Big Game Theory Hunting: The Peculiarities of Human Behavior in the InfoSec Game

BIG GAME THEORY HUNTING THE PECULIARITIES OF HUMAN BEHAVIOR IN THE INFOSEC GAME Kelly Shortridge (@swagitda_) Black Hat 2017

I’m Kelly

This is game theory

It’s time for hunting some game theory 4

Do you believe bug-free software is a reasonable assumption? 5

Do you believe wetware is more complex than software? 6

Traditional Game Theory relies on the assumption of bug-free wetware 7

Behavioral Game Theory assumes there’s no such thing as bug-free 8

“Think how hard physics would be if particles could think” —Murray Gell-Mann 9

“Amateurs study cryptography, professionals study economics” —Dan Geer quoting Allan Schiffman 10

This is what you’ll learn: 11

1. Why traditional game theory isn’t even a theory and is unfit for strategy-making
2. A new framework for modeling the infosec game based on behavioral insights 3. New defensive strategies that exploit your adversaries’ “thinking” and “learning” 12

Let’s go hunting to find out why 13

I. What is Game Theory?

tl;dr – game theory is a mathematical language used to describe scenarios of conflict and cooperation 15

Game theory is more about language than theory Use it as a engendering tool, not as something to dictate optimal strategies 16

GT applies whenever actions of players are interdependent Strategic scenarios include many types of games, with different “solutions” for each 17

Zero Sum Games Lou Levit 18

Non-Zero Sum Games 19

Negative Sum Games 20

Positive Sum Games 21

Complete vs. Incomplete Information 22

Perfect vs. Imperfect Information Paul Green 23

Information Symmetry vs. Asymmetry Dakota Corbin 24

Defender Attacker Defender Games

Sequential games in which sets of players are attackers and defenders Assumes people are risk-neutral & attackers want to be maximally harmful 26

First move = defenders choosing a defensive investment plan Second move = attackers observe the defensive preparations & choose an attack plan 27

Nash Equilibrium is often used to “solve” games. This is bad. 28

Nash Equilibrium = optimal outcome of a noncooperative game Players are making the best decisions for themselves while taking their opponent’s decisions into account 29

Prisoner’s Dilemma Player 1 Player 2 Confess Refuse Confess -2, -2 0, -4 Refuse -4, 0 -1, -1 30

Nash Equilibrium is based on a priori reasoning Assumes rational, all-knowing players Assumes others’ decisions don’t affect you 31

People have applied Nash Equilibrium to infosec over the years… 32

Defender should play extremely fast so the attacker drops out of the game Better to invest in security than not invest, regardless of attacker strategy (wow!) Just apply tons of mathematical equations! 33

II. New defensive framework

Use GT for its expressive power in describing a framework for the infosec game Look at data outside GT, e.g. from experiments in domains similar to infosec, to select correct assumptions 35

What game is infosec? Ruben Bagues

DAD game (continuous defense & attack) Non-zero-sum Incomplete, imperfect, asymmetrical information Sequential / dynamic 37

This is a (uniquely?) tricky game 38

Have you heard infosec described as a “cat and mouse” game before? 39

Traditional Game Theory doesn’t allow for those… …or most of the characteristics of the “infosec game” 40

Assumes people are rational (they aren’t) Assumes static vs. dynamic environments Can’t ever be “one step ahead” of your adversary Deviations from Nash Equilibrium are common 41

“I feel, personally, that the study of experimental games is the proper route of travel for finding ‘the ultimate truth’ in relation to games as played by human players” —John Nash 42

Behavior-based framework Dayne Topkin

Experimental – how do people actually behave? People predict their opponent’s moves by either “thinking” or “learning” 44

Thinking = modeling how opponents are likely to respond 45

Our brains work like volatile memory 46

Working memory is a hard constraint for human thinking Enumerating steps past the next round is hard Humans kinda suck at recursion 47

Learning = predicting how players will act based on prior games / rounds 48

Humans learn through “error-reinforcement learning” (trial & error) People have “learning rates,” how much experiences factor into decision making Dopamine neurons encode errors 49

Veksler & Buchler study 200 consecutive “security games” across 4 strategies Different learning rates for attackers Tested # of prevented attacks for each strategy 50

Fixed strategy = prevent 10% - 25% of attacks Game Theory strategy = prevent 50% of attacks Random strategy = prevent 49.6% of attacks Cognitive Modeling strategy = prevent between 61% - 77% 51

Don’t be replaced by a random SecurityStrategyTM algorithm 52

III. Implementation 53

1. SWOT Analysis
2. Thinking Exploitation 3. Learning Exploitation 4. Minimax
3. Looking Ahead 54

SWOT Analysis Scott Webb

101: Traditional SWOT Strengths, Weaknesses, Opportunities, Threats 56

Model SWOT for yourself in relation to your adversary Model SWOT for your adversary in relation to you 57

“The primary insight of GT is the importance of focusing on others – of putting yourself in the shoes of other players and trying to play out all the reactions…as far ahead as possible” – Adam Brandenburger 58

Strengths Weaknesses ▪ Understanding of target environment ▪ Inadequate budget ▪ Motivation to not be breached ▪ Limited employee training ▪ Lack of personnel Opportunities Threats ▪ Leverage new tech to allow for tear up/down ▪ Attackers can use new tech for scalability ▪ Increased board attention to get budget ▪ Hard to keep up with pace of new attack surface 59

201: Perceptual SWOT 60

For you and your adversary, consider: How can the strengths be weaknesses? How can the weaknesses be strengths? 61

Self vs. Other Reality Strength Perception Threat Opportunity 62 Strength Weakness Weakness Strength Weakness Perception Strength Reality Weakness Opportunity Threat

“Core rigidities” = deeply embedded knowledge sets that create problems Compliance, fixed security guidelines Top management can be the wrong people for an evolving environment 63

Attacker strength = having time to craft an attack Leverage that “strength” with strategies leading down rabbit holes and wasting their time 64

Attacker strength = access to known vulns Confuse them with fake architecture so they can’t be certain what systems you’re running 65

Thinking Exploitation

Thinking strategy: belief prompting Increase players thinking by one step 67

“Prompt” the player to consider who their opponents are & how their opponents will react Model assumptions around capital, time, tools, risk aversion 68

Your goal is to ask, “if I do X, how will that change my opponent’s strategy?” 69

A generic belief prompting guide: 70

How would attackers pre-emptively bypass the defensive move? What will the opponent do next in response? Costs of the opponent’s offensive move? Probability the opponent will conduct the move? 71

Example: A skiddie lands on one of our servers, what do they do next? 72

Perform local recon, escalate to whatever privs they can get Counter: priv separation, don’t hardcode creds Leads to: attacker must exploit server, risk = server crashes 73

Decision Tree Modelling Jessica Furtney

Model decision trees both for offense & defense Theorize probabilities of each branch’s outcome Creates tangible metrics to deter selfjustification 75

“Attackers will take the least cost path through an attack graph from their start node to their goal node” – Dino Dai Zovi, “Attacker Math” 76

25% 65% Reality Criminal Group Skiddies / Random 60% Priv Separation 0% Nation State 100% 10% 50% 50% Known exploit 30% Role sep 0% 60% Use DB on box 0% Win 40% Absorb into 25% botnet Tokenization, 70% segmentation 40% Anomaly detection 0% Win 77 98% 50% seccomp 10% 1day GRSec 5% 2% Elite 0day

1. Which of your assets do attackers want? 2. What’s the easiest way attackers get to those assets? 3. What countermeasures are on that path? 4. What new path will the attacker take given #3? 5. Repeat 1 – 4 until it’s “0day all the way down”
2. Assign rough probabilities 78

Whiteboards + camera snaps (or “DO NOT ERASE!!!!”) Draw.io, Gliffy (plugs into Confluence) Google Docs (> insert drawing) PowerPoint (what I used) Visio (last resort) 79

Decision trees help create a feedback loop to refine strategy 80

Decision trees help for auditing after an incident & easy updating Serves as a historical record to refine decisionmaking process Mitigates “doubling down” effect by showing where strategy failed 81

Defender’s advantage = knowing the home turf Visualize the hardest path for attackers – how can you force them onto to that path? Commonalities on trees = which strategies mitigate the most risk across various attacks 82

Make decision trees the new “nice report” #WOCinTech

A new request for your pen-testers / red-team The ask: outline which paths they did or didn’t take, and why (a decision tree w/ explanations) Helps you see the attacker perspective of your defenses & where to improve 84

Learning Exploitation

Information asymmetry exploitation – disrupt attacker learning process Learning rate exploitation – introduce unreliability and pre-empt moves 86

Exploit the fact that you understand the local environment better than attackers 87

Falsifying Data Braydon Anderson

Defenders have info adversaries need to intercept Dynamic envs = frequently in learning phase Hide or falsify data on the legitimate system side 89

Macron Case Study Soroush Karimi

Allegedly used phishing tarpitting Signed onto phishing pages & planted bogus creds and info Obvious fakes in dumped documents 91

#wastehistime2016…but for hackers 92

Goal is to remove the attacker’s scientific method so they can’t test hypotheses (Pretend like hashtags are a thing and tweet #wastehackertime2017 with your own ideas) 93

Create custom email rejection messages Create honeydoc on the “Avallach Policy” Have response to suspicious emails be, “This violates the Avallach policy” Track when the doc is accessed 94

General strategy: create honeytokens that look to describe legitimate policies or technologies that would be useful in attacker recon 95

Non-Determinism Candice Seplow

Different behaviors at different times Can’t expect same result every time 97

ASLR is a non-deterministic feature, but highly deterministic in that it always works the same I want to amplify and extend it to higher levels 98

Raise costs at the very first step of the attack: recon Make the attacker uncertain of your defensive profile and environment 99

Attackers now design malware to be VM-aware #WOCinTech 100

Good: Make everything look like a malware analyst’s sandbox Better: Make everything look like a different malware analyst’s sandbox each time 101

Put wolfskins on the sheep

Mix & match hollow but sketchylooking artifacts on normal, physical systems 103

RocProtect-v1 – https://github.com/fr0gger/RocProtect-V1 Emulates virtual artifacts onto physical machine (see Unprotect Project as well) 104

VMwareServices.exe VBoxService.exe Vmwaretray.exe VMSrvc.exe vboxtray.exe ollydbg.exe wireshark.exe fiddler.exe \.\pipe\cuckoo cuckoomon.dll dbghelp.dll Mac addresses: “00:0C:29”, “00:1C:14”, “00:50:56”, “00:05:69” 105

system32\drivers\VBoxGuest.sys system32\drivers\VBoxMouse.sys HKLM\SOFTWARE\Oracle\VirtualBox Guest Additions C:\cuckoo, C:\IDA Program Files\Vmware 106

Make the IsDebuggerPresent function call always return non-zero Create fake versions of driver objects like .\NTICE and .\SyserDbgMsg Set KdDebuggerEnabled to 0x03 107

Load DLLs from AV engines using a Windows loader with a forwarder DLL ex64.sys (Symantec) McAVSCV.DLL (McAfee) SAUConfigDLL.dll (Sophos) cbk7.sys (Carbon Black) cymemdef.dll (Cylance) CSAgent.sys (Crowdstrike) 108

Deploy lightest weight hypervisor possible for added “wolfskin” https://github.com/asamy/ksm https://github.com/ionescu007/SimpleVisor https://github.com/Bareflank/hypervisor 109

Minimax Mike Wilson 110

Minimax / maximin = minimize the possible loss for a worst case maximum loss scenario 111

Want to find the minimum of the sum of the expected cost of protection and expected cost of non-protection 112

Don’t have a monoculture – diversity is strongly beneficial for protection Stochastic decisions may be better than deterministic From The Imitation Game: should only act on Enigma info some of the time, not all 113

Looking Ahead

Fluctuating infrastructure using emerging tech in “Infrastructure 3.0” Netflix’s Chaos Monkey https://github.com/Netflix/SimianArmy/wiki/Cha os-Monkey 115

Modelling attacker cognition via model tracing Prerequisite: how to begin observing attacker cognition 116

Preferences change based on experience Models incorporate the “post-decision-state” Higher the attacker’s learning rate, easier to predict their decisions 117

ΔUA = α (R – UA), where: ▪ UA = expected utility of an offensive action ▪ α = learning rate ▪ R = feedback (success / failure) 118

If α = 0.2, R = 1 for win & -1 for loss, then: ▪ ΔUA = 0.2(1- 0) = 0.2 Attacker is 20% more likely to do this again From here, you can adjust the learning rate based on data you see 119

Track utility values for each attacker action For detected / blocked actions, attacker action & outcome are known variables (so utility is calculable) Highest “U” = action attacker will pursue 120

IV. Conclusion 121

It is no longer time for some Game Theory 122

In fact, we’ve learned that GT is a language, not even a theory 123

Start with a SWOT analysis to gain perspective 124

Use thinking exploitation to improve threat modelling 125

Use learning exploitation to beleaguer your adversaries 126

Let’s work together to build strategies based on this behavioral framework 127

Next step – how to begin modeltracing attackers After that – predict attacker behavior 128

Try these at home – make your blue team empirical Worst case, random strategies beat fixed ones & are just as good as GT 129

“Good enough is good enough. Good enough always beats perfect.” —Dan Geer 130

Suggested reading ▪ David Laibson’s Behavioral Game Theory lectures @ Harvard ▪ “Game Theory: A Language of Competition and Cooperation,” Adam Brandenburger ▪ “Advances in Understanding Strategic Behavior,” Camerer, Ho, Chong ▪ “Know Your Enemy: Applying Cognitive Modeling in the Security Domain,” Veksler, Buchler ▪ “Know Your Adversary: Insights for a Better Adversarial Behavioral Model,” Abbasi, et al. ▪ “Deterrence and Risk Preferences in Sequential Attacker–Defender Games with Continuous Efforts,” Payappalli, Zhuang, Jose ▪ “Improving Learning and Adaptation in Security Games by Exploiting Information Asymmetry,” He, Dai, Ning ▪ “Behavioral theories and the neurophysiology of reward,” Schultz ▪ “Evolutionary Security,” and “Measuring Security,” Dan Geer 131

@swagitda_ /in/kellyshortridge kelly@greywire.net 132