies
Mikko Rinne
 
iThome Cloud Summit: The next generation of data center: Machine Intelligent ...
iThome Cloud Summit: The next generation of data center: Machine Intelligent ...iThome Cloud Summit: The next generation of data center: Machine Intelligent ...
iThome Cloud Summit: The next generation of data center: Machine Intelligent ...
Evan Lin
 
The Heatmap
 - Why is Security Visualization so Hard?
The Heatmap
 - Why is Security Visualization so Hard?The Heatmap
 - Why is Security Visualization so Hard?
The Heatmap
 - Why is Security Visualization so Hard?
Raffael Marty
 

What's hot (19)

Khan farhan cv
Khan farhan cvKhan farhan cv
Khan farhan cv
 
Deep Learning in Security—An Empirical Example in User and Entity Behavior An...
Deep Learning in Security—An Empirical Example in User and Entity Behavior An...Deep Learning in Security—An Empirical Example in User and Entity Behavior An...
Deep Learning in Security—An Empirical Example in User and Entity Behavior An...
 
Lakesh_resume_02-07
Lakesh_resume_02-07Lakesh_resume_02-07
Lakesh_resume_02-07
 
Event streaming pipeline with Windows Azure and ArcGIS Geoevent extension
Event streaming pipeline with Windows Azure and ArcGIS Geoevent extensionEvent streaming pipeline with Windows Azure and ArcGIS Geoevent extension
Event streaming pipeline with Windows Azure and ArcGIS Geoevent extension
 
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Clou...
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Clou...Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Clou...
Dynamic Multi-Keyword Ranked Search Based on Bloom Filter Over Encrypted Clou...
 
Deep Learning for Public Safety in Chicago and San Francisco
Deep Learning for Public Safety in Chicago and San FranciscoDeep Learning for Public Safety in Chicago and San Francisco
Deep Learning for Public Safety in Chicago and San Francisco
 
October 2014 Webinar: Cybersecurity Threat Detection
October 2014 Webinar: Cybersecurity Threat DetectionOctober 2014 Webinar: Cybersecurity Threat Detection
October 2014 Webinar: Cybersecurity Threat Detection
 
Complex Event Processing with Esper
Complex Event Processing with EsperComplex Event Processing with Esper
Complex Event Processing with Esper
 
Deploy Deep Learning Models with TensorFlow + Lambda
Deploy Deep Learning Models with TensorFlow + LambdaDeploy Deep Learning Models with TensorFlow + Lambda
Deploy Deep Learning Models with TensorFlow + Lambda
 
VeriSign iDefense Security Intelligence Services
VeriSign iDefense Security Intelligence ServicesVeriSign iDefense Security Intelligence Services
VeriSign iDefense Security Intelligence Services
 
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...
Trend-Based Networking Driven by Big Data Telemetry for Sdn and Traditional N...
 
Random4 and hirshberg algorithm
Random4 and hirshberg algorithmRandom4 and hirshberg algorithm
Random4 and hirshberg algorithm
 
Large-Scale Malicious Domain Detection with Spark AI
Large-Scale Malicious Domain Detection with Spark AILarge-Scale Malicious Domain Detection with Spark AI
Large-Scale Malicious Domain Detection with Spark AI
 
Bioinformatics Data Pipelines built by CSIRO on AWS
Bioinformatics Data Pipelines built by CSIRO on AWSBioinformatics Data Pipelines built by CSIRO on AWS
Bioinformatics Data Pipelines built by CSIRO on AWS
 
XGSN: An Open-source Semantic Sensing Middleware for the Web of Things
XGSN: An Open-source Semantic Sensing Middleware for the Web of ThingsXGSN: An Open-source Semantic Sensing Middleware for the Web of Things
XGSN: An Open-source Semantic Sensing Middleware for the Web of Things
 
Distributed Near Real-Time Processing of Sensor Network Data Flows for Smart ...
Distributed Near Real-Time Processing of Sensor Network Data Flows for Smart ...Distributed Near Real-Time Processing of Sensor Network Data Flows for Smart ...
Distributed Near Real-Time Processing of Sensor Network Data Flows for Smart ...
 
Event Processing Using Semantic Web Technologies
Event Processing Using Semantic Web TechnologiesEvent Processing Using Semantic Web Technologies
Event Processing Using Semantic Web Technologies
 
iThome Cloud Summit: The next generation of data center: Machine Intelligent ...
iThome Cloud Summit: The next generation of data center: Machine Intelligent ...iThome Cloud Summit: The next generation of data center: Machine Intelligent ...
iThome Cloud Summit: The next generation of data center: Machine Intelligent ...
 
The Heatmap
 - Why is Security Visualization so Hard?
The Heatmap
 - Why is Security Visualization so Hard?The Heatmap
 - Why is Security Visualization so Hard?
The Heatmap
 - Why is Security Visualization so Hard?
 

Similar to Cyber Attacks Spatial Analysis

Creating Your Own Threat Intel Through Hunting & Visualization
Creating Your Own Threat Intel Through Hunting & VisualizationCreating Your Own Threat Intel Through Hunting & Visualization
Creating Your Own Threat Intel Through Hunting & Visualization
Raffael Marty
 
Discover advanced threats with threat intelligence - Jeremy Li
Discover advanced threats with threat intelligence - Jeremy LiDiscover advanced threats with threat intelligence - Jeremy Li
Discover advanced threats with threat intelligence - Jeremy Li
Jeremy Li
 
System Support for Internet of Things
System Support for Internet of ThingsSystem Support for Internet of Things
System Support for Internet of Things
HarshitParkar6677
 
Solving Cybersecurity at Scale
Solving Cybersecurity at ScaleSolving Cybersecurity at Scale
Solving Cybersecurity at Scale
DataWorks Summit
 
DataStax and Esri: Geotemporal IoT Search and Analytics
DataStax and Esri: Geotemporal IoT Search and AnalyticsDataStax and Esri: Geotemporal IoT Search and Analytics
DataStax and Esri: Geotemporal IoT Search and Analytics
DataStax Academy
 
Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud ...
Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud ...Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud ...
Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud ...
Priyanka Aash
 
Intrusion detection using data mining
Intrusion detection using data miningIntrusion detection using data mining
Intrusion detection using data mining
balbeerrawat
 
Finding the needle in the haystack: how Nestle is leveraging big data to defe...
Finding the needle in the haystack: how Nestle is leveraging big data to defe...Finding the needle in the haystack: how Nestle is leveraging big data to defe...
Finding the needle in the haystack: how Nestle is leveraging big data to defe...
Big Data Spain
 
SPAR 2015 - Civil Maps Presentation by Sravan Puttagunta
SPAR 2015 - Civil Maps Presentation by Sravan PuttaguntaSPAR 2015 - Civil Maps Presentation by Sravan Puttagunta
SPAR 2015 - Civil Maps Presentation by Sravan Puttagunta
Sravan Puttagunta
 
Using Data Science for Cybersecurity
Using Data Science for CybersecurityUsing Data Science for Cybersecurity
Using Data Science for Cybersecurity
VMware Tanzu
 
Approximation Data Structures for Streaming Applications
Approximation Data Structures for Streaming ApplicationsApproximation Data Structures for Streaming Applications
Approximation Data Structures for Streaming Applications
Debasish Ghosh
 
Applying Provenance in APT Monitoring and Analysis Practical Challenges for S...
Applying Provenance in APT Monitoring and Analysis Practical Challenges for S...Applying Provenance in APT Monitoring and Analysis Practical Challenges for S...
Applying Provenance in APT Monitoring and Analysis Practical Challenges for S...
Graeme Jenkinson
 
Secure and Privacy-Preserving Big-Data Processing
Secure and Privacy-Preserving Big-Data ProcessingSecure and Privacy-Preserving Big-Data Processing
Secure and Privacy-Preserving Big-Data Processing
Shantanu Sharma
 
Bertenthal
BertenthalBertenthal
Bertenthal
Jesse Lingeman
 
Mining Software Repositories for Security: Data Quality Issues Lessons from T...
Mining Software Repositories for Security: Data Quality Issues Lessons from T...Mining Software Repositories for Security: Data Quality Issues Lessons from T...
Mining Software Repositories for Security: Data Quality Issues Lessons from T...
CREST
 
High Availability HPC ~ Microservice Architectures for Supercomputing
High Availability HPC ~ Microservice Architectures for SupercomputingHigh Availability HPC ~ Microservice Architectures for Supercomputing
High Availability HPC ~ Microservice Architectures for Supercomputing
inside-BigData.com
 
DHPA Techday 2015 - Maciej Korczyński - Reputation Metrics Design to Improve ...
DHPA Techday 2015 - Maciej Korczyński - Reputation Metrics Design to Improve ...DHPA Techday 2015 - Maciej Korczyński - Reputation Metrics Design to Improve ...
DHPA Techday 2015 - Maciej Korczyński - Reputation Metrics Design to Improve ...
Splend
 
Getting Started with Real-time Analytics
Getting Started with Real-time AnalyticsGetting Started with Real-time Analytics
Getting Started with Real-time Analytics
Amazon Web Services
 
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiBuilding the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
DataWorks Summit
 
D3SF17- Improving Our China Clients Performance
D3SF17- Improving Our China Clients PerformanceD3SF17- Improving Our China Clients Performance
D3SF17- Improving Our China Clients Performance
Imperva Incapsula
 

Similar to Cyber Attacks Spatial Analysis (20)

Creating Your Own Threat Intel Through Hunting & Visualization
Creating Your Own Threat Intel Through Hunting & VisualizationCreating Your Own Threat Intel Through Hunting & Visualization
Creating Your Own Threat Intel Through Hunting & Visualization
 
Discover advanced threats with threat intelligence - Jeremy Li
Discover advanced threats with threat intelligence - Jeremy LiDiscover advanced threats with threat intelligence - Jeremy Li
Discover advanced threats with threat intelligence - Jeremy Li
 
System Support for Internet of Things
System Support for Internet of ThingsSystem Support for Internet of Things
System Support for Internet of Things
 
Solving Cybersecurity at Scale
Solving Cybersecurity at ScaleSolving Cybersecurity at Scale
Solving Cybersecurity at Scale
 
DataStax and Esri: Geotemporal IoT Search and Analytics
DataStax and Esri: Geotemporal IoT Search and AnalyticsDataStax and Esri: Geotemporal IoT Search and Analytics
DataStax and Esri: Geotemporal IoT Search and Analytics
 
Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud ...
Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud ...Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud ...
Transfer Learning: Repurposing ML Algorithms from Different Domains to Cloud ...
 
Intrusion detection using data mining
Intrusion detection using data miningIntrusion detection using data mining
Intrusion detection using data mining
 
Finding the needle in the haystack: how Nestle is leveraging big data to defe...
Finding the needle in the haystack: how Nestle is leveraging big data to defe...Finding the needle in the haystack: how Nestle is leveraging big data to defe...
Finding the needle in the haystack: how Nestle is leveraging big data to defe...
 
SPAR 2015 - Civil Maps Presentation by Sravan Puttagunta
SPAR 2015 - Civil Maps Presentation by Sravan PuttaguntaSPAR 2015 - Civil Maps Presentation by Sravan Puttagunta
SPAR 2015 - Civil Maps Presentation by Sravan Puttagunta
 
Using Data Science for Cybersecurity
Using Data Science for CybersecurityUsing Data Science for Cybersecurity
Using Data Science for Cybersecurity
 
Approximation Data Structures for Streaming Applications
Approximation Data Structures for Streaming ApplicationsApproximation Data Structures for Streaming Applications
Approximation Data Structures for Streaming Applications
 
Applying Provenance in APT Monitoring and Analysis Practical Challenges for S...
Applying Provenance in APT Monitoring and Analysis Practical Challenges for S...Applying Provenance in APT Monitoring and Analysis Practical Challenges for S...
Applying Provenance in APT Monitoring and Analysis Practical Challenges for S...
 
Secure and Privacy-Preserving Big-Data Processing
Secure and Privacy-Preserving Big-Data ProcessingSecure and Privacy-Preserving Big-Data Processing
Secure and Privacy-Preserving Big-Data Processing
 
Bertenthal
BertenthalBertenthal
Bertenthal
 
Mining Software Repositories for Security: Data Quality Issues Lessons from T...
Mining Software Repositories for Security: Data Quality Issues Lessons from T...Mining Software Repositories for Security: Data Quality Issues Lessons from T...
Mining Software Repositories for Security: Data Quality Issues Lessons from T...
 
High Availability HPC ~ Microservice Architectures for Supercomputing
High Availability HPC ~ Microservice Architectures for SupercomputingHigh Availability HPC ~ Microservice Architectures for Supercomputing
High Availability HPC ~ Microservice Architectures for Supercomputing
 
DHPA Techday 2015 - Maciej Korczyński - Reputation Metrics Design to Improve ...
DHPA Techday 2015 - Maciej Korczyński - Reputation Metrics Design to Improve ...DHPA Techday 2015 - Maciej Korczyński - Reputation Metrics Design to Improve ...
DHPA Techday 2015 - Maciej Korczyński - Reputation Metrics Design to Improve ...
 
Getting Started with Real-time Analytics
Getting Started with Real-time AnalyticsGetting Started with Real-time Analytics
Getting Started with Real-time Analytics
 
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFiBuilding the High Speed Cybersecurity Data Pipeline Using Apache NiFi
Building the High Speed Cybersecurity Data Pipeline Using Apache NiFi
 
D3SF17- Improving Our China Clients Performance
D3SF17- Improving Our China Clients PerformanceD3SF17- Improving Our China Clients Performance
D3SF17- Improving Our China Clients Performance
 

Recently uploaded

一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(harvard毕业证书)哈佛大学毕业证如何办理一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
taqyea
 
Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......
Sachin Paul
 
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
 
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
nuttdpt
 
一比一原版爱尔兰都柏林大学毕业证(本硕)ucd学位证书如何办理
一比一原版爱尔兰都柏林大学毕业证(本硕)ucd学位证书如何办理一比一原版爱尔兰都柏林大学毕业证(本硕)ucd学位证书如何办理
一比一原版爱尔兰都柏林大学毕业证(本硕)ucd学位证书如何办理
hqfek
 
Template xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptxTemplate xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptx
TeukuEriSyahputra
 
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Aggregage
 
一比一原版美国帕森斯设计学院毕业证(parsons毕业证书)如何办理
一比一原版美国帕森斯设计学院毕业证(parsons毕业证书)如何办理一比一原版美国帕森斯设计学院毕业证(parsons毕业证书)如何办理
一比一原版美国帕森斯设计学院毕业证(parsons毕业证书)如何办理
asyed10
 
Sample Devops SRE Product Companies .pdf
Sample Devops SRE  Product Companies .pdfSample Devops SRE  Product Companies .pdf
Sample Devops SRE Product Companies .pdf
Vineet
 
Jio cinema Retention & Engagement Strategy.pdf
Jio cinema Retention & Engagement Strategy.pdfJio cinema Retention & Engagement Strategy.pdf
Jio cinema Retention & Engagement Strategy.pdf
inaya7568
 
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
eoxhsaa
 
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI LicensingA presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
AlessioFois2
 
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
slg6lamcq
 
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
 
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
bopyb
 
一比一原版(曼大毕业证书)曼尼托巴大学毕业证如何办理
一比一原版(曼大毕业证书)曼尼托巴大学毕业证如何办理一比一原版(曼大毕业证书)曼尼托巴大学毕业证如何办理
一比一原版(曼大毕业证书)曼尼托巴大学毕业证如何办理
ytypuem
 
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
ElizabethGarrettChri
 
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
 
writing report business partner b1+ .pdf
writing report business partner b1+ .pdfwriting report business partner b1+ .pdf
writing report business partner b1+ .pdf
VyNguyen709676
 
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
lzdvtmy8
 

Recently uploaded (20)

一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(harvard毕业证书)哈佛大学毕业证如何办理一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
一比一原版(harvard毕业证书)哈佛大学毕业证如何办理
 
Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......Palo Alto Cortex XDR presentation .......
Palo Alto Cortex XDR presentation .......
 
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
 
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
一比一原版(UCSB文凭证书)圣芭芭拉分校毕业证如何办理
 
一比一原版爱尔兰都柏林大学毕业证(本硕)ucd学位证书如何办理
一比一原版爱尔兰都柏林大学毕业证(本硕)ucd学位证书如何办理一比一原版爱尔兰都柏林大学毕业证(本硕)ucd学位证书如何办理
一比一原版爱尔兰都柏林大学毕业证(本硕)ucd学位证书如何办理
 
Template xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptxTemplate xxxxxxxx ssssssssssss Sertifikat.pptx
Template xxxxxxxx ssssssssssss Sertifikat.pptx
 
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
Beyond the Basics of A/B Tests: Highly Innovative Experimentation Tactics You...
 
一比一原版美国帕森斯设计学院毕业证(parsons毕业证书)如何办理
一比一原版美国帕森斯设计学院毕业证(parsons毕业证书)如何办理一比一原版美国帕森斯设计学院毕业证(parsons毕业证书)如何办理
一比一原版美国帕森斯设计学院毕业证(parsons毕业证书)如何办理
 
Sample Devops SRE Product Companies .pdf
Sample Devops SRE  Product Companies .pdfSample Devops SRE  Product Companies .pdf
Sample Devops SRE Product Companies .pdf
 
Jio cinema Retention & Engagement Strategy.pdf
Jio cinema Retention & Engagement Strategy.pdfJio cinema Retention & Engagement Strategy.pdf
Jio cinema Retention & Engagement Strategy.pdf
 
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
一比一原版多伦多大学毕业证(UofT毕业证书)学历如何办理
 
A presentation that explain the Power BI Licensing
A presentation that explain the Power BI LicensingA presentation that explain the Power BI Licensing
A presentation that explain the Power BI Licensing
 
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
一比一原版南十字星大学毕业证(SCU毕业证书)学历如何办理
 
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
 
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
一比一原版(GWU,GW文凭证书)乔治·华盛顿大学毕业证如何办理
 
一比一原版(曼大毕业证书)曼尼托巴大学毕业证如何办理
一比一原版(曼大毕业证书)曼尼托巴大学毕业证如何办理一比一原版(曼大毕业证书)曼尼托巴大学毕业证如何办理
一比一原版(曼大毕业证书)曼尼托巴大学毕业证如何办理
 
Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024Open Source Contributions to Postgres: The Basics POSETTE 2024
Open Source Contributions to Postgres: The Basics POSETTE 2024
 
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
 
writing report business partner b1+ .pdf
writing report business partner b1+ .pdfwriting report business partner b1+ .pdf
writing report business partner b1+ .pdf
 
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
一比一原版格里菲斯大学毕业证(Griffith毕业证书)学历如何办理
 

Cyber Attacks Spatial Analysis

  • 1. Cyber Attacks Analysis Shwetha Narayanan Insight Data Engineering Fellow New York – Summer 2017 Real Time Analysis of Cyber Attack Hotspots
  • 3. Streaming Data Source • Anti-virus software companies • Augmented data to scale • 4000 - 6000 events per minute • Scaled up to 100,000 events per minute
  • 4. Streaming Data Source • Content –Attack Type •Malware •DDOS •Backdoor –Location Information of victim and attacker
  • 6. Getis - Ord • Used when you have geospatial data • Calculates statistical significant clusters based on a feature • Estimate a Gi Score for every space in the region – Higher Gi score => Significant Hotspot • Compares the feature score of current cell and it’s neighbors with sum of all feature values
  • 7. Getis Ord – Gi Score • Steps to Calculate – Divide the space into cells – Accumulate attack counts in each cell – Calculate Gi Score • Blue vs Green – Blue is surrounded by cells of higher attack count 5 3 2 4 1 5 10 14 9 9 10 1 2
  • 8. Interactive Query • Find events within a radius of 10 miles – Calculate Bounding box Bounding box • min(x), max(x), min(y), max(y) • Based on earth’s spherical radius at that point
  • 10. Demo
  • 11. Kafka Streams Technical Challenges • Streams application should provide Serializers and Deserializers to materialize the data – Read input from stream / Write to stream • Built in serializers are: String, Integer, Long, Double
  • 12. How to work with other data formats?
  • 13. Deserializer for other data formats
  • 14. Creating a Serde - SerializerDeserializer
  • 15. Kafka Streams Technical Challenges • Kafka Streams Errors – Internal Topic Error - Cannot create internal topics • User permissions to create topics - Stack Overflow • Set Group ID and Application ID • Used for co-ordinating between instances
  • 16. About Me - Shwetha Narayanan • Recently graduated with Masters in Computer Science • Worked for 2 years as a Software Engineer • Co-authored a paper on “Enabling Real time crime intelligence using mobile GIS and prediction methods”, EISIC, 2013
  • 18. Screenshots - Cyber Attack Trends
  • 19. Getis Score - Calculation
  • 20. Bounding Box calculation acos(sin(input_lat) * sin(Lat) + cos(input_lat) * cos(Lat) * cos(Lon - (input_lon))) * 6371 <= 1000;