1. B. Aditya Prakash Computer Science Virginia Tech.
Understanding, Predicting and Managing Behaviors using Propagation: From Flu-trends to Cyber-Security
B. Aditya Prakash
Computer Science
Virginia Tech.
Fidelis Cybersecurity, Sept 26, 2016

2. Thanks!
Abhishek Sharma
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3. Networks are everywhere!
Facebook Network [2010]
Gene Regulatory Network [Decourty 2008]
Human Disease Network [Barabasi 2007]
The Internet [2005]
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4. Dynamical Processes over networks are also everywhere!
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5. Why do we care? Social collaboration Information Diffusion
Viral Marketing
Epidemiology and Public Health
Cyber Security
Human mobility
Games and Virtual Worlds
Ecology
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6. Why do we care? (1: Epidemiology)
Dynamical Processes over networks
[AJPH 2007]
SI Model
CDC data: Visualization of the first 35 tuberculosis (TB) patients and their 1039 contacts
Diseases over contact networks
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7. Why do we care? (1: Epidemiology)
Dynamical Processes over networks
Each circle is a hospital
~3000 hospitals
More than 30,000 patients transferred
Mention number of hospitals
Patients transferred
[US-MEDICARE NETWORK 2005]
Problem: Given k units of disinfectant, whom to immunize?
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8. Why do we care? (1: Epidemiology)
~6x fewer!
[US-MEDICARE NETWORK 2005]
CURRENT PRACTICE
OUR METHOD
Hospital-acquired inf. took 99K+ lives, cost $5B+ (all per year)
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9. Why do we care? (2: Online Diffusion)
> 800m users, ~$1B revenue [WSJ 2010]
~100m active users
> 50m users
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10. Why do we care? (2: Online Diffusion)
Dynamical Processes over networks
Buy Versace™!
Celebrity
Followers
Social Media Marketing
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11. Why do we care? (3: To change the world?)
Dynamical Processes over networks
Social networks and Collaborative Action
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12. High Impact – Multiple Settings
epidemic out-breaks
Q. How to squash rumors faster?
Q. How do opinions spread?
Q. How to market better?
products/viruses
transmit s/w patches
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13. Large real-world networks & processes
Research Theme
ANALYSIS
Understanding
POLICY/ ACTION
Managing/Utilizing
DATA
Large real-world networks & processes
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14. Research Theme – Public Health
ANALYSIS
Will an epidemic happen?
POLICY/ ACTION
How to control out-breaks?
DATA
Modeling # patient transfers
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15. Research Theme – Social Media
ANALYSIS
# cascades in future?
POLICY/ ACTION
How to market better?
DATA
Modeling Tweets spreading
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16. Large real-world networks & processes
In this talk
DATA
Large real-world networks & processes
Q1: How to predict Flu-
trends better?
Q2: How does information evolve over time?
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17. Large real-world networks & processes
In this talk
DATA
Large real-world networks & processes
Q3: How do malware attacks evolve over time?
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18. Outline Motivation Part 1: Learning Models (Empirical Studies)
Part 2: Policy and Action (Algorithms)
Conclusion
single virus VS multiple viruses
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19. Part 1 Part 1: Learning Models (Empirical Studies)
Q1: How to predict Flu-trends better?
Q2: How does information evolve over time?
Q3: How does malware attacks evolve over time?
single virus VS multiple viruses
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20. Surveillance How to estimate and predict flu trends?
[Chen et. al. ICDM 2014]
Surveillance
How to estimate and predict flu trends?
Population survey
Hospital record
Lab survey
Surveillance
Report
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21. GFT & Twitter Estimate flu trends using online electronic sources
So cold today, I’m catching cold.
I have headache, sore throat, I can’t
go to school today.
My nose is totally congested, I have
a hard time understanding what I’m
saying.
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22. Observation 1: States
There are different states in an infection cycle.
SEIR model:
1. Susceptible Exposed
3. Infected Recovered
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23. Observation 2: Ep. & So. Gap
Infection cases drop exponentially in epidemiology (Hethcote 2000)
Keyword mentions drop in a power-law pattern in social media (Matsubara 2012)
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24. HFSTM Model Details Hidden Flu-State from Tweet Model (HFSTM)
Each word (w) in a tweet (Oi) can be generated by:
A background topic
Non-flu related topics
State related topics
Latent state
Initial prob.
Transit. switch
Binary non-flu related switch
Transit. prob.
Binary background switch
Word distribution
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25. HFSTM Model Generating tweets Generate the state for a tweet
Generate the topic for a word
State: [S,E,I]
Topic: [Background,
Non-flu,
State] S:
good
This
restaurant is
really E:
The
movie
was
but it
freezing I: I
think
have
flu
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26. Inference Details EM-based algorithm: HFSTM-FIT E-step: M-step:
At(i)=P(O1,O2,…,Ot,St=i)
Bt(i)=P(Ot+1,…,OTu|St=i)
γt(i)=P(St=i|Ou)
M-step:
Other parameters such as state transition probabilities, topic distributions, etc.
Parameters learned:
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27. A possible issue with HFSTM
Suffers from large, noisy vocabulary.
Semi-supervision for improvement
Introduce weak supervision into HFSTM.
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28. HFSTM-A HFSTM-A(spect)
Introduce an aspect variable y, expressing our belief on whether a word is flu-related or not.
The value of y biases the switch variables s.t. flu-related words are more likely to be explained by state topics.
When the aspect value (y) is introduced, the switching probability are updated accordingly.
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29. Vocabulary & Dataset Vocabulary (230 words): Dataset (34,000 tweets):
Flu-related keyword list by Chakraborty SDM 2014
Extra state-related keyword list
Dataset (34,000 tweets):
Identify infected users and collect their tweets
Train on data from Jun 20, 2013-Aug 06, 2013
Test on two time period:
Dec 01, July 08, 2013
Nov 10, 2013-Jan 26, 2014
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30. Learned word distributions
The most probable words learned in each state
Probably healthy: S
Having symptons: E
Definitely sick: I
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31. Learned state transition
Transition probabilities
Transition in real tweets
Learned by HFSTM:
Not directly flu-related, yet correctly identified
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32. Flu trend fitting Ground-truth: Algorithms:
The Pan American Health Organization (PAHO)
Algorithms:
Baseline:
Count the number of keywords weekly as features, and regress to the ground-truth curve.
Google flu trend:
Take the google flu trend data as input, regress to the PAHO curve.
HFSTM:
Distinguish different states of keyword, and only use the number of keywords in I state. Again regress to PAHO.
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33. Flu trend fitting
Linear regression to the case count reported by PAHO (the ground-truth)
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34. HFSTM-A
Results are qualitatively similar with HFSTM, when the vocabulary is 10 times larger.
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35. Part 1 Part 1: Learning Models (Empirical Studies)
Q1: How to predict Flu-trends better?
Q2: How does information evolve over time?
Q3: How does malware attacks evolve over time?
single virus VS multiple viruses
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36. Google Search Volume ? ? e.g., given (1) first spike,
(2) release date of two sequel movies
(3) access volume before the release date
(1) First spike
(2) Release date
(3) Two weeks before release ? ?
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37. Patterns Y X
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38. Patterns Y
More Data X
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39. Patterns Y
Anomaly ? X
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40. Patterns Y
Anomaly ?
Extrapolation X
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41. Patterns Y
Anomaly
Imputation
Extrapolation X
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42. Patterns
Anomaly
Imputation
Compression
Extrapolation
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43. Rise and fall patterns in social media
Meme (# of mentions in blogs)
short phrases Sourced from U.S. politics in 2008
“you can put lipstick on a pig”
“yes we can”
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44. Rise and fall patterns in social media
Can we find a unifying model, which includes these patterns?
four classes on YouTube [Crane et al. ’08]
six classes on Meme [Yang et al. ’11]
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45. Rise and fall patterns in social media
Answer: YES!
We can represent all patterns by single model
In Matsubara, Sakurai, Prakash+ SIGKDD 2012
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46. Main idea - SpikeM β 1. Un-informed bloggers (uninformed about rumor)
2. External shock at time nb (e.g, breaking news)
3. Infection (word-of-mouth) β
Time n=0
Time n=nb
Time n=nb+1
Infectiveness of a blog-post at age n:
Strength of infection (quality of news)
Decay function (how infective a blog posting is)
Power Law
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47. -1.5 slope
J. G. Oliveira et. al. Human Dynamics: The Correspondence Patterns of Darwin and Einstein. Nature 437, 1251 (2005) . [PDF]
(also in Leskovec, McGlohon+, SDM 2007)
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48. SpikeM - with periodicity
Details
SpikeM - with periodicity
Full equation of SpikeM
Periodicity
12pm
Peak activity
3am
Low activity
Time n
Bloggers change their activity over time
(e.g., daily, weekly, yearly)
activity
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49. Tail-part forecasts
SpikeM can capture tail part
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50. “What-if” forecasting
e.g., given (1) first spike,
(2) release date of two sequel movies
(3) access volume before the release date
(1) First spike
(2) Release date
(3) Two weeks before release ? ?
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51. “What-if” forecasting
SpikeM can forecast not only tail-part, but also rise-part!
SpikeM can forecast upcoming spikes
(1) First spike
(2) Release date
(3) Two weeks before release
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52. Bonus: Protest Predictions
Violent Protest (VP)
[Sundereisan et al. ASONAM 2014]
[Jin et al. SIGKDD 2014]
Can Twitter provide a lead time?
South American twitter dataset
Language: Spanish/Portuguese
Idea
Look for trending keywords.
Predict event type for protest using SpikeM
parameters! VP
A political tweet
Non Violent Protest (P) P
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53. Part 1 Part 1: Learning Models (Empirical Studies)
Q1: How to predict Flu-trends better?
Q2: How does information evolve over time?
Q3: How do malware attacks evolve over time?
single virus VS multiple viruses
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54. Modeling Malware Penetration
Worldwide Intelligence Network
Which machine got which malware
(or legitimate files)
1 Billion nodes
37 Billion edges
Q: Temporal patterns?
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55. Pointers: Book chapter
Graph Mining for Cyber Security
Prakash.
Cyber Warfare: Building the Scientific Foundation
Springer 2015.
Latest results on using big-data graph mining for cyber security
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56. Book plug The Global Cyber-Vulnerability Report
Subrahmanian, Ovelgonne, Dumitras, Prakash.
Springer 2016
The result of analyzing two years of data from Symantec comprising over 20 billion malware and telemetry reports from over 4 million machines per year over a 2 year period. In addition, the report looks at the cybersecurity policies of all 44 countries and tries to identify important next steps that must be taken to mitigate cyber-threats.
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57. WINE dataset
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58. Cybersecurity
Popularity of files follows a power-law
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59. Q: Temporal Patterns Looks familiar? 
Exponential rise and power law fall
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60. SpikeM again (or SharkFin)
[Papalexakakis et. al. ASONAM 2013]
7 parameters only!
~ 400 points
~ 400 points
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61. Latent Propagation Patterns
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62. BUT
Does not take into account differences between detections vs actual infections.
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63. Domain-based approach: Data
[Chan et. al. WSDM 2016]
Looked at the entire 2 years of WINE data.
Augmented with vulnerability and patch data from NIST’s National Vulnerability Database (NVD)
Considered all machines from 40 countries – study still ongoing. Considered the 50 most commonly occurring malware.
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64. Study Approach: Main Steps
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65. Study Approach: Patch & Detection Incompetence
Incompetence : 4 base variables to measure hosts' incompetence in detecting malware and incompetence in patching (absolute and relative) w.r.t. various time period. “How much time each host took in detecting or patching for each malware”
For each time tick, we built a directed bipartite graph capturing normalized detection/patching incompetence between malware and hosts
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66. FBP Model
Dependent variable: For each (c,m) pair, the % of hosts in the country c attacked by malware m.
Independent variables for each (c,m) pair:
ADI, API, RDI, RPI, AADI, ARDI,AAPI, ARPI, ADA, RDA, APA and RPA of hosts in country c, APH and RPH of malware m
Six similarity measures for hosts in two different countries
Per Capita GDP and HDI of countries
Found k-nearest neighbors of each (c,m) pair according to different similarity measures and used features of those countries as well.
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67. DIPS and DIPS-EXP Model
Infection rate 𝛽 𝑡 .
Patching rates:
Susceptible hosts: 𝜃(𝑡)
Detected hosts: 𝛿(𝑡)
Developed algorithm to learn best parameters for DIPS and DIPS-Exp model by minimizing error terms.
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68. Learning DIPS parameters
Given the #infections and detection at each time t in learning period, find parameters Φ to minimize the sum of squared errors:
Learning algorithm (Two phases)
Train parameters with sum of all infections and detections
Train the subset of parameters for each target pair
ML technique: Levenberg Marquardt (LM) algorithm
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69. Ensemble Models Developed two kinds of models: Ensemble Models
Feature-based prediction (FBP)
Propagation-based prediction (DIPS)
Ensemble Models
Based on FBP
Add output from DIPS models as additional features
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70. Experiments : Overall
We predict infection ratios of hosts in each country for each malware
Test all country-malware pairs for top 50 malware and top 40 GDP countries w.r.t. # of infections
NRMSE is important because infections ratios over countries are very different
FBP shows better performance than FUNNEL w.r.t. all performance measures
DIPS shows better performance than FBP w.r.t. all performance measures
ESM0 is the best w.r.t. NRMSE
FUNNEL*: disease infection prediction model
FBP + FUNNEL does not work
The MAE* values were computed with
|# of ground true infected hosts – the expected # of infected hosts|
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71. Experiments
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72. Summary of Forecasting Experiments
FBP, DIPS and ESM showed better performance when there were lots of infection attempts.
FBP showed reliable performance across the board
DIPS was very accurate when infectiousness level is high
ESM takes both advantages of FBP and DIPS and shows very accurate and reliable performance
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73. Extensions: Human Vulnerability (to malware attacks) Study
Identify behaviors of users that are correlated with the number of attacks on those users
Approach:
Find statistical proxies for human behavior
Correlate them with malware attacks
[Ovelgonne et al. TIST 2016]
Some slides from: V S Subrahmanian
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74. Host behavior and Vulnerability
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75. Results 1
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76. Number of Binaries vs number of infections per host
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77. Results 2:
Software developers are the most vulnerable (8.1 vs. 3.3)), even after discounting for the fact that many binaries may have been produced by them. All results are statistically significant with p < (i.e. with > 99.9% confidence)
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78. Also: Cyber-vulnerability
Characterize CV of customers in different countries?
help governments/companies better ensure safer user behavior
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79. Data Preparation
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80. Question
Which 5 countries have the highest rate of attacks per machine?
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81. Similarly
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82. World Cyber-Vulnerability Map
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83. Europe
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84. Average Attacks per Host
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85. But in India…
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86. Per capita GDP and attacks
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87. Downloaded Binaries and Risk
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88. 50 most-common malware Order?US UK
S. Korea
India
China
S. Korea, India, and China may be doing a better job patching against the most common 50 types of malware. Or maybe in the US, people distribute patching effort across lots of malware components. Our model took patching behavior into account.
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89. More in… The Global Cyber-Vulnerability Report
Subrahmanian, Ovelgonne, Dumitras, Prakash.
Springer 2016
The result of analyzing two years of data from Symantec comprising over 20 billion malware and telemetry reports from over 4 million machines per year over a 2 year period. In addition, the report looks at the cybersecurity policies of all 44 countries and tries to identify important next steps that must be taken to mitigate cyber-threats.
Prakash 2016

90. Outline Motivation Part 1: Learning Models (Empirical Studies)
Part 2: Policy and Action (Algorithms)
Conclusion
single virus VS multiple viruses
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91. Alg 1: Immunization (= Interventions)
Different Flavors:
Pre-emptive
Data-aware
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92. Pre-emptive: Vulnerability
First eigenvalue λ1 (of adjacency matrix) is sufficient for most diffusion models.
[Prakash et al. ICDM’12 selected for best papers]
λ1 is the epidemic threshold
“Safe”
“Vulnerable”
“Deadly”
Increasing λ1 , Increasing vulnerability
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93. Goal Decrease λ1 as much as possible
Node based [Tong, P., + ICDM 2010]
Edge-based [Tong, P., Eliassi-Rad+ CIKM 2012, Best Paper Award]
Edge-Manipulation [P., Adamic+ SDM 2013]
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94. Latest results
First (provable) approximation algorithms for edge-based problem [Saha, Adiga, P., Vullikanti SDM 2015])
O(log^2 n)--factor (can be improved to O(log n))
Based on the idea of removing closed walks
Semi-Definite Programming Rounding-based O(1) factor
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95. Data-aware Immunization
[Zhang and Prakash, SDM 2014
Zhang and Prakash, TKDD 2015]
Given: Graph and Infected nodes
Find: ‘best’ nodes for immunization
Complexity
NP-hard
Hard to approximate within an absolute error
DAVA-tree
Optimal solution on the tree
DAVA and DAVA-fast
Merging infected nodes
Build a “dominator tree”, and run DAVA-tree
Running time: subquadratic
DAVA: O(k(|E|+ |V|log|V|))
DAVA-fast: O(|E|+|V|log|V|)
Graph with infected nodes
Dominator tree
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96. Extensions
Can be extended to Uncertain and noisy initial data as well!
[Zhang and Prakash, CIKM 2014]
Twitter Firehose API
1% sample
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97. Alg 2: “Zoom-out” of the network
“Zoom-out” of the cascade graph to get a quick picture (= summarization) A D D A
Zoom-out C C B B F E F E
Smaller representation of the network
Big graph
Coarsening
[Purohit, Prakash, et, al. SIGKDD 2014]
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98. Application: Diffusion observation
Stats:
1891 groups
mean group size: 16.6
the largest group: nodes (roughly 40% of nodes)
(See more results in the paper)
Observation 1: a very large fraction of movies propagate in a small number of groups
Observation 2: a multi-modal distribution
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99. And many others…. Finding Culprits [Prakash et al. 2012]
Correcting for missing data in cascades [Sunderaisen et al. 2015] …
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100. Outline Motivation Part 1: Learning Models (Empirical Studies)
Part 2: Policy and Action (Algorithms)
Conclusion
single virus VS multiple viruses
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101. Large real-world networks & processes
Future Plans
ANALYSIS
Understanding
POLICY/ ACTION
Managing
DATA
Large real-world networks & processes
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102. Scalability – Big Data Need scalable algorithms for
Datasets of unprecedented scale
High dimensionality and sample size!
Need scalable algorithms for
Learning Models
Developing Policy
Leverage parallel systems
Map-Reduce clusters (like Hadoop) for data-intensive jobs (more than 6000 machines)
Parallelized compute-intensive simulations (like Condor)
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103. Uncertain Data in Cascade analysis (more implementable policies)
Correcting for missing data
Designing More Robust Immunization Policies
Original, Nodes sampled off
Culprits, and missing nodes filled in
Zhang and Prakash. CIKM 2014
Sundereisan, Vreeken, Prakash
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104. Coarsening How is it related to community structure?
More applications, like Visualization…
Parallelization A D D A
Zoom-out C C B B F E F E
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Big graph

105. Summarization and Segmentation
Automatic segmentation?
Segment flu cascades?
…….
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106. References
Scalable Vaccine Distribution in Large Graphs given Uncertain Data (Yao Zhang and B. Aditya Prakash) -- In CIKM 2014.
Fast Influence-based Coarsening for Large Networks (Manish Purohit, B. Aditya Prakash, Chahhyun Kang, Yao Zhang and V. S. Subrahmanian) – In SIGKDD 2014
DAVA: Distributing Vaccines over Large Networks under Prior Information (Yao Zhang and B. Aditya Prakash) -- In SDM 2014
Fractional Immunization on Networks (B. Aditya Prakash, Lada Adamic, Jack Iwashnya, Hanghang Tong, Christos Faloutsos) – In SDM 2013
Spotting Culprits in Epidemics: Who and How many? (B. Aditya Prakash, Jilles Vreeken, Christos Faloutsos) – In ICDM 2012, Brussels Vancouver (Invited to KAIS Journal Best Papers of ICDM.)
Gelling, and Melting, Large Graphs through Edge Manipulation (Hanghang Tong, B. Aditya Prakash, Tina Eliassi-Rad, Michalis Faloutsos, Christos Faloutsos) – In ACM CIKM 2012, Hawaii (Best Paper Award)
Rise and Fall Patterns of Information Diffusion: Model and Implications (Yasuko Matsubara, Yasushi Sakurai, B. Aditya Prakash, Lei Li, Christos Faloutsos) – In SIGKDD 2012, Beijing
Interacting Viruses on a Network: Can both survive? (Alex Beutel, B. Aditya Prakash, Roni Rosenfeld, Christos Faloutsos) – In SIGKDD 2012, Beijing
Winner-takes-all: Competing Viruses or Ideas on fair-play networks (B. Aditya Prakash, Alex Beutel, Roni Rosenfeld, Christos Faloutsos) – In WWW 2012, Lyon
Threshold Conditions for Arbitrary Cascade Models on Arbitrary Networks (B. Aditya Prakash, Deepayan Chakrabarti, Michalis Faloutsos, Nicholas Valler, Christos Faloutsos) - In IEEE ICDM 2011, Vancouver (Invited to KAIS Journal Best Papers of ICDM.)
Times Series Clustering: Complex is Simpler! (Lei Li, B. Aditya Prakash) - In ICML 2011, Bellevue
Epidemic Spreading on Mobile Ad Hoc Networks: Determining the Tipping Point (Nicholas Valler, B. Aditya Prakash, Hanghang Tong, Michalis Faloutsos and Christos Faloutsos) – In IEEE NETWORKING 2011, Valencia, Spain
Formalizing the BGP stability problem: patterns and a chaotic model (B. Aditya Prakash, Michalis Faloutsos and Christos Faloutsos) – In IEEE INFOCOM NetSciCom Workshop, 2011.
On the Vulnerability of Large Graphs (Hanghang Tong, B. Aditya Prakash, Tina Eliassi-Rad and Christos Faloutsos) – In IEEE ICDM 2010, Sydney, Australia
Virus Propagation on Time-Varying Networks: Theory and Immunization Algorithms (B. Aditya Prakash, Hanghang Tong, Nicholas Valler, Michalis Faloutsos and Christos Faloutsos) – In ECML-PKDD 2010, Barcelona, Spain
MetricForensics: A Multi-Level Approach for Mining Volatile Graphs (Keith Henderson, Tina Eliassi-Rad, Christos Faloutsos, Leman Akoglu, Lei Li, Koji Maruhashi, B. Aditya Prakash and Hanghang Tong) - In SIGKDD 2010, Washington D.C.
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107. Acknowledgements Collaborators Christos Faloutsos
Roni Rosenfeld,
Michalis Faloutsos,
Lada Adamic,
Theodore Iwashyna (M.D.),
Dave Andersen,
Tina Eliassi-Rad,
Iulian Neamtiu,
Varun Gupta,
Jilles Vreeken,
V. S. Subrahmanian
John Brownstein (M.D.)
Deepayan Chakrabarti, Hanghang Tong, Kunal Punera, Ashwin Sridharan, Sridhar Machiraju, Mukund Seshadri, Alice Zheng, Lei Li, Polo Chau, Nicholas Valler, Alex Beutel, Xuetao Wei
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108. Acknowledgements Students Liangzhe Chen Shashidhar Sundereisan
Benjamin Wang
Yao Zhang
Sorour Amiri
Bijaya Adhikari
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109. Acknowledgements
Funding
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110. Making Diffusion Work for You
B. Aditya Prakash
Analysis
Policy/Action
Data
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