Self-learning Intrusion Prevention Systems.

1. 1/28
Self-Learning Intrusion Prevention Systems
NSE Seminar 21/10 2022
Kim Hammar & Rolf Stadler
π
Σ Σ
security
objective
feedback
control
input
target
system
security
indicators
disturbance

2. 2/28
Use Case: Intrusion Prevention
I A Defender owns an infrastructure
I Consists of connected components
I Components run network services
I Defender defends the infrastructure
by monitoring and active defense
I Has partial observability
I An Attacker seeks to intrude on the
infrastructure
I Has a partial view of the
infrastructure
I Wants to compromise specific
components
I Attacks by reconnaissance,
exploitation and pivoting
Attacker Clients
. . .
Defender
1 IPS
1
alerts
Gateway
7 8 9 10 11
6
5
4
3
2
12
13 14 15 16
17
18
19
21
23
20
22
24
25 26
27 28 29 30 31

3. 3/28
Challenges: Evolving and Automated Attacks
I Challenges
I Evolving & automated attacks
I Complex infrastructures
Attacker Clients
. . .
Defender
1 IPS
1
alerts
Gateway
7 8 9 10 11
6
5
4
3
2
12
13 14 15 16
17
18
19
21
23
20
22
24
25 26
27 28 29 30 31

4. 3/28
Goal: Automation and Learning
I Challenges
I Evolving & automated attacks
I Complex infrastructures
I Our Goal:
I Automate security tasks
I Adapt to changing attack methods
Attacker Clients
. . .
Defender
1 IPS
1
alerts
Gateway
7 8 9 10 11
6
5
4
3
2
12
13 14 15 16
17
18
19
21
23
20
22
24
25 26
27 28 29 30 31

5. 3/28
Approach: Self-Learning Security Systems
I Challenges
I Evolving & automated attacks
I Complex infrastructures
I Our Goal:
I Automate security tasks
I Adapt to changing attack methods
I Our Approach: Self-Learning
Systems:
I real-time telemetry
I stream processing
I theories from control/game/decision
theory
I computational methods (e.g.
dynamic programming &
reinforcement learning)
I automated network management
(SDN, NFV, etc.)
Attacker Clients
. . .
Defender
1 IPS
1
alerts
Gateway
7 8 9 10 11
6
5
4
3
2
12
13 14 15 16
17
18
19
21
23
20
22
24
25 26
27 28 29 30 31

6. 4/28
The Intrusion Prevention Problem
20 40 60 80 100 120 140 160 180 200
0.5
1
t
b
20 40 60 80 100 120 140 160 180 200
50
100
150
200
t
Alerts
20 40 60 80 100 120 140 160 180 200
5
10
15
t
Logins

7. 4/28
The Intrusion Prevention Problem
20 40 60 80 100 120 140 160 180 200
0.5
1
t
b
20 40 60 80 100 120 140 160 180 200
50
100
150
200
t
Alerts
20 40 60 80 100 120 140 160 180 200
5
10
15
t
Logins

8. 4/28
The Intrusion Prevention Problem
20 40 60 80 100 120 140 160 180 200
0.5
1
t
b
20 40 60 80 100 120 140 160 180 200
50
100
150
200
t
Alerts
20 40 60 80 100 120 140 160 180 200
5
10
15
t
Logins

9. 4/28
The Intrusion Prevention Problem
20 40 60 80 100 120 140 160 180 200
0.5
1
t
b
20 40 60 80 100 120 140 160 180 200
50
100
150
200
t
Alerts
20 40 60 80 100 120 140 160 180 200
5
10
15
t
Logins

10. 4/28
The Intrusion Prevention Problem
20 40 60 80 100 120 140 160 180 200
0.5
1
t
b
20 40 60 80 100 120 140 160 180 200
50
100
150
200
t
Alerts
20 40 60 80 100 120 140 160 180 200
5
10
15
t
Logins

11. 4/28
The Intrusion Prevention Problem
20 40 60 80 100 120 140 160 180 200
0.5
1
t
b
20 40 60 80 100 120 140 160 180 200
50
100
150
200
t
Alerts
20 40 60 80 100 120 140 160 180 200
5
10
15
t
Logins

12. 4/28
The Intrusion Prevention Problem
20 40 60 80 100 120 140 160 180 200
0.5
1
t
b
20 40 60 80 100 120 140 160 180 200
50
100
150
200
t
Alerts
20 40 60 80 100 120 140 160 180 200
5
10
15
t
Logins

13. 4/28
The Intrusion Prevention Problem
20 40 60 80 100 120 140 160 180 200
0.5
1
t
b
20 40 60 80 100 120 140 160 180 200
50
100
150
200
t
Alerts
20 40 60 80 100 120 140 160 180 200
5
10
15
t
Logins

14. 4/28
The Intrusion Prevention Problem
20 40 60 80 100 120 140 160 180 200
0.5
1
t
b
20 40 60 80 100 120 140 160 180 200
50
100
150
200
t
Alerts
20 40 60 80 100 120 140 160 180 200
5
10
15
t
Logins
When to take a defensive action?
Which action to take?

15. 5/28
Self-learning Intrusion Prevention: Current Landscape
Levels of security automation
No automation.
Manual detection.
Manual prevention.
No alerts.
No automatic responses.
Lack of tools.
1980s 1990s 2000s-Now Research
Operator assistance.
Manual detection.
Manual prevention.
Audit logs.
Security tools.
Partial automation.
System has automated functions
for detection/prevention
but requires manual
updating and configuration.
Intrusion detection systems.
Intrusion prevention systems.
High automation.
System automatically
updates itself.
Automated attack detection.
Automated attack mitigation.

16. 6/28
Outline
I Use Case & Motivation:
I Use case: Intrusion prevention
I Self-learning security systems: current landscape
I Our Approach
I Network emulation and digital twin
I Stochastic game simulation and reinforcement learning
I Summary of results so far
I Comparison with related work
I Intrusion prevention through optimal multiple stopping
I Dynkin games and learning in dynamic environments
I System for policy validation
I Outlook on future work
I Extend use case
I Rollout-based methods
I Conclusions
I Takeaways

17. 6/28
Outline
I Use Case & Motivation:
I Use case: Intrusion prevention
I Self-learning security systems: current landscape
I Our Approach
I Network emulation and digital twin
I Stochastic game simulation and reinforcement learning
I Summary of results so far
I Comparison with related work
I Intrusion prevention through optimal multiple stopping
I Dynkin games and learning in dynamic environments
I System for policy validation
I Outlook on future work
I Extend use case
I Rollout-based methods
I Conclusions
I Takeaways

18. 6/28
Outline
I Use Case & Motivation:
I Use case: Intrusion prevention
I Self-learning security systems: current landscape
I Our Approach
I Network emulation and digital twin
I Stochastic game simulation and reinforcement learning
I Summary of results so far
I Comparison with related work
I Intrusion prevention through optimal multiple stopping
I Dynkin games and learning in dynamic environments
I System for policy validation
I Outlook on future work
I Extend use case
I Rollout-based methods
I Conclusions
I Takeaways

19. 6/28
Outline
I Use Case & Motivation:
I Use case: Intrusion prevention
I Self-learning security systems: current landscape
I Our Approach
I Network emulation and digital twin
I Stochastic game simulation and reinforcement learning
I Summary of results so far
I Comparison with related work
I Intrusion prevention through optimal multiple stopping
I Dynkin games and learning in dynamic environments
I System for policy validation
I Outlook on future work
I Extend use case
I Rollout-based methods
I Conclusions
I Takeaways

20. 6/28
Outline
I Use Case & Motivation:
I Use case: Intrusion prevention
I Self-learning security systems: current landscape
I Our Approach
I Network emulation and digital twin
I Stochastic game simulation and reinforcement learning
I Summary of results so far
I Comparison with related work
I Intrusion prevention through optimal multiple stopping
I Dynkin games and learning in dynamic environments
I System for policy validation
I Outlook on future work
I Extend use case
I Rollout-based methods
I Conclusions
I Takeaways

21. 7/28
Our Approach for Automated Network Security
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Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

22. 7/28
Our Approach for Automated Network Security
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
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Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

23. 7/28
Our Approach for Automated Network Security
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
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Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

24. 7/28
Our Approach for Automated Network Security
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
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.
Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

25. 7/28
Our Approach for Automated Network Security
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
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.
.
.
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.
.
.
.
.
.
Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

26. 7/28
Our Approach for Automated Network Security
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
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.
Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

27. 7/28
Our Approach for Automated Network Security
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
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Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

28. 7/28
Our Approach for Automated Network Security
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
.
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.
.
.
.
.
.
.
.
.
.
.
.
Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

29. 8/28
Creating a Digital Twin of the Target Infrastructure
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
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.
.
.
.
.
.
.
.
Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

30. 9/28
Creating a Digital Twin of the Target Infrastructure
I Emulate hosts with docker containers
I Emulate IPS and vulnerabilities with
software
I Network isolation and traffic shaping
through NetEm in the Linux kernel
I Enforce resource constraints using
cgroups.
I Emulate client arrivals with Poisson
process
I Internal connections are full-duplex
& loss-less with bit capacities of 1000
Mbit/s
I External connections are full-duplex
with bit capacities of 100 Mbit/s &
0.1% packet loss in normal operation
and random bursts of 1% packet loss
Attacker Clients
. . .
Defender
1 IPS
1
alerts
Gateway
7 8 9 10 11
6
5
4
3
2
12
13 14 15 16
17
18
19
21
23
20
22
24
25 26
27 28 29 30 31

31. 9/28
Creating a Digital Twin of the Target Infrastructure
I Emulate hosts with docker containers
I Emulate IPS and vulnerabilities with
software
I Network isolation and traffic shaping
through NetEm in the Linux kernel
I Enforce resource constraints using
cgroups.
I Emulate client arrivals with Poisson
process
I Internal connections are full-duplex
& loss-less with bit capacities of 1000
Mbit/s
I External connections are full-duplex
with bit capacities of 100 Mbit/s &
0.1% packet loss in normal operation
and random bursts of 1% packet loss
Attacker Clients
. . .
Defender
1 IPS
1
alerts
Gateway
7 8 9 10 11
6
5
4
3
2
12
13 14 15 16
17
18
19
21
23
20
22
24
25 26
27 28 29 30 31

32. 9/28
Creating a Digital Twin of the Target Infrastructure
I Emulate hosts with docker containers
I Emulate IPS and vulnerabilities with
software
I Network isolation and traffic shaping
through NetEm in the Linux kernel
I Enforce resource constraints using
cgroups.
I Emulate client arrivals with Poisson
process
I Internal connections are full-duplex
& loss-less with bit capacities of 1000
Mbit/s
I External connections are full-duplex
with bit capacities of 100 Mbit/s &
0.1% packet loss in normal operation
and random bursts of 1% packet loss
Attacker Clients
. . .
Defender
1 IPS
1
alerts
Gateway
7 8 9 10 11
6
5
4
3
2
12
13 14 15 16
17
18
19
21
23
20
22
24
25 26
27 28 29 30 31

33. 9/28
Creating a Digital Twin of the Target Infrastructure
I Emulate hosts with docker containers
I Emulate IPS and vulnerabilities with
software
I Network isolation and traffic shaping
through NetEm in the Linux kernel
I Enforce resource constraints using
cgroups.
I Emulate client arrivals with Poisson
process
I Internal connections are full-duplex
& loss-less with bit capacities of 1000
Mbit/s
I External connections are full-duplex
with bit capacities of 100 Mbit/s &
0.1% packet loss in normal operation
and random bursts of 1% packet loss
Attacker Clients
. . .
Defender
1 IPS
1
alerts
Gateway
7 8 9 10 11
6
5
4
3
2
12
13 14 15 16
17
18
19
21
23
20
22
24
25 26
27 28 29 30 31

34. 9/28
Creating a Digital Twin of the Target Infrastructure
I Emulate hosts with docker containers
I Emulate IPS and vulnerabilities with
software
I Network isolation and traffic shaping
through NetEm in the Linux kernel
I Enforce resource constraints using
cgroups.
I Emulate client arrivals with Poisson
process
I Internal connections are full-duplex
& loss-less with bit capacities of 1000
Mbit/s
I External connections are full-duplex
with bit capacities of 100 Mbit/s &
0.1% packet loss in normal operation
and random bursts of 1% packet loss
Attacker Clients
. . .
Defender
1 IPS
1
alerts
Gateway
7 8 9 10 11
6
5
4
3
2
12
13 14 15 16
17
18
19
21
23
20
22
24
25 26
27 28 29 30 31

35. 9/28
Creating a Digital Twin of the Target Infrastructure
I Emulate hosts with docker containers
I Emulate IPS and vulnerabilities with
software
I Network isolation and traffic shaping
through NetEm in the Linux kernel
I Enforce resource constraints using
cgroups.
I Emulate client arrivals with Poisson
process
I Internal connections are full-duplex
& loss-less with bit capacities of 1000
Mbit/s
I External connections are full-duplex
with bit capacities of 100 Mbit/s &
0.1% packet loss in normal operation
and random bursts of 1% packet loss
Attacker Clients
. . .
Defender
1 IPS
1
alerts
Gateway
7 8 9 10 11
6
5
4
3
2
12
13 14 15 16
17
18
19
21
23
20
22
24
25 26
27 28 29 30 31

36. 9/28
Our Approach for Automated Network Security
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

37. 10/28
System Identification
ˆ
f
O
(o
t
|0)
Probability distribution of # IPS alerts weighted by priority ot
0 1000 2000 3000 4000 5000 6000 7000 8000 9000
ˆ
f
O
(o
t
|1)
Fitted model Distribution st = 0 Distribution st = 1
I The distribution fO of defender observations (system metrics)
is unknown.
I We fit a Gaussian mixture distribution ˆ
fO as an estimate of fO
in the target infrastructure.
I For each state s, we obtain the conditional distribution ˆ
fO|s
through expectation-maximization.

38. 10/28
Our Approach for Automated Network Security
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Emulation System
Target
Infrastructure
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation System
Reinforcement Learning &
Generalization
Strategy evaluation &
Model estimation
Automation &
Self-learning systems

39. 11/28
The Simulation System
I We model the evolution of the system with a discrete-time
dynamical system.
I We assume a Markovian system with stochastic dynamics and
partial observability.
Stochastic
System
(Markov)
Noisy
Sensor
Optimal
filter
Controller
action at
observation
ot
state
st
belief
bt

40. 11/28
The Simulation System
I We model the evolution of the system with a discrete-time
dynamical system.
I We assume a Markovian system with stochastic dynamics and
partial observability.
I A Partially Observed Markov Decision Process (POMDP)
I If attacker is static.
I A Partially Observed Stochastic Game (POSG)
I If attacker is dynamic.
Stochastic
System
(Markov)
Noisy
Sensor
Optimal
filter
Controller
Attacker
action a
(1)
t
action a
(2)
t
observation
ot
state
st
belief
bt

41. 11/28
The Simulation System
I We model the evolution of the system with a discrete-time
dynamical system.
I We assume a Markovian system with stochastic dynamics and
partial observability.
I A Partially Observed Markov Decision Process (POMDP)
I If attacker is static.
I A Partially Observed Stochastic Game (POSG)
I If attacker is dynamic.
Stochastic
System
(Markov)
Noisy
Sensor
Optimal
filter
Controller
Attacker
action a
(1)
t
action a
(2)
t
observation
ot
state
st
belief
bt
I Compute/learn control strategies: Stochastic
approximation (RL), dynamic programming, linear
programming, etc.

42. 12/28
Outline
I Use Case & Motivation:
I Use case: Intrusion prevention
I Self-learning security systems: current landscape
I Our Approach
I Network emulation and digital twin
I Stochastic game simulation and reinforcement learning
I Summary of results so far
I Comparison with related work
I Intrusion prevention through optimal multiple stopping
I Dynkin games and learning in dynamic environments
I System for policy validation
I Outlook on future work
I Extend use case
I Rollout-based methods
I Conclusions
I Takeaways

43. 12/28
Outline
I Use Case & Motivation:
I Use case: Intrusion prevention
I Self-learning security systems: current landscape
I Our Approach
I Network emulation and digital twin
I Stochastic game simulation and reinforcement learning
I Summary of results so far
I Comparison with related work
I Intrusion prevention through optimal multiple stopping
I Dynkin games and learning in dynamic environments
I System for policy validation
I Outlook on future work
I Extend use case
I Rollout-based methods
I Conclusions
I Takeaways

44. 12/28
Related Work on Self-Learning Security Systems
External validity
Use case/
control/
learning
complexity
Goal
Georgia et al. 2000.
(Next generation
intrusion detection:
reinforcement learning)
Xu et al. 2005.
(An RL approach to
host-based
intrusion detection)
Servin et al. 2008.
(Multi-agent RL for
intrusion detection)

45. 12/28
Related Work on Self-Learning Security Systems
External validity
Use case/
control/
learning
complexity
Goal
Georgia et al. 2000.
(Next generation
intrusion detection:
reinforcement learning)
Xu et al. 2005.
(An RL approach to
host-based
intrusion detection)
Servin et al. 2008.
(Multi-agent RL for
intrusion detection)
Malialis et al. 2013.
(Decentralized
RL response to
DDoS attacks)
Zhu et al. 2019.
(Adaptive
Honeypot engagement)
Apruzzese et al. 2020.
(Deep RL to
evade botnets)

46. 12/28
Related Work on Self-Learning Security Systems
External validity
Use case/
control/
learning
complexity
Goal
Georgia et al. 2000.
(Next generation
intrusion detection:
reinforcement learning)
Xu et al. 2005.
(An RL approach to
host-based
intrusion detection)
Servin et al. 2008.
(Multi-agent RL for
intrusion detection)
Malialis et al. 2013.
(Decentralized
RL response to
DDoS attacks)
Zhu et al. 2019.
(Adaptive
Honeypot engagement)
Apruzzese et al. 2020.
(Deep RL to
evade botnets)
Xiao et al. 2021.
(RL approach to APT)
etc. 2022.

47. 12/28
Related Work on Self-Learning Security Systems
External validity
Use case/
control/
learning
complexity
Goal
Georgia et al. 2000.
(Next generation
intrusion detection:
reinforcement learning)
Xu et al. 2005.
(An RL approach to
host-based
intrusion detection)
Servin et al. 2008.
(Multi-agent RL for
intrusion detection)
Malialis et al. 2013.
(Decentralized
RL response to
DDoS attacks)
Zhu et al. 2019.
(Adaptive
Honeypot engagement)
Apruzzese et al. 2020.
(Deep RL to
evade botnets)
Xiao et al. 2021.
(RL approach to APT)
etc. 2022.

48. 12/28
Related Work on Self-Learning Security Systems
External validity
Use case/
control/
learning
complexity
Goal
Georgia et al. 2000.
(Next generation
intrusion detection:
reinforcement learning)
Xu et al. 2005.
(An RL approach to
host-based
intrusion detection)
Servin et al. 2008.
(Multi-agent RL for
intrusion detection)
Malialis et al. 2013.
(Decentralized
RL response to
DDoS attacks)
Zhu et al. 2019.
(Adaptive
Honeypot engagement)
Apruzzese et al. 2020.
(Deep RL to
evade botnets)
Xiao et al. 2021.
(RL approach to APT)
etc. 2022.
Our work 2020-2022
only
2 actions
3 states!

49. 13/28
1: Intrusion Prevention through Optimal Stopping1
I Intrusion Prevention as an Optimal Stopping Problem:
I A stochastic process (st)T
t=1 is observed sequentially
I Two options per t: (i) continue to observe; or (ii) stop
I Find the optimal stopping time τ∗
:
τ∗
= arg max
τ
Eτ
" τ−1
X
t=1
γt−1
RC
st st+1
+ γτ−1
RS
sτ sτ
#
where RS
ss0 & RC
ss0 are the stop/continue rewards
I Stop action = Defensive action
Intrusion event
time-step t = 1 Intrusion ongoing
t
t = T
Early stopping times Stopping times that
interrupt the intrusion
Episode
1
Kim Hammar and Rolf Stadler. “Learning Intrusion Prevention Policies through Optimal Stopping”. In:
International Conference on Network and Service Management (CNSM 2021).
http://dl.ifip.org/db/conf/cnsm/cnsm2021/1570732932.pdf. Izmir, Turkey, 2021.

50. 14/28
1: Intrusion Prevention through Optimal Stopping2
Stochastic
System
(Markov)
Noisy
Sensor
Optimal
filter
Stopping strategy
at ∈ {S, C}
IPS alerts
ot
state
st
belief
bt ∈ [0, 1]
I States: Intrusion st ∈ {0, 1}, terminal ∅.
I Observations:
I Number of IPS Alerts ot ∈ O
I ot is drawn from r.v. O ∼ fO(·|st).
I Based on history ht of observations, the defender can compute
the belief bt(st) = P[st|ht].
I Actions: A1 = A2 = {S, C}
I Rewards: security and service.
I Transition probabilities: Follows from game dynamics.
2
Kim Hammar and Rolf Stadler. “Learning Intrusion Prevention Policies through Optimal Stopping”. In:
International Conference on Network and Service Management (CNSM 2021).
http://dl.ifip.org/db/conf/cnsm/cnsm2021/1570732932.pdf. Izmir, Turkey, 2021.

51. 14/28
Convex Stopping set with Threshold α∗
1 ∈ B
b(1)
0 1
belief space B = [0, 1]

52. 14/28
Convex Stopping set with Threshold α∗
1 ∈ B
b(1)
0 1
belief space B = [0, 1]
S1
α∗
1

53. 15/28
Bang-Bang Controller with Threshold α∗
1 ∈ B
0
1
2
1
ak = π∗
k(b)
b∗
1
threshold
b (belief)
continue
stop

54. 16/28
Learning Curves in Simulation and Emulation
0
50
100
Novice
Reward per episode
0
50
100
Episode length (steps)
0.0
0.5
1.0
P[intrusion interrupted]
0.0
0.5
1.0
1.5
P[early stopping]
5
10
Duration of intrusion
−50
0
50
100
experienced
0
50
100
150
0.0
0.5
1.0
0.0
0.5
1.0
1.5
0
5
10
0 20 40 60
training time (min)
−50
0
50
100
expert
0 20 40 60
training time (min)
0
50
100
150
0 20 40 60
training time (min)
0.0
0.5
1.0
0 20 40 60
training time (min)
0.0
0.5
1.0
1.5
2.0
0 20 40 60
training time (min)
0
5
10
15
20
πθ,l simulation πθ,l emulation (∆x + ∆y) ≥ 1 baseline Snort IPS upper bound

55. 17/28
2: Intrusion Prevention through Optimal Multiple
Stopping3
I Intrusion Prevention
through Multiple Optimal
Stopping:
I Maximize reward of
stopping times
τL, τL−1, . . . , τ1:
π∗
l ∈ arg max
πl
Eπl
" τL−1
X
t=1
γt−1
RC
st ,st+1,L
+ γτL−1
RS
sτL
,sτL+1,L + . . . +
τ1−1
X
t=τ2+1
γt−1
RC
st ,st+1,1 + γτ1−1
RS
sτ1
,sτ1+1,1
#
I Each stopping time = one
defensive action
0 1
∅
t ≥ 1
lt > 0
t ≥ 2
lt > 0
intrusion starts
Qt = 1
final stop
lt = 0
intrusion
prevented
lt = 0
3
Kim Hammar and Rolf Stadler. “Intrusion Prevention Through Optimal Stopping”. In: IEEE Transactions on
Network and Service Management 19.3 (2022), pp. 2333–2348. doi: 10.1109/TNSM.2022.3176781.

56. 18/28
Structural Result: Optimal Multi-Threshold Policy &
Nested Stopping Sets
b(1)
0 1
belief space B = [0, 1]

57. 18/28
Structural Result: Optimal Multi-Threshold Policy &
Nested Stopping Sets
b(1)
0 1
belief space B = [0, 1]
S1
α∗
1

58. 18/28
Structural Result: Optimal Multi-Threshold Policy &
Nested Stopping Sets
b(1)
0 1
belief space B = [0, 1]
S1
S2
α∗
1
α∗
2

59. 18/28
Structural Result: Optimal Multi-Threshold Policy &
Nested Stopping Sets
b(1)
0 1
belief space B = [0, 1]
S1
S2
.
.
.
SL
α∗
1
α∗
2
α∗
L
. . .

60. 19/28
Comparison against State-of-the-art Algorithms
0 10 20 30 40 50 60
training time (min)
0
50
100
Reward per episode against Novice
0 10 20 30 40 50 60
training time (min)
Reward per episode against Experienced
0 10 20 30 40 50 60
training time (min)
Reward per episode against Expert
PPO ThresholdSPSA Shiryaev’s Algorithm (α = 0.75) HSVI upper bound

61. 20/28
3: Intrusion Prevention through Optimal Multiple Stopping
and Game-Play4
I Optimal stopping (Dynkin) game:
I Dynamic attacker
I Stop actions of the defender determine
when to take defensive actions
I Goal: find Nash Equilibrium (NE)
strategies and game value
J1(π1,l , π2,l ) = E(π1,l ,π2,l )
" T
X
t=1
γt−1
Rlt
(st, at)
#
B1(π2,l ) = arg max
π1,l ∈Π1
J1(π1,l , π2,l )
B2(π1,l ) = arg min
π2,l ∈Π2
J1(π1,l , π2,l )
(π∗
1,l , π∗
2,l ) ∈ B1(π∗
2,l ) × B2(π∗
1,l ) NE
π̃2,l ∈ B2(π1,l )
π2,l
π1,l
π̃1,l ∈ B1(π2,l )
π̃0
2,l ∈ B2(π0
1,l )
π0
2,l
π0
1,l
π̃0
1,l ∈ B1(π0
2,l )
. . .
π∗
2,l ∈ B2(π∗
1,l )
π∗
1,l ∈ B1(π∗
2,l )
4
Kim Hammar and Rolf Stadler. “Learning Security Strategies through Game Play and Optimal Stopping”. In:
Proceedings of the ML4Cyber workshop, ICML 2022, Baltimore, USA, July 17-23, 2022. PMLR, 2022.

62. 21/28
Structure of Best Response Strategies
b(1)
Defender
0 1

63. 21/28
Structure of Best Response Strategies
b(1)
Defender
0 1
S
(1)
1
α̃1

64. 21/28
Structure of Best Response Strategies
b(1)
Defender
0 1
S
(1)
1
S
(1)
2
α̃1
α̃2

65. 21/28
Structure of Best Response Strategies
b(1)
Defender
0 1
S
(1)
1
S
(1)
2
.
.
.
S
(1)
L
α̃1
α̃2
α̃L . . .

66. 21/28
Structure of Best Response Strategies
b(1)
Defender
0 1
S
(1)
1,π2,l
S
(1)
2,π2,l
.
.
.
S
(1)
L,π2,l
α̃1
α̃2
α̃L . . .
b(1)
Attacker
0 1
S
(2)
1,1,π1,l
S
(2)
1,L,π1,l
β̃1,1
β̃1,L
β̃0,1
. . . β̃0,L
. . .
S
(2)
0,1,π1,l
S
(2)
0,L,π1,l

67. 22/28
Converge Rates and Comparison with State-of-the-art
Algorithms
0 10 20 30 40 50 60
running time (min)
0
2
4
Exploitability
0 10 20 30 40 50 60
running time (min)
0
250
500
750
Approximation error (gap)
55.0 57.5 60.0
7.5
10.0
T-FP NFSP HSVI

68. 23/28
4: Learning in Dynamic IT Environments5
Policy Learning
Agent
Environment
System Identification
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Digital Twin
and Attack
Scenarios
Target
System
Model
M
Traces h1, h2, . . .
Policy π
Configuration I
and change events
Policy π
Policy evaluation &
Data collection
Automated
security policy
5
Kim Hammar and Rolf Stadler. “An Online Framework for Adapting Security Policies in Dynamic IT
Environments”. In: International Conference on Network and Service Management (CNSM 2022). Thessaloniki,
Greece, 2022.

69. 24/28
4: Learning in Dynamic IT Environments6
Algorithm 1: High-level execution of the framework
Input: emulator: method to create digital twin
ϕ: system identification algorithm
φ: policy learning algorithm
1 Algorithm (emulator, ϕ, φ)
2 do in parallel
3 DigitalTwin(emulator)
4 SystemIdProcess(ϕ)
5 LearningProcess(φ)
6 end
1 Procedure DigitalTwin(emulator)
2 Loop
3 π ← ReceiveFromLearningProcess()
4 ht ← CollectTrace(π)
5 SendToSystemIdProcess(ht)
6 UpdateDigitalTwin(emulator)
7 EndLoop
1 Procedure SystemIdProcess(ϕ)
2 Loop
3 h1, h2, . . . ← ReceiveFromDigitalTwin()
4 M ← ϕ(h1, h2, . . .) // estimate model
5 SendToLearningProcess(M)
6 EndLoop
1 Procedure LearningProcess(φ)
2 Loop
3 M ← ReceiveFromSystemIdProcess()
4 π ← φ(M) // learn policy π
5 SendToDigitalTwin(π)
6 EndLoop
6
Kim Hammar and Rolf Stadler. “An Online Framework for Adapting Security Policies in Dynamic IT
Environments”. In: International Conference on Network and Service Management (CNSM 2022). Thessaloniki,
Greece, 2022.

70. 25/28
Learning in Dynamic IT Environments7
200
400
600
#
clients
5000
10000
E[
Ẑ]
0 10 20 30 40 50
execution time (hours)
0
20
Avg
reward
E
h
Ẑt,O|1
i
E
h
Ẑt,O|0
i
E
h
Ẑ
[10]
t,O|1
i
E
h
Ẑ
[10]
t,O|0
i
upper bound Our framework [10]
Results from running our framework for 50 hours in the digital
twin/emulation.
7
Kim Hammar and Rolf Stadler. “An Online Framework for Adapting Security Policies in Dynamic IT
Environments”. In: International Conference on Network and Service Management (CNSM 2022). Thessaloniki,
Greece, 2022.

71. 26/28
Outline
I Use Case & Motivation:
I Use case: Intrusion prevention
I Self-learning security systems: current landscape
I Our Approach
I Network emulation and digital twin
I Stochastic game simulation and reinforcement learning
I Summary of results so far
I Comparison with related work
I Intrusion prevention through optimal multiple stopping
I Dynkin games and learning in dynamic environments
I System for policy validation
I Outlook on future work
I Extend use case
I Rollout-based methods
I Conclusions
I Takeaways

72. 26/28
Outline
I Use Case & Motivation:
I Use case: Intrusion prevention
I Self-learning security systems: current landscape
I Our Approach
I Network emulation and digital twin
I Stochastic game simulation and reinforcement learning
I Summary of results so far
I Comparison with related work
I Intrusion prevention through optimal multiple stopping
I Dynkin games and learning in dynamic environments
I System for policy validation
I Outlook on future work
I Extend use case
I Rollout-based methods
I Conclusions
I Takeaways

73. 27/28
Current and Future Work
Time
st
st+1
st+2
st+3
. . .
rt+1
rt+2
rt+3
rrT
1. Extend use case
I Additional defender actions
I Utilize SDN controller and NFV-based defenses
I Increase observation space and attacker model
I More heterogeneous client population
2. Extend solution framework
I Model-predictive control
I Rollout-based techniques
I Extend system identification algorithm
3. Extend theoretical results
I Exploit symmetries and causal structure
I Utilize theory to improve sample efficiency
I Decompose solution framework hierarchically

74. 28/28
Conclusions
I We develop a method to
automatically learn security strategies.
I We apply the method to an intrusion
prevention use case.
I We design a solution framework guided
by the theory of optimal stopping.
I We present several theoretical results
on the structure of the optimal
solution.
I We show numerical results in a
realistic emulation environment.
s1,1 s1,2 s1,3 . . . s1,n
s2,1 s2,2 s2,3 . . . s2,n
.
.
.
.
.
.
.
.
.
.
.
.
.
.
.
Emulation
Target
System
Model Creation &
System Identification
Strategy Mapping
π
Selective
Replication
Strategy
Implementation π
Simulation &
Learning