SlideShare une entreprise Scribd logo

Sybil Attacks - MobiSys Seminar

•
•
Neal Lathia
Neal Lathia

This document proposes monitoring recommender systems over time to detect sybil attacks. It suggests distrusting newcomers to force sybils to draw out their attacks. It recommends monitoring at the system, user, and item levels by learning normal temporal behavior and flagging anomalies. The key contributions are forcing sybils to reveal attacks through prolonged activity and monitoring a wide range of attacks over time.

FormationActualités & Politique
1  sur  48
detecting temporal sybil attacks n. lathia, s. hailes & l. capra mobisys seminar, sept. 29 2009
the web is based on cooperation...
the web is crowd-sourced... ratings: recommender, retrieval systems captchas: digitising text wikis: knowledge repositories
crowd-sourcing is cooperation... my ratings compute your recommendations. your reviews inform my decisions. your links help search engines to respond to my queries.
cooperation is policed by reputation and trust ebay: online trade and markets #followfriday on twitter ? trust ratings, ratings, ratings...
...we cooperate without knowing each other people are (nearly) anonymous why could this be a problem?
for example, recommender systems: recommendations → people → rate items → classification algorithms → recommendations → people...
problem with anonymity: recommendations → people → rate items → classification algorithms → recommendations → people... can you trust them? are they real people? are they rating honestly?
sybil attacks: ...when an attacker tries to subvert the system by creating a large number of sybils—pseudonymous identities—in order to gain a disproportionate amount of influence...
sybil attacks: why? how? random: inject noise, ruin the party for everyone targetted: promote/demote items. make money? APIs: rate content automatically.
recommender system sybil attack: shilling, profile injection, ... “honest” ratings attacker's ratings
each sybil rates: target, selected, filler items target: item that attacker wants promoted/demoted selected: similar items, to deceive the algorithm filler: other items, to deceive humans
how to defend a recommender system?
a) treat it as a classification problem where are the sybils? “honest” ratings attacker's ratings
problems with classification approach: when is your system under attack? when to run classifier?
problems with classification approach: when are sybils damaging your recommendations? wait until they have all rated?
proposal: b) monitor recommender system over time
contributions: 1. force sybils to draw out their attack 2. learn normal temporal behaviour 3. monitor for wide range of attacks 4. force sybils to attack more intelligently
1. force sybils to draw out their attack rather than appear, rate, disappear how? distrust newcomers
distrust newcomers prediction shift → time →
distrust newcomers prediction shift → time →
distrust newcomers prediction shift → time →
1. force sybils to draw out their attack how? distrust newcomers sybils are forced to appear more than once
examining temporal attack behaviour single sybil – – – – group of sybils target, target, filler, filler, selected selected, but also:
group size and dynamics how many sybils? how many ratings per sybil?
how can they behave? (many, few) (many, many) how many sybils? (few, few) (few, many) how many ratings per sybil?
how can does this affect the data? impact = how much malicious data how many sybils? how many ratings per sybil?
how to measure attacks? precision, recall, impact tp tp #sybil ratings pr = re = imp = tp + fp tp + fn #ratings
how to detect these attacks? monitor! item-level system-level how many sybils? user-level how many ratings per sybil?
how to detect these attacks? monitor! system-level how many sybils? how many ratings per sybil?
overview of the methodology 1. monitor: look at how data changes over time 2. flag: look at how data changes under attack
1. system level
1. system level - attack
(system) avg ratings per user 1. monitor: exp. weighted moving avg. mut = (β mut-|w|) + ((1-β) Rt/Ut) 2. flag: incoming ratings above moving threshold Rt/Ut > mut + (αg σt) (parameters α, β updated automatically)
1. system level - evaluation simulated data: play with data variance, attack amplitude
1. system level - evaluation simulated data: play with data variance, attack amplitude
1. system level - evaluation real data: netflix ratings (+ timestamps)
1. system level - evaluation real data: netflix ratings (+ timestamps)
item-level system-level how many sybils? user-level how many ratings per sybil?
(user-level) similar monitor/flag solution 1. monitor: a. how many high-volume raters? b. how much do high-volume raters rate? 2. flag: group size-ratings above threshold file:///C:/Documents%20and%20Settings/User/Desktop/misc/documents/19%20attacks/wsdm_2010/img/highVolume.jpg file:///C:/Documents%20and%20Settings/User/Desktop/misc/documents/19%20attacks/wsdm_2010/img/highRatings.jpg
(user-level) evaluation: real data how many sybils? how many ratings per sybil?
(user-level) evaluation: real data how many sybils? how many ratings per sybil?
(user-level) evaluation: real data item-level system-level how many sybils? user-level how many ratings per sybil?
(item-level) slightly different context 1. the item is rated by many users define many? using how other items were rated 2. the item is rated with extreme ratings define extreme? what is avg item mean? 3. (from a + b) the item mean ratings shifts nuke or promote? flag: if all three conditions broken. Why? 1 → popular item. 2 → few extreme ratings. 3 → cold start item 1 + 2 but not 3 → attack doesn't change anything
3. evaluate: simulated attacks on real data
what next? attackers can defeat these defenses the ramp-up attack
but...
conclusions: 1. force sybils to draw out their attack 2. learn normal temporal behaviour 3. monitor system, users, items 4. force sybils to attack more intelligently

Recommandé

Temporal Defenses for Robust Recommendations
Temporal Defenses for Robust RecommendationsTemporal Defenses for Robust Recommendations
Temporal Defenses for Robust RecommendationsNeal Lathia
 
A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor ...
A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor ...A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor ...
A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor ...Editor IJCATR
 
Network Information And Security
Network Information And SecurityNetwork Information And Security
Network Information And Securityanandk10
 
A REVIEW ON SYBIL ATTACK DETECTION TECHNIQUES
A REVIEW ON SYBIL ATTACK DETECTION TECHNIQUESA REVIEW ON SYBIL ATTACK DETECTION TECHNIQUES
A REVIEW ON SYBIL ATTACK DETECTION TECHNIQUESJournal For Research
 
project report on IoT
project report on IoTproject report on IoT
project report on IoTAnshul Sahu
 
Security in IoT
Security in IoTSecurity in IoT
Security in IoTgr9293
 
Wormhole attack
Wormhole attackWormhole attack
Wormhole attackHarsh Kishore Mishra
 
REAL-TIME INTRUSION DETECTION SYSTEM FOR BIG DATA
REAL-TIME INTRUSION DETECTION SYSTEM FOR BIG DATAREAL-TIME INTRUSION DETECTION SYSTEM FOR BIG DATA
REAL-TIME INTRUSION DETECTION SYSTEM FOR BIG DATAijp2p
 

Recommandé

Temporal Defenses for Robust Recommendations
Temporal Defenses for Robust RecommendationsTemporal Defenses for Robust Recommendations
Temporal Defenses for Robust RecommendationsNeal Lathia
 
A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor ...
A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor ...A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor ...
A Lightweight Algorithm for Detecting Sybil Attack in Mobile Wireless Sensor ...Editor IJCATR
 
Network Information And Security
Network Information And SecurityNetwork Information And Security
Network Information And Securityanandk10
 
A REVIEW ON SYBIL ATTACK DETECTION TECHNIQUES
A REVIEW ON SYBIL ATTACK DETECTION TECHNIQUESA REVIEW ON SYBIL ATTACK DETECTION TECHNIQUES
A REVIEW ON SYBIL ATTACK DETECTION TECHNIQUESJournal For Research
 
project report on IoT
project report on IoTproject report on IoT
project report on IoTAnshul Sahu
 
Security in IoT
Security in IoTSecurity in IoT
Security in IoTgr9293
 
Wormhole attack
Wormhole attackWormhole attack
Wormhole attackHarsh Kishore Mishra
 
REAL-TIME INTRUSION DETECTION SYSTEM FOR BIG DATA
REAL-TIME INTRUSION DETECTION SYSTEM FOR BIG DATAREAL-TIME INTRUSION DETECTION SYSTEM FOR BIG DATA
REAL-TIME INTRUSION DETECTION SYSTEM FOR BIG DATAijp2p
 
Profile injection attack detection in recommender system
Profile injection attack detection in recommender systemProfile injection attack detection in recommender system
Profile injection attack detection in recommender systemASHISH PANNU
 
The security mindset securing social media integrations and social learning...
The security mindset securing social media integrations and social learning...The security mindset securing social media integrations and social learning...
The security mindset securing social media integrations and social learning...franco_bb
 
DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...
DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...
DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...DevSecCon
 
Reading Group Presentation: The Power of Procrastination
Reading Group Presentation: The Power of ProcrastinationReading Group Presentation: The Power of Procrastination
Reading Group Presentation: The Power of ProcrastinationMichael Rushanan
 
Subverting Machine Learning Detections for fun and profit
Subverting Machine Learning Detections for fun and profitSubverting Machine Learning Detections for fun and profit
Subverting Machine Learning Detections for fun and profitRam Shankar Siva Kumar
 
Building a Modern Security Engineering Organization. Zane Lackey
Building a Modern Security Engineering Organization. Zane Lackey Building a Modern Security Engineering Organization. Zane Lackey
Building a Modern Security Engineering Organization. Zane LackeyYandex
 
Cyber_Attack_Forecasting_Jones_2015
Cyber_Attack_Forecasting_Jones_2015Cyber_Attack_Forecasting_Jones_2015
Cyber_Attack_Forecasting_Jones_2015Malachi Jones
 
Building a Modern Security Engineering Organization
Building a Modern Security Engineering OrganizationBuilding a Modern Security Engineering Organization
Building a Modern Security Engineering OrganizationZane Lackey
 
The Diamond Model for Intrusion Analysis - Threat Intelligence
The Diamond Model for Intrusion Analysis - Threat IntelligenceThe Diamond Model for Intrusion Analysis - Threat Intelligence
The Diamond Model for Intrusion Analysis - Threat IntelligenceThreatConnect
 
Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...
Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...
Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...Alexandre Sieira
 
DefCamp - Mohamed Bedewi - Building a Weaponized Honeypot
DefCamp - Mohamed Bedewi - Building a Weaponized HoneypotDefCamp - Mohamed Bedewi - Building a Weaponized Honeypot
DefCamp - Mohamed Bedewi - Building a Weaponized HoneypotShah Sheikh
 
Enabling effective hunt teaming and incident response
Enabling effective hunt teaming and incident responseEnabling effective hunt teaming and incident response
Enabling effective hunt teaming and incident responsejeffmcjunkin
 
R af d
R af dR af d
R af dWilliam L. McGill
 
Risk Analysis for Dummies
Risk Analysis for DummiesRisk Analysis for Dummies
Risk Analysis for DummiesWilliam L. McGill
 
Major
MajorMajor
MajorKARAN GOYAL
 
20101017 program analysis_for_security_livshits_lecture03_security
20101017 program analysis_for_security_livshits_lecture03_security20101017 program analysis_for_security_livshits_lecture03_security
20101017 program analysis_for_security_livshits_lecture03_securityComputer Science Club
 
usenix_2014.pptx
usenix_2014.pptxusenix_2014.pptx
usenix_2014.pptxXyLyu
 
Sexy defense
Sexy defenseSexy defense
Sexy defenseIftach Ian Amit
 
Now you see me, now you don't: chasing evasive malware - Giovanni Vigna
Now you see me, now you don't: chasing evasive malware - Giovanni Vigna Now you see me, now you don't: chasing evasive malware - Giovanni Vigna
Now you see me, now you don't: chasing evasive malware - Giovanni Vigna Lastline, Inc.
 
Profile Injection Attack Detection in Recommender System
Profile Injection Attack Detection in Recommender SystemProfile Injection Attack Detection in Recommender System
Profile Injection Attack Detection in Recommender SystemASHISH PANNU
 
Everything around the NLP (London.AI Feb 2021)
Everything around the NLP (London.AI Feb 2021)Everything around the NLP (London.AI Feb 2021)
Everything around the NLP (London.AI Feb 2021)Neal Lathia
 
Using machine learning for customer service (Data Talks Club)
Using machine learning for customer service (Data Talks Club)Using machine learning for customer service (Data Talks Club)
Using machine learning for customer service (Data Talks Club)Neal Lathia
 

Contenu connexe

Similaire à Sybil Attacks - MobiSys Seminar

Profile injection attack detection in recommender system
Profile injection attack detection in recommender systemProfile injection attack detection in recommender system
Profile injection attack detection in recommender systemASHISH PANNU
 
The security mindset securing social media integrations and social learning...
The security mindset securing social media integrations and social learning...The security mindset securing social media integrations and social learning...
The security mindset securing social media integrations and social learning...franco_bb
 
DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...
DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...
DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...DevSecCon
 
Reading Group Presentation: The Power of Procrastination
Reading Group Presentation: The Power of ProcrastinationReading Group Presentation: The Power of Procrastination
Reading Group Presentation: The Power of ProcrastinationMichael Rushanan
 
Subverting Machine Learning Detections for fun and profit
Subverting Machine Learning Detections for fun and profitSubverting Machine Learning Detections for fun and profit
Subverting Machine Learning Detections for fun and profitRam Shankar Siva Kumar
 
Building a Modern Security Engineering Organization. Zane Lackey
Building a Modern Security Engineering Organization. Zane Lackey Building a Modern Security Engineering Organization. Zane Lackey
Building a Modern Security Engineering Organization. Zane LackeyYandex
 
Cyber_Attack_Forecasting_Jones_2015
Cyber_Attack_Forecasting_Jones_2015Cyber_Attack_Forecasting_Jones_2015
Cyber_Attack_Forecasting_Jones_2015Malachi Jones
 
Building a Modern Security Engineering Organization
Building a Modern Security Engineering OrganizationBuilding a Modern Security Engineering Organization
Building a Modern Security Engineering OrganizationZane Lackey
 
The Diamond Model for Intrusion Analysis - Threat Intelligence
The Diamond Model for Intrusion Analysis - Threat IntelligenceThe Diamond Model for Intrusion Analysis - Threat Intelligence
The Diamond Model for Intrusion Analysis - Threat IntelligenceThreatConnect
 
Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...
Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...
Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...Alexandre Sieira
 
DefCamp - Mohamed Bedewi - Building a Weaponized Honeypot
DefCamp - Mohamed Bedewi - Building a Weaponized HoneypotDefCamp - Mohamed Bedewi - Building a Weaponized Honeypot
DefCamp - Mohamed Bedewi - Building a Weaponized HoneypotShah Sheikh
 
Enabling effective hunt teaming and incident response
Enabling effective hunt teaming and incident responseEnabling effective hunt teaming and incident response
Enabling effective hunt teaming and incident responsejeffmcjunkin
 
20101017 program analysis_for_security_livshits_lecture03_security
20101017 program analysis_for_security_livshits_lecture03_security20101017 program analysis_for_security_livshits_lecture03_security
20101017 program analysis_for_security_livshits_lecture03_securityComputer Science Club
 
usenix_2014.pptx
usenix_2014.pptxusenix_2014.pptx
usenix_2014.pptxXyLyu
 
Now you see me, now you don't: chasing evasive malware - Giovanni Vigna
Now you see me, now you don't: chasing evasive malware - Giovanni Vigna Now you see me, now you don't: chasing evasive malware - Giovanni Vigna
Now you see me, now you don't: chasing evasive malware - Giovanni Vigna Lastline, Inc.
 
Profile Injection Attack Detection in Recommender System
Profile Injection Attack Detection in Recommender SystemProfile Injection Attack Detection in Recommender System
Profile Injection Attack Detection in Recommender SystemASHISH PANNU
 

Similaire à Sybil Attacks - MobiSys Seminar (20)

Profile injection attack detection in recommender system
Profile injection attack detection in recommender systemProfile injection attack detection in recommender system
Profile injection attack detection in recommender system
 
The security mindset securing social media integrations and social learning...
The security mindset securing social media integrations and social learning...The security mindset securing social media integrations and social learning...
The security mindset securing social media integrations and social learning...
 
DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...
DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...
DevSecCon Singapore 2018 - Measuring and maximizing vuln discovery efforts by...
 
Reading Group Presentation: The Power of Procrastination
Reading Group Presentation: The Power of ProcrastinationReading Group Presentation: The Power of Procrastination
Reading Group Presentation: The Power of Procrastination
 
Subverting Machine Learning Detections for fun and profit
Subverting Machine Learning Detections for fun and profitSubverting Machine Learning Detections for fun and profit
Subverting Machine Learning Detections for fun and profit
 
Building a Modern Security Engineering Organization. Zane Lackey
Building a Modern Security Engineering Organization. Zane Lackey Building a Modern Security Engineering Organization. Zane Lackey
Building a Modern Security Engineering Organization. Zane Lackey
 
Cyber_Attack_Forecasting_Jones_2015
Cyber_Attack_Forecasting_Jones_2015Cyber_Attack_Forecasting_Jones_2015
Cyber_Attack_Forecasting_Jones_2015
 
Building a Modern Security Engineering Organization
Building a Modern Security Engineering OrganizationBuilding a Modern Security Engineering Organization
Building a Modern Security Engineering Organization
 
The Diamond Model for Intrusion Analysis - Threat Intelligence
The Diamond Model for Intrusion Analysis - Threat IntelligenceThe Diamond Model for Intrusion Analysis - Threat Intelligence
The Diamond Model for Intrusion Analysis - Threat Intelligence
 
Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...
Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...
Threat Intelligence Baseada em Dados: Métricas de Disseminação e Compartilham...
 
DefCamp - Mohamed Bedewi - Building a Weaponized Honeypot
DefCamp - Mohamed Bedewi - Building a Weaponized HoneypotDefCamp - Mohamed Bedewi - Building a Weaponized Honeypot
DefCamp - Mohamed Bedewi - Building a Weaponized Honeypot
 
Enabling effective hunt teaming and incident response
Enabling effective hunt teaming and incident responseEnabling effective hunt teaming and incident response
Enabling effective hunt teaming and incident response
 
R af d
R af dR af d
R af d
 
Risk Analysis for Dummies
Risk Analysis for DummiesRisk Analysis for Dummies
Risk Analysis for Dummies
 
Major
MajorMajor
Major
 
20101017 program analysis_for_security_livshits_lecture03_security
20101017 program analysis_for_security_livshits_lecture03_security20101017 program analysis_for_security_livshits_lecture03_security
20101017 program analysis_for_security_livshits_lecture03_security
 
usenix_2014.pptx
usenix_2014.pptxusenix_2014.pptx
usenix_2014.pptx
 
Sexy defense
Sexy defenseSexy defense
Sexy defense
 
Now you see me, now you don't: chasing evasive malware - Giovanni Vigna
Now you see me, now you don't: chasing evasive malware - Giovanni Vigna Now you see me, now you don't: chasing evasive malware - Giovanni Vigna
Now you see me, now you don't: chasing evasive malware - Giovanni Vigna
 
Profile Injection Attack Detection in Recommender System
Profile Injection Attack Detection in Recommender SystemProfile Injection Attack Detection in Recommender System
Profile Injection Attack Detection in Recommender System
 

Plus de Neal Lathia

Everything around the NLP (London.AI Feb 2021)
Everything around the NLP (London.AI Feb 2021)Everything around the NLP (London.AI Feb 2021)
Everything around the NLP (London.AI Feb 2021)Neal Lathia
 
Using machine learning for customer service (Data Talks Club)
Using machine learning for customer service (Data Talks Club)Using machine learning for customer service (Data Talks Club)
Using machine learning for customer service (Data Talks Club)Neal Lathia
 
Using language models to supercharge Monzo’s customer support
Using language models to supercharge Monzo’s customer support Using language models to supercharge Monzo’s customer support
Using language models to supercharge Monzo’s customer supportNeal Lathia
 
Making Better Decisions Faster
Making Better Decisions FasterMaking Better Decisions Faster
Making Better Decisions FasterNeal Lathia
 
Machine Learning, Faster
Machine Learning, FasterMachine Learning, Faster
Machine Learning, FasterNeal Lathia
 
AI & Personalised Experiences
AI & Personalised ExperiencesAI & Personalised Experiences
AI & Personalised ExperiencesNeal Lathia
 
Opportunities & Challenges in Personalised Travel
Opportunities & Challenges in Personalised TravelOpportunities & Challenges in Personalised Travel
Opportunities & Challenges in Personalised TravelNeal Lathia
 
Bootstrapping a Destination Recommendation Engine
Bootstrapping a Destination Recommendation EngineBootstrapping a Destination Recommendation Engine
Bootstrapping a Destination Recommendation EngineNeal Lathia
 
Machine Learning for Product Managers
Machine Learning for Product ManagersMachine Learning for Product Managers
Machine Learning for Product ManagersNeal Lathia
 
Mining Smartphone Data (with Python)
Mining Smartphone Data (with Python)Mining Smartphone Data (with Python)
Mining Smartphone Data (with Python)Neal Lathia
 
Happier and Healthier with Smartphone Data
Happier and Healthier with Smartphone DataHappier and Healthier with Smartphone Data
Happier and Healthier with Smartphone DataNeal Lathia
 
Data Science in Digital Health
Data Science in Digital HealthData Science in Digital Health
Data Science in Digital HealthNeal Lathia
 
Using Smartphones to Measure (and Intervene in) Daily Life
Using Smartphones to Measure (and Intervene in) Daily LifeUsing Smartphones to Measure (and Intervene in) Daily Life
Using Smartphones to Measure (and Intervene in) Daily LifeNeal Lathia
 
Analysing Daily Behaviours with Large-Scale Smartphone Data
Analysing Daily Behaviours with Large-Scale Smartphone DataAnalysing Daily Behaviours with Large-Scale Smartphone Data
Analysing Daily Behaviours with Large-Scale Smartphone DataNeal Lathia
 
Cambridge Quantified Self Meetup
Cambridge Quantified Self MeetupCambridge Quantified Self Meetup
Cambridge Quantified Self MeetupNeal Lathia
 
Data Science in #mHealth
Data Science in #mHealthData Science in #mHealth
Data Science in #mHealthNeal Lathia
 
Tube Star: Crowd-Sourced Experiences on Public Transport
Tube Star: Crowd-Sourced Experiences on Public Transport Tube Star: Crowd-Sourced Experiences on Public Transport
Tube Star: Crowd-Sourced Experiences on Public Transport Neal Lathia
 
Emotion Sense: From Design to Deployment
Emotion Sense: From Design to DeploymentEmotion Sense: From Design to Deployment
Emotion Sense: From Design to DeploymentNeal Lathia
 
Opportunities and Challenges of Using Smartphones for Health Monitoring and I...
Opportunities and Challenges of Using Smartphones for Health Monitoring and I...Opportunities and Challenges of Using Smartphones for Health Monitoring and I...
Opportunities and Challenges of Using Smartphones for Health Monitoring and I...Neal Lathia
 
Using Smartphones to Research Daily Life
Using Smartphones to Research Daily LifeUsing Smartphones to Research Daily Life
Using Smartphones to Research Daily LifeNeal Lathia
 

Plus de Neal Lathia (20)

Everything around the NLP (London.AI Feb 2021)
Everything around the NLP (London.AI Feb 2021)Everything around the NLP (London.AI Feb 2021)
Everything around the NLP (London.AI Feb 2021)
 
Using machine learning for customer service (Data Talks Club)
Using machine learning for customer service (Data Talks Club)Using machine learning for customer service (Data Talks Club)
Using machine learning for customer service (Data Talks Club)
 
Using language models to supercharge Monzo’s customer support
Using language models to supercharge Monzo’s customer support Using language models to supercharge Monzo’s customer support
Using language models to supercharge Monzo’s customer support
 
Making Better Decisions Faster
Making Better Decisions FasterMaking Better Decisions Faster
Making Better Decisions Faster
 
Machine Learning, Faster
Machine Learning, FasterMachine Learning, Faster
Machine Learning, Faster
 
AI & Personalised Experiences
AI & Personalised ExperiencesAI & Personalised Experiences
AI & Personalised Experiences
 
Opportunities & Challenges in Personalised Travel
Opportunities & Challenges in Personalised TravelOpportunities & Challenges in Personalised Travel
Opportunities & Challenges in Personalised Travel
 
Bootstrapping a Destination Recommendation Engine
Bootstrapping a Destination Recommendation EngineBootstrapping a Destination Recommendation Engine
Bootstrapping a Destination Recommendation Engine
 
Machine Learning for Product Managers
Machine Learning for Product ManagersMachine Learning for Product Managers
Machine Learning for Product Managers
 
Mining Smartphone Data (with Python)
Mining Smartphone Data (with Python)Mining Smartphone Data (with Python)
Mining Smartphone Data (with Python)
 
Happier and Healthier with Smartphone Data
Happier and Healthier with Smartphone DataHappier and Healthier with Smartphone Data
Happier and Healthier with Smartphone Data
 
Data Science in Digital Health
Data Science in Digital HealthData Science in Digital Health
Data Science in Digital Health
 
Using Smartphones to Measure (and Intervene in) Daily Life
Using Smartphones to Measure (and Intervene in) Daily LifeUsing Smartphones to Measure (and Intervene in) Daily Life
Using Smartphones to Measure (and Intervene in) Daily Life
 
Analysing Daily Behaviours with Large-Scale Smartphone Data
Analysing Daily Behaviours with Large-Scale Smartphone DataAnalysing Daily Behaviours with Large-Scale Smartphone Data
Analysing Daily Behaviours with Large-Scale Smartphone Data
 
Cambridge Quantified Self Meetup
Cambridge Quantified Self MeetupCambridge Quantified Self Meetup
Cambridge Quantified Self Meetup
 
Data Science in #mHealth
Data Science in #mHealthData Science in #mHealth
Data Science in #mHealth
 
Tube Star: Crowd-Sourced Experiences on Public Transport
Tube Star: Crowd-Sourced Experiences on Public Transport Tube Star: Crowd-Sourced Experiences on Public Transport
Tube Star: Crowd-Sourced Experiences on Public Transport
 
Emotion Sense: From Design to Deployment
Emotion Sense: From Design to DeploymentEmotion Sense: From Design to Deployment
Emotion Sense: From Design to Deployment
 
Opportunities and Challenges of Using Smartphones for Health Monitoring and I...
Opportunities and Challenges of Using Smartphones for Health Monitoring and I...Opportunities and Challenges of Using Smartphones for Health Monitoring and I...
Opportunities and Challenges of Using Smartphones for Health Monitoring and I...
 
Using Smartphones to Research Daily Life
Using Smartphones to Research Daily LifeUsing Smartphones to Research Daily Life
Using Smartphones to Research Daily Life
 

Dernier

1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdfQucHHunhnh
 
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdfUGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdfNirmal Dwivedi
 
Making communications land - Are they received and understood as intended? we...
Making communications land - Are they received and understood as intended? we...Making communications land - Are they received and understood as intended? we...
Making communications land - Are they received and understood as intended? we...Association for Project Management
 
Unit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptxUnit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptxVishalSingh1417
 
Towards a code of practice for AI in AT.pptx
Towards a code of practice for AI in AT.pptxTowards a code of practice for AI in AT.pptx
Towards a code of practice for AI in AT.pptxJisc
 
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin ClassesMixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin ClassesCeline George
 
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxUnit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxVishalSingh1417
 
Dyslexia AI Workshop for Slideshare.pptx
Dyslexia AI Workshop for Slideshare.pptxDyslexia AI Workshop for Slideshare.pptx
Dyslexia AI Workshop for Slideshare.pptxcallscotland1987
 
Food safety_Challenges food safety laboratories_.pdf
Food safety_Challenges food safety laboratories_.pdfFood safety_Challenges food safety laboratories_.pdf
Food safety_Challenges food safety laboratories_.pdfSherif Taha
 
Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.pptRamjanShidvankar
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxheathfieldcps1
 
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptxMaritesTamaniVerdade
 
ICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxAreebaZafar22
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...christianmathematics
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxVishalSingh1417
 
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfHoldier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfagholdier
 
Graduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - EnglishGraduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - Englishneillewis46
 
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxHMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxEsquimalt MFRC
 
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...pradhanghanshyam7136
 

Dernier (20)

Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024Mehran University Newsletter Vol-X, Issue-I, 2024
Mehran University Newsletter Vol-X, Issue-I, 2024
 
1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf1029-Danh muc Sach Giao Khoa khoi 6.pdf
1029-Danh muc Sach Giao Khoa khoi 6.pdf
 
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdfUGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
UGC NET Paper 1 Mathematical Reasoning & Aptitude.pdf
 
Making communications land - Are they received and understood as intended? we...
Making communications land - Are they received and understood as intended? we...Making communications land - Are they received and understood as intended? we...
Making communications land - Are they received and understood as intended? we...
 
Unit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptxUnit-IV; Professional Sales Representative (PSR).pptx
Unit-IV; Professional Sales Representative (PSR).pptx
 
Towards a code of practice for AI in AT.pptx
Towards a code of practice for AI in AT.pptxTowards a code of practice for AI in AT.pptx
Towards a code of practice for AI in AT.pptx
 
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin ClassesMixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
Mixin Classes in Odoo 17 How to Extend Models Using Mixin Classes
 
Unit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptxUnit-V; Pricing (Pharma Marketing Management).pptx
Unit-V; Pricing (Pharma Marketing Management).pptx
 
Dyslexia AI Workshop for Slideshare.pptx
Dyslexia AI Workshop for Slideshare.pptxDyslexia AI Workshop for Slideshare.pptx
Dyslexia AI Workshop for Slideshare.pptx
 
Food safety_Challenges food safety laboratories_.pdf
Food safety_Challenges food safety laboratories_.pdfFood safety_Challenges food safety laboratories_.pdf
Food safety_Challenges food safety laboratories_.pdf
 
Application orientated numerical on hev.ppt
Application orientated numerical on hev.pptApplication orientated numerical on hev.ppt
Application orientated numerical on hev.ppt
 
The basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptxThe basics of sentences session 3pptx.pptx
The basics of sentences session 3pptx.pptx
 
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
2024-NATIONAL-LEARNING-CAMP-AND-OTHER.pptx
 
ICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptxICT Role in 21st Century Education & its Challenges.pptx
ICT Role in 21st Century Education & its Challenges.pptx
 
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
Explore beautiful and ugly buildings. Mathematics helps us create beautiful d...
 
Unit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptxUnit-IV- Pharma. Marketing Channels.pptx
Unit-IV- Pharma. Marketing Channels.pptx
 
Holdier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdfHoldier Curriculum Vitae (April 2024).pdf
Holdier Curriculum Vitae (April 2024).pdf
 
Graduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - EnglishGraduate Outcomes Presentation Slides - English
Graduate Outcomes Presentation Slides - English
 
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptxHMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
HMCS Max Bernays Pre-Deployment Brief (May 2024).pptx
 
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
Kodo Millet PPT made by Ghanshyam bairwa college of Agriculture kumher bhara...
 

Sybil Attacks - MobiSys Seminar

  • 1. detecting temporal sybil attacks n. lathia, s. hailes & l. capra mobisys seminar, sept. 29 2009
  • 2. the web is based on cooperation...
  • 3. the web is crowd-sourced... ratings: recommender, retrieval systems captchas: digitising text wikis: knowledge repositories
  • 4. crowd-sourcing is cooperation... my ratings compute your recommendations. your reviews inform my decisions. your links help search engines to respond to my queries.
  • 5. cooperation is policed by reputation and trust ebay: online trade and markets #followfriday on twitter ? trust ratings, ratings, ratings...
  • 6. ...we cooperate without knowing each other people are (nearly) anonymous why could this be a problem?
  • 7. for example, recommender systems: recommendations → people → rate items → classification algorithms → recommendations → people...
  • 8. problem with anonymity: recommendations → people → rate items → classification algorithms → recommendations → people... can you trust them? are they real people? are they rating honestly?
  • 9. sybil attacks: ...when an attacker tries to subvert the system by creating a large number of sybils—pseudonymous identities—in order to gain a disproportionate amount of influence...
  • 10. sybil attacks: why? how? random: inject noise, ruin the party for everyone targetted: promote/demote items. make money? APIs: rate content automatically.
  • 11. recommender system sybil attack: shilling, profile injection, ... “honest” ratings attacker's ratings
  • 12. each sybil rates: target, selected, filler items target: item that attacker wants promoted/demoted selected: similar items, to deceive the algorithm filler: other items, to deceive humans
  • 13. how to defend a recommender system?
  • 14. a) treat it as a classification problem where are the sybils? “honest” ratings attacker's ratings
  • 15. problems with classification approach: when is your system under attack? when to run classifier?
  • 16. problems with classification approach: when are sybils damaging your recommendations? wait until they have all rated?
  • 18. contributions: 1. force sybils to draw out their attack 2. learn normal temporal behaviour 3. monitor for wide range of attacks 4. force sybils to attack more intelligently
  • 19. 1. force sybils to draw out their attack rather than appear, rate, disappear how? distrust newcomers
  • 23. 1. force sybils to draw out their attack how? distrust newcomers sybils are forced to appear more than once
  • 24. examining temporal attack behaviour single sybil – – – – group of sybils target, target, filler, filler, selected selected, but also:
  • 25. group size and dynamics how many sybils? how many ratings per sybil?
  • 26. how can they behave? (many, few) (many, many) how many sybils? (few, few) (few, many) how many ratings per sybil?
  • 27. how can does this affect the data? impact = how much malicious data how many sybils? how many ratings per sybil?
  • 28. how to measure attacks? precision, recall, impact tp tp #sybil ratings pr = re = imp = tp + fp tp + fn #ratings
  • 29. how to detect these attacks? monitor! item-level system-level how many sybils? user-level how many ratings per sybil?
  • 30. how to detect these attacks? monitor! system-level how many sybils? how many ratings per sybil?
  • 31. overview of the methodology 1. monitor: look at how data changes over time 2. flag: look at how data changes under attack
  • 33. 1. system level - attack
  • 34. (system) avg ratings per user 1. monitor: exp. weighted moving avg. mut = (β mut-|w|) + ((1-β) Rt/Ut) 2. flag: incoming ratings above moving threshold Rt/Ut > mut + (αg σt) (parameters α, β updated automatically)
  • 35. 1. system level - evaluation simulated data: play with data variance, attack amplitude
  • 36. 1. system level - evaluation simulated data: play with data variance, attack amplitude
  • 37. 1. system level - evaluation real data: netflix ratings (+ timestamps)
  • 38. 1. system level - evaluation real data: netflix ratings (+ timestamps)
  • 39. item-level system-level how many sybils? user-level how many ratings per sybil?
  • 40. (user-level) similar monitor/flag solution 1. monitor: a. how many high-volume raters? b. how much do high-volume raters rate? 2. flag: group size-ratings above threshold file:///C:/Documents%20and%20Settings/User/Desktop/misc/documents/19%20attacks/wsdm_2010/img/highVolume.jpg file:///C:/Documents%20and%20Settings/User/Desktop/misc/documents/19%20attacks/wsdm_2010/img/highRatings.jpg
  • 41. (user-level) evaluation: real data how many sybils? how many ratings per sybil?
  • 42. (user-level) evaluation: real data how many sybils? how many ratings per sybil?
  • 43. (user-level) evaluation: real data item-level system-level how many sybils? user-level how many ratings per sybil?
  • 44. (item-level) slightly different context 1. the item is rated by many users define many? using how other items were rated 2. the item is rated with extreme ratings define extreme? what is avg item mean? 3. (from a + b) the item mean ratings shifts nuke or promote? flag: if all three conditions broken. Why? 1 → popular item. 2 → few extreme ratings. 3 → cold start item 1 + 2 but not 3 → attack doesn't change anything
  • 45. 3. evaluate: simulated attacks on real data
  • 46. what next? attackers can defeat these defenses the ramp-up attack
  • 48. conclusions: 1. force sybils to draw out their attack 2. learn normal temporal behaviour 3. monitor system, users, items 4. force sybils to attack more intelligently