Managing the Weakest Link: A Game-Theoretic Approach for the - - PowerPoint PPT Presentation

▶
managing the weakest link a game theoretic approach for
SMART_READER_LITE
LIVE PREVIEW

Managing the Weakest Link: A Game-Theoretic Approach for the - - PowerPoint PPT Presentation

Dec 06, 2023 16 likes •460 views

Managing the Weakest Link: A Game-Theoretic Approach for the Mitigation of Insider Threats Aron Laszka 1 , 2 Benjamin Johnson 3 ottle 4 Pascal Sch Jens Grossklags 1 ohme 4 Rainer B 1 Pennsylvania State University 2 Budapest University of

slide-1
SLIDE 1

Managing the Weakest Link: A Game-Theoretic Approach for the Mitigation of Insider Threats

Aron Laszka 1,2 Benjamin Johnson 3 Pascal Sch¨

  • ttle 4

Jens Grossklags 1 Rainer B¨

  • hme 4

1Pennsylvania State University 2Budapest University of Technology and Economics 3University of California, Berkeley 4University of M¨

unster

Laszka et al. (PennState) Managing the Weakest Link 1 / 26

slide-2
SLIDE 2

Motivation - Cyber-espionage

Will the new device be a phone or not?

Laszka et al. (PennState) Managing the Weakest Link 2 / 26

slide-3
SLIDE 3

Motivation - Cyber-espionage

Will interest rates change or not?

Laszka et al. (PennState) Managing the Weakest Link 3 / 26

slide-4
SLIDE 4

Motivation - Cyber-espionage

Respond to a cyber-attack with conventional warfare?

Laszka et al. (PennState) Managing the Weakest Link 4 / 26

slide-5
SLIDE 5

Motivation - Cyber-espionage

What is published

◮ FBI “estimates that every year billions of U.S.

dollars are lost to foreign and domestic competitors who deliberately target economic intelligence in flourishing U.S. industries and technologies” [4]

◮ a 2012 report identifies the loss for the

German industry caused by industrial espionage to be around 4.2 billion e [2]

◮ US and one particular foreign nation in the

last four years: “nearly 100 individual or corporate defendants have been charged by the Justice Department with stealing trade secrets or classified information” [5]

Laszka et al. (PennState) Managing the Weakest Link 5 / 26

slide-6
SLIDE 6

Weakest Link: Insider Threats

FBI: “A domestic or foreign business competitor ... may wish to place a spy into a company in order to gain access to non-public

  • information. Alternatively, they may try to recruit an existing

employee to do the same thing.” [3] 2012 report on Germany: over 70% of losses were caused by members

  • f their own organization [2]

traditionally: access control

Laszka et al. (PennState) Managing the Weakest Link 6 / 26

slide-7
SLIDE 7

Weakest Link: Insider Threats

FBI: “A domestic or foreign business competitor ... may wish to place a spy into a company in order to gain access to non-public

  • information. Alternatively, they may try to recruit an existing

employee to do the same thing.” [3] 2012 report on Germany: over 70% of losses were caused by members

  • f their own organization [2]

traditionally: access control, but secrets have to be shared with some employees

◮ CERT investigation of 23 attacks: “in 78% of the incidents, the

insiders were authorized users with active computer accounts” [7]

How can we mitigate these risks?

Laszka et al. (PennState) Managing the Weakest Link 6 / 26

slide-8
SLIDE 8

Managing Insider Threats Managing insider threats

Laszka et al. (PennState) Managing the Weakest Link 7 / 26

slide-9
SLIDE 9

Managing Insider Threats

... Team composition Assessing the trustworthiness

  • f employees (e.g., [6])

Estimating the value of intellectual property (e.g., [1])

Laszka et al. (PennState) Managing the Weakest Link 7 / 26

slide-10
SLIDE 10

Managing Insider Threats

... Team composition Assessing the trustworthiness

  • f employees (e.g., [6])

Estimating the value of intellectual property (e.g., [1]) we focus on this

Laszka et al. (PennState) Managing the Weakest Link 7 / 26

slide-11
SLIDE 11

Model - Introduction

secret of value S

Laszka et al. (PennState) Managing the Weakest Link 8 / 26

slide-12
SLIDE 12

Model - Introduction

secret of value S N employees

Laszka et al. (PennState) Managing the Weakest Link 8 / 26

slide-13
SLIDE 13

Model - Introduction

secret of value S N employees Alice, the manager, selects k employees

Laszka et al. (PennState) Managing the Weakest Link 8 / 26

slide-14
SLIDE 14

Model - Introduction

secret of value S N employees Alice, the manager, selects k employees Eve, the adversary

Laszka et al. (PennState) Managing the Weakest Link 8 / 26

slide-15
SLIDE 15

Model - Introduction

secret of value S N employees Alice, the manager, selects k employees Eve, the adversary targets an employee and tries to bribe her with a value of b

Laszka et al. (PennState) Managing the Weakest Link 8 / 26

slide-16
SLIDE 16

Model - Introduction

secret of value S N employees Alice, the manager, selects k employees Eve, the adversary bribe value b has to be higher than the trustworthiness level Ti of the employee FAIL

Laszka et al. (PennState) Managing the Weakest Link 8 / 26

slide-17
SLIDE 17

Trustworthiness Level Distributions

the probability that the bribe is successful (given that the targeted employee actually knows the secret) is increasing in the bribe value we assume that both players can learn the trustworthiness level distributions b 1 Pr[Ti < b]

Laszka et al. (PennState) Managing the Weakest Link 9 / 26

slide-18
SLIDE 18

Model - Details

Game-theoretic model two-player, one-shot game Alice, the manager, selects a set I of k employees → her pure strategies are the k-subsets of N Eve, the adversary, targets an employee i and chooses a bribe value b → her pure strategies are (i, b) pairs when Alice selects set I and Eve chooses (i, b)

◮ if i ∈ I and b ≥ Ti: Eve learns the secret and gains S − b, while Alice

loses S

◮ if i ∈ I or b < Ti: Eve does not learn the secret and loses b, while

Alice does not lose anything

information available to the players

◮ both players know the employees’ trustworthiness distributions ◮ but they do not know the other players’ strategic choice

mixed strategies

◮ Alice: probability ai of sharing the secret with employee i Laszka et al. (PennState) Managing the Weakest Link 10 / 26

slide-19
SLIDE 19

Na¨ ıve Ideas

“Select the k most trustworthy employees.”

Laszka et al. (PennState) Managing the Weakest Link 11 / 26

slide-20
SLIDE 20

Na¨ ıve Ideas

“Select the k most trustworthy employees.” “Eve will always target the employees who are the most likely to know the secret.”

Laszka et al. (PennState) Managing the Weakest Link 11 / 26

slide-21
SLIDE 21

Na¨ ıve Ideas

“Select the k most trustworthy employees.” “Eve will always target the employees who are the most likely to know the secret.” “If the secret has to be shared with more employees (i.e., if k is higher), it is never safer.”

Laszka et al. (PennState) Managing the Weakest Link 11 / 26

slide-22
SLIDE 22

Na¨ ıve Ideas

“Select the k most trustworthy employees.” “Eve will always target the employees who are the most likely to know the secret.” “If the secret has to be shared with more employees (i.e., if k is higher), it is never safer.”

They

Laszka et al. (PennState) Managing the Weakest Link 11 / 26

slide-23
SLIDE 23

Na¨ ıve Ideas

“Select the k most trustworthy employees.” “Eve will always target the employees who are the most likely to know the secret.” “If the secret has to be shared with more employees (i.e., if k is higher), it is never safer.”

They are all

Laszka et al. (PennState) Managing the Weakest Link 11 / 26

slide-24
SLIDE 24

Na¨ ıve Ideas

“Select the k most trustworthy employees.” “Eve will always target the employees who are the most likely to know the secret.” “If the secret has to be shared with more employees (i.e., if k is higher), it is never safer.”

They are all wrong!

Laszka et al. (PennState) Managing the Weakest Link 11 / 26

slide-25
SLIDE 25

Game-Theoretic Analysis

Outline Eve’s expected gain from targeting a given employee theorems characterizing Alice’s and Eve’s equilibrium strategies (For a more detailed and formal discussion, please see the paper.)

Laszka et al. (PennState) Managing the Weakest Link 12 / 26

slide-26
SLIDE 26

Eve’s Gain from Targeting a Given Employee i

bribing cost b expected benefit Pr[Ti ≤ b] · S· ai

Laszka et al. (PennState) Managing the Weakest Link 13 / 26

slide-27
SLIDE 27

Eve’s Gain from Targeting a Given Employee i

not profitable profitable not profitable bribing cost b expected benefit Pr[Ti ≤ b] · S· ai

Laszka et al. (PennState) Managing the Weakest Link 13 / 26

slide-28
SLIDE 28

Eve’s Gain from Targeting a Given Employee i

MaxUE(Ti, ai) > 0 maximum profit not profitable profitable not profitable bribing cost b expected benefit Pr[Ti ≤ b] · S· ai

Laszka et al. (PennState) Managing the Weakest Link 13 / 26

slide-29
SLIDE 29

Eve’s Gain from Targeting a Given Employee i

bribing cost b expected benefit Pr[Ti ≤ b] · S· ai

Laszka et al. (PennState) Managing the Weakest Link 14 / 26

slide-30
SLIDE 30

Eve’s Gain from Targeting a Given Employee i

MaxUE(Ti, ai) = 0 “profitable” not profitable bribing cost b expected benefit Pr[Ti ≤ b] · S· ai

Laszka et al. (PennState) Managing the Weakest Link 14 / 26

slide-31
SLIDE 31

Alice’s Strategy in an Equilibrium

Theorem

Alice is either secure, that is, Eve has no strategy against her with a positive gain, or she shares the secret with every employee with non-zero probability. Over the set of employee with whom Alice does not certainly share the secret, Eve’s expected gain is uniform. Furthermore, this expected gain is at least as much as the gain from any employee with whom Alice shares the secret certainly. sharing probability ai expected gain MaxUE(Ti, ai) 1

Laszka et al. (PennState) Managing the Weakest Link 15 / 26

slide-32
SLIDE 32

Eve’s Strategy in an Equilibrium

Theorem

Over the set of employees with whom Alice does not certainly share the secret, the probability that Eve learns the secret from a given employee is uniform. The employees with whom Alice shares the secret with certainty are at most as likely to be targeted by Eve as the other employees, with whom Alice is less likely to share the secret.

Laszka et al. (PennState) Managing the Weakest Link 16 / 26

slide-33
SLIDE 33

Computing an Equilibrium

Our characterizations of the players’ equilibrium strategies are not

  • nly necessary but also sufficient

Find a strategy satisfying Alice’s equilibrium strategy characterization Find an equilibrium strategy for Eve Find the employees with the highest expected gain and the corresponding bribe values Find a distribution equalizing Alice’s loss over the employees “Find”: any multidimensional numerical optimization method (e.g., the Nelder-Mead algorithm)

Laszka et al. (PennState) Managing the Weakest Link 17 / 26

slide-34
SLIDE 34

Uniform Trustworthiness Distributions

good approximation when little information is available b li hi never reveals the secret always reveals the secret 1 Pr[Ti < b]

Laszka et al. (PennState) Managing the Weakest Link 18 / 26

slide-35
SLIDE 35

Uniform Trustworthiness Distributions

Lemma

For a given employee i, Eve’s optimal bribe value is either 0 or hi (or both). b li hi 1 Pr[Ti < b] this does not matter actually

Laszka et al. (PennState) Managing the Weakest Link 19 / 26

slide-36
SLIDE 36

Uniform Trustworthiness Distributions

Lemma

Let k′ be

i hi/S. Then, the equilibrium of the game can be

characterized as follows: k < k′: Alice is perfectly secure, Eve never bribes any of the employees.

“There is a critical team size, below which we can be perfectly secure, ...”

Laszka et al. (PennState) Managing the Weakest Link 20 / 26

slide-37
SLIDE 37

Uniform Trustworthiness Distributions

Lemma

Let k′ be

i hi/S. Then, the equilibrium of the game can be

characterized as follows: k < k′: Alice is perfectly secure, Eve never bribes any of the employees. k > k′: Alice is not secure, Eve always chooses a sufficiently high bribe value and learns the secret with non-zero probability. k = k′: Eve can choose one of the above.

“There is a critical team size, below which we can be perfectly secure, but above which our only chance is randomizing the selection.”

Laszka et al. (PennState) Managing the Weakest Link 20 / 26

slide-38
SLIDE 38

Uniform Trustworthiness Distributions - Illustration

Laszka et al. (PennState) Managing the Weakest Link 21 / 26

slide-39
SLIDE 39

Conclusions and Open Problems

Conclusions & lessons learned

◮ game-theoretic model for bribe-resistant team composition ◮ do not (always) follow your intuitions ◮ a project manager should select every employee with a non-zero

probability, unless there is a perfectly secure strategy

◮ trusting people is tricky Laszka et al. (PennState) Managing the Weakest Link 22 / 26

slide-40
SLIDE 40

Conclusions and Open Problems

Conclusions & lessons learned

◮ game-theoretic model for bribe-resistant team composition ◮ do not (always) follow your intuitions ◮ a project manager should select every employee with a non-zero

probability, unless there is a perfectly secure strategy

◮ trusting people is tricky

Open problems

◮ study the model instantiated with actual data ◮ targeting multiple employees at the same time ◮ asymmetric information Laszka et al. (PennState) Managing the Weakest Link 22 / 26

slide-41
SLIDE 41

Thank you for your attention! Questions?

laszka@crysys.hu, johnsonb@ischool.berkeley.edu, pascal.schoettle@wi.uni-muenster.de, jensg@ist.psu.edu, rainer.boehme@wi.uni-muenster.de

Acknowledgements

We gratefully acknowledge the support of the Penn State Institute for Cyber-Science. The first author would like to thank the Campus Hungary Program for supporting his research visit. The third author would like to thank the Office of Naval Research (ONR) for supporting his research visit under Visiting Scientists Grant N62909-13-1-V029.

Laszka et al. (PennState) Managing the Weakest Link 23 / 26

slide-42
SLIDE 42

References I

[1] Nick Bontis. Assessing knowledge assets: A review of the models used to measure intellectual capital. International Journal of Management Reviews, 3(1):41–60, 2001. [2] Corporate Trust (Business Risk & Crisis Mgmt. GmbH). Studie: Industriespionage 2012 - Aktuelle Risiken f¨ ur die deutsche Wirtschaft durch Cyberwar, 2012. [3] FBI. The insider threat. http://www.fbi.gov/about-us/investigate/ counterintelligence/insider_threat_brochure, April 2013.

Laszka et al. (PennState) Managing the Weakest Link 24 / 26

slide-43
SLIDE 43

References II

[4] Federal Bureau of Investigation. Economic espionage. http://www.fbi.gov/about-us/investigate/ counterintelligence/economic-espionage. [5] Peter Finn. Chinese citizen sentenced in military data-theft case. Washington Post, March 2013. [6] Asmaa Munshi, Peter Dell, and Helen Armstrong. Insider threat behavior factors: A comparison of theory with reported incidents. In IEEE HICSS 2012, pages 2402–2411, 2012.

Laszka et al. (PennState) Managing the Weakest Link 25 / 26

slide-44
SLIDE 44

References III

[7] Marisa Randazzo, Michelle Keeney, Eileen Kowalski, Dawn Cappelli, and Andrew Moore. Insider threat study: Illicit cyber activity in the banking and finance sector. Technical Report CMU/SEI-2004-TR-021, Carnegie Mellon University, June 2005.

Laszka et al. (PennState) Managing the Weakest Link 26 / 26