Davis Social Links Leveraging Social Informatics for Cyber Security: - - PowerPoint PPT Presentation

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Davis Social Links Leveraging Social Informatics for Cyber Security: - - PowerPoint PPT Presentation

Davis Social Links Leveraging Social Informatics for Cyber Security: Architecture, Implementation, and Applications S. Felix Wu Computer Science Department Univers BTH, Karlskrona, Sweden ity of California, Davis wu@cs.ucdavis.edu


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  • S. Felix Wu

Computer Science Department UniversBTH, Karlskrona, Swedenity of California, Davis wu@cs.ucdavis.edu

http://www.facebook.com/sfelixwu http://dsl.cs.ucdavis.edu

Davis Social Links

Leveraging Social Informatics for Cyber Security: Architecture, Implementation, and Applications

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Research Objectives

  • Systems leveraging Social Informatics

– How can Social Informatics be maintained and leveraged to handle cyber security issues? – Current Internet applications and Future Internet Design

  • Social-Centric OS Kernel

– How to protect the fairness/value of the Social Informatics/Resource layer? – Complement to Social-Centric Internet

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Architecture

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Amy Felix Tim Eric

Email as just an example…

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The Implication of FP’s

  • Spam-filters have to be conservative…
  • We will have some false negatives in our
  • wn inboxes.
  • We will spend our own time to further

filter..

– For me, 1~2 seconds per email

A: eliminate 99.99% spams, 10% hams B: eliminate 60% spams, 0.00001% hams

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Oops…

“BTW, a small typo as well!”

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11/16 /2007 11/26 /2007 In my office 11/27 /2007 Spammed? 12/10 /2007 “Memoryless” For Felix Wu

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Why is this message different to me?

  • Relationship between the communicating

entities

  • This message is special and personal

– E.g., not all the professors in the whole world will receive this, BTW.

  • There is a difference between content and

relationship!

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Will John Amy Justin Felix Nancy Mary Sam Tim Eric

X-DSL 0x15EF2AC4 GENI geni eni 0.667

https://dslcore.slice1054.genislices.emulab.net/soemail/src/login.php

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Social Informatics

  • The traditional Cyber Security approach

– Packets, Content, Memory, File, Log – Signatures/Anomalies

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Social Informatics

  • The traditional Cyber Security approach

– Packets, Content, Memory, File, Log – Signatures/Anomalies

  • The Social Informatics approach

– Social relationship/dynamics behind all of them! – Signatures/Anomalies at the Social Plane – Conjecture – the AMOUNT of social informatics consumed is much more predictable than informatics itself.

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How many? within how much time?

  • Social capitals/resources

“anomalous” social transactions

Justin Eric

???

Felix

The response from the dark side….

  • btain the expected social profile
  • leverage a large number of compromised social bots
  • each produce/consume a very small amount of SR

13 EuroView 2011

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Social Informatics Dynamics

  • What is it?
  • Social Relationship, it’s really about quality

and the context!

  • Interactions via/versus Social Relationship
  • Controlling the Social Relationship based on

the Context

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Social Informatics

  • Leveraged in

– Emails (Communication), Recommendation Systems, Network security, Wikipedia, Bit Torrent, Search Engine,…

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“Kernelization” of Social Informatics

  • Managing/Sharing Social Resources

– Motivation example: Farmville

  • Many “Theoretical” Perspectives

– E.g., game theory, network formation/evolution, privacy/anonymity, trust management

  • Our take is the “System” perspective

– Can we build a good CSI (Computational Social Informatics) kernel to support the realization

  • f general social computing/networking

concepts?

16 EuroView 2011

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SMTP

Eric Felix

Justin

We are trying to make the information of social context and relationship explicit!

Dualism

Justin Eric Felix

0.73 0.65

17 EuroView 2011

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Simulation study

  • f 100K+ nodes…
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OSN

DSL/FAITH

Policy/Reputation-based Route discovery Community Oriented Keywords Name-ID resolution Social Context

FAITH over OSN

Application

Eric Felix

Social-Enabled Applications and Games Existing Applications

Wrapper

Social network transformation

tagging DSL  FAITH Emphasizing Trustworthiness in Social Informatics

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Implementation

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Facebook API

  • How social informatics is being accessed

under Facebook?

  • REST and GRAPH

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Our own Social Informatics Kernel

FAITH FAITH (Facebook Application Identifier Translator & Hypervisor) like NAT (Network Address Translation) “Should I have faith in you?”

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I-Application

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Facebook Applications: Identification/Transformation/Hypervisor

FAITH

Facebook/ Faith Applications

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Farmville and Facebook

Justin Eric Felix

FB friends: 790+ FV neighbors: 30+

neighbor friend The “Add me please” push!!!

25 EuroView 2011

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A Couple Issues

  • Careless in adding friends regardless …

– Incentive model at the “Facebook” level to balance such a behavior

  • Certain Applications are pushing users to

accept new friendships unconditionally.

– But, how do we control the quality of the friendships, at least, in the context of a particular application?

26 EuroView 2011

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Social Network Transformation

  • What is the best/effective VPSN for this

application X?

– Virtual Private Social Network

Justin Eric Felix

v-friend/neighbor friend friend

Justin Eric Felix Justin Eric Felix

FB friend FV friend

27 EuroView 2011

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Social Network Transformation

  • Each application might need different OSN

topologies.

Justin Eric Felix

0.73 0.65

28 EuroView 2011

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Gamers Unite!

29 EuroView 2011

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Facebook wall has been “junked”

  • Wall should be an application itself!
  • The users need a much better control!
  • Should FB itself be a R/W device to

SCOSK (such that we can have Twitter and

  • thers as well)?

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Social-aware “Googling”

  • Page Rank along is insufficient

– Popularity versus Diversity

  • Integration of Trust

– Which SN features are more relevant

  • Propagation/Aggregation of Interests/Trust/OSN

– Proactive push and reactive pull

  • Social network itself might depend on “K”

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Ego-centric Social Network

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Live/News Feeds to Prantik on Facebook

updates feeds

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An earlier experiment

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Number of FB Users: 16 Number of User Data crawled: 1374 (i.e. Size of Social Network Crawled) Total Content Links crawled: 12618 Total Unique Keywords Extracted: 76158 (487706) Keywords Per Link: 39.13 Next round: 200 ~1000 users

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Date

  • Num. Links Increment

Sun, 10 Jan 2010 00:00:00 349 349 Tue, 09 Feb 2010 00:00:00 382 33 Thu, 11 Mar 2010 00:00:00 422 40 Sat, 10 Apr 2010 01:00:00 441 19 Mon, 10 May 2010 01:00:00 460 19 Wed, 09 Jun 2010 01:00:00 491 31 Fri, 09 Jul 2010 01:00:00 535 44 Sun, 08 Aug 2010 01:00:00 577 42 Tue, 07 Sep 2010 01:00:00 641 64 Thu, 07 Oct 2010 01:00:00 695 54 Sat, 06 Nov 2010 01:00:00 866 171 Mon, 06 Dec 2010 00:00:00 3396 2530 Wed, 05 Jan 2011 00:00:00 5902 2506 Fri, 04 Feb 2011 00:00:00 7893 1991 Sun, 06 Mar 2011 00:00:00 10320 2427

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“Preliminary” results

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Measure/Method Degree-rank Social-rank Real-time diversity 0.1823 [8] 0.2249 [8] 0.2249 [6] Page-rank 151 143 77

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Degree Rank

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Social Rank/Diversity

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Freshness

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Remarks

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Social Computing Paradigm

Applications (or routers/gateways) consuming SI

Trustworthy Social Informatics

Architecture/Design/Implementation

Impact to Real World Applications and Society? how to evaluate?