Outline

1. Automatic Trust ManagementforAdaptive Survivable Systems Howard Shrobe MIT AI LabComputational Vulnerability AnalysisforModel Based DiagnosisJuly 2001 PI Meeting Santa Fe

2. Outline • Overall Framework • Review of Diagnostic Process • Computational Vulnerability Analysis

3. Adaptive Survivable Systems • Techniques that enable self-monitoring and diagnosis • Driven by representations of structure and purpose • The application knows the purposes of its components • The application checks that these are achieved • If these purposes are not achieved, the application localizes and characterize the failure • Techniques that enable application adaptation • The application achieve its purpose as well as possible within the available infrastructure by choosing alternatives. • Driven by models of Trust (informed by diagnosis and monitoring) • Driven by models of computational alternatives • It must have more than one way to effect each critical computation • It should choose an alternative approach if the first one failed • It should make its initial choices in light of the trust model

4. The Active Trust Management Architecture Self Adaptive Survivable Systems Trust Model: Trustworthiness Compromises Attacks Perpetual Analytical Monitoring Rational Decision Making Trend Templates System Models & Domain Architecture Other Information Sources: Intrusion Detectors Rational Resource Allocation

5. Dopey Doc Sleepy Grumpy Motivating Example Performance expectations Grammar Center Grammar Speech Processing text Display Start Voice Capture utterance Gui Directives query Omnibase Display Generator response Integrity Constraint

6. Diagnosis as Likely Mode Identification • Single Level, Single Model Model Based Diagnosis • Tells you which components aren’t working as expected • Multi-Mode Diagnosis • Tells you in what way they aren’t working as expected • Multi-Level, Multi-Mode Diagnosis • Tells you how the misbehaviors are coupled through common-mode failures (or compromises) and ranks the failures by their probabilities. • Attack Models • Tells you how the common mode failures (or compromised modes of the resources) are in turn coupled to common attacks exploiting vulnerabilities of the resources.

7. Model Based Diagnosis with Multiple Faults • Each component is modeled by multi-directional constraints representing the normal behavior • As a value is propagated through a component model, it is labeled with the assumption that this component works • A conflict is detected at any place to which inconsistent values are propagated • A conflict set is the set of all labels attached to the conflicting values • A diagnosis is a set of assumptions which form a covering set of all Conflict set • Goal is to find all minimum diagnoses

8. 15 25 25 20 15 Model Based TroubleshootingGDE Times 3 Plus 40 40 5 Times 5 5 Plus 40 35 Times 3 Conflicts: Blue or Violet Broken Diagnoses: Green Broken, Red with compensating fault Green Broken, Yellow with masking fault

9. Multi-Mode Diagnosis Observed: 5 Predicted: Low = 5 High = 10 B L H P Normal: 2 4 0.9 Fast: -30 1 .04 Slow: 5 30 .06 A OUT1 MID Low = 3 High = 6 L H P Normal: 3 6 .7 Fast: -30 2 .1 Slow: 7 30 .2 0 IN L H P Normal: 5 10 0.8 Fast: -30 4 .03 Slow: 11 30 .07 OUT2 Observed: 17 Predicted: Low = 8 High =16 C Consistent Diagnoses ABC MID MID Prob Explanation Low High NormalNormalSlow 3 3 .04410 C is delayed SlowFastNormal 7 12 .00640 A Slow, B Masks runs negative! FastNormalSlow 1 2 .00630 A Fast, C Slower Normal FastSlow 4 6 .00196 B not too fast, C slow FastSlowSlow -30 0 .00042 A Fast, B Masks, C slow SlowFastFast 13 30 .00024 A Slow, B Masks, C not masking fast

10. Conditional probability = .2 Normal: Delay: 2,4 Delayed: Delay 4,+inf Accelerated: Delay -inf,2 Normal: Probability 90% Parasite: Probability 9% Other: Probability 1% Conditional probability = .4 Conditional probability = .3 Has models Has models Node17 Component 1 Located On Multi-Mode Multi-Tiered Diagnosis • The model is augmented with another level of detail showing the dependence of computations on underlying resources • Each resource has models of its state of compromise • The modes of the resource models are linked to the modes of the computational models by conditional probabilities • The model forms a bayesian network

11. An Example System Description N H Normal .6 .15 Peak .1 .80 Off Peak .3 .05 N H Normal .50 .05 Fast .25 .45 Slow .25 .50 N H Normal .8 .3 Slow .2 .7 A B C N H Normal .50 .05 Fast .25 .45 Slow .25 .50 N H Normal .60 .05 Slow .25 .45 Slower .15 .50 D E Host1 Host2 Host3 Host4 Normal .9 Hacked .1 Normal .85 Hacked .15 Normal .7 Hacked .3 Normal .8 Hacked .2

12. N H Normal .6 .15 Peak .1 .80 Off Peak .3 .05 N H Normal .50 .05 Fast .25 .45 Slow .25 .50 N H Normal .8 .3 Slow .2 .7 A B C N H Normal .50 .05 Fast .25 .45 Slow .25 .50 N H Normal .60 .05 Slow .25 .45 Slower .15 .50 D E Host1 Host2 Host3 Host4 Normal .9 Hacked .1 Normal .85 Hacked .15 Normal .7 Hacked .3 Normal .8 Hacked .2 The System Description includes a Bayesian Network • The Model can be viewed as a Two-Tiered Bayesian Network • Resources with modes • Computations with modes • Conditional probabilities linking the modes

13. N H Normal .6 .15 Peak .1 .80 Off Peak .3 .05 N H Normal .50 .05 Fast .25 .45 Slow .25 .50 N H Normal .8 .3 Slow .2 .7 A B C N H Normal .50 .05 Fast .25 .45 Slow .25 .50 N H Normal .60 .05 Slow .25 .45 Slower .15 .50 D E The system description includes a behavioral model • The Model can also be viewed as a behavioral model with multiple modes per device • Each model has behavioral description • The modes have posterior probabilities linked by conditional probabilities to the probabilities of the modes of the resources

14. N H Normal .6 .15 Peak .1 .80 Off Peak .3 .05 N H Normal .50 .05 Fast .25 .45 Slow .25 .50 N H Normal .8 .3 Slow .2 .7 Discrepancy Observed Here A B C Conflict: A = NORMAL B = NORMAL C = NORMAL N H Normal .50 .05 Fast .25 .45 Slow .25 .50 N H Normal .60 .05 Slow .25 .45 Slower .15 .50 Least Likely Member of Conflict Most Likely Alternative is SLOW D E Integrating model based and Bayesian reasoning • Start with each behavioral model in the “normal” state • Repeat: Check for Consistency of the current model • If inconsistent, • Add a new node to the Bayesian network • This node represents the logical-and of the nodes in the conflict. • It’s truth-value is pinned at FALSE. • Prune out all possible solutions which are a super-set of the conflict set. • Pick another set of models from the remaining solutions • If consistent, add to the set of possible diagnoses • Continue until all inconsistent sets of models are found • Solve the Bayesian network

15. Adding Attack Models • An Attack Model specifies the set of attacks that are believed to be possible in the environment • Each resource has a set of vulnerabilities • Vulnerabilities enable attacks on that resource • A successful attack exploits the vulnerability, putting the resource into a non-normal behavioral mode • This is given as a set of conditional probabilities • If the attack succeeded on a resource of this type then the likelihood that the resource is in mode-x is P • This now forms a three tiered Bayesian network Has- vulerability Enables Host1 Buffer-Overflow Overflow-Attack Resource-type Normal .5 Causes Unix-Family .7 Slow

16. Three Tiered Model

17. What the diagnostic process tells us • All non-conflicting combination of models are possible diagnoses • The posterior probabilities tell you how likely each diagnosis is. • This guides recovery processing • Each mode of each resource has a posterior probability • This guides resource selection in the future • The attack models couple the resource models, given a system wide view. • This informs the trust model • This couples to long-term monitoring, that looks for complex multi-stage attacks

18. Computational Vulnerability Analysis • Grounding the attack model in systematic analysis • Ontology of: • System Properties • System Types • System Structure • Control and Dependencies

19. Generating Attack ModelsThrough Vulnerability Analysis • The problem: Where does the attack model and its links to behavioral modes come from? • So far, by hand crafting • Vulnerability Analysis supplants this by a systematic analysis: • Forming an ontology of how computer systems are structured • Building models of the environment • Network topology: nodes, routers, switches, filter, firewalls • System types: hardware, operating systems • Server and user suites: Which servers and users run where • Analyzing how properties depend on resources • Analyzing the vulnerabilities of the resources

20. Modeling System Structure File System files Part-of resources Operating System Access Controller Hardware controls Logon Controller User Set Processor Part-of Part-of Input-to Job Admitter Memory Device Controllers controls Scheduler Work Load controls Input-to Devices Device Drivers Scheduler Policy Resides-In controls

21. Modeling the topology Machine name: sleepy OS Type: Windows-NT Server Suite: IIS….. User Authentication Pool: Dwarfs… Switch: subnet restrictions. …. Switch: subnet restrictions. …. Router: Enclave restrictions. …. Topology tells you: who can share (and sniff) which packets who can affect what types of connections to whom

22. Modeling Dependencies • Start with the desirable properties of systems: • Reliable performance • Privacy of communications • Integrity and/or privacy of data • Analyze which system components impact those properties • Performance - scheduler • Privacy - access-controller • To affect a desirable property control a component that contributes to the delivery of that property

23. Controlling components (1) • One way to gain control of a component is to directly exploit a known vulnerability • One way to control a Microsoft IIS web server is to use a buffer overflow attack on it. IIS Web Server Process IIS Web Server Is vulnerable to Takes control of Buffer-Overflow Attack Buffer-Overflow Attack

24. Scheduler Policy Parameters Modification- action Controlling components (2) • Another way to control a component is to find an input to the component and then find a way to modify the input • Modify the scheduler policy parameters Scheduler Scheduler Input to control by Scheduler Policy Parameters

25. Control- action User Job Admitter Controlling components (3) • Another way to control a component is to find one of its components and then to find a way to gain control the sub-component Job-Admitter Job-Admitter Component-of control by User Job Admitter

26. Modifying Inputs (1) • One way to modify an input is to find a component which controls the input and then to find a way to gain control component Scheduler Scheduler Input-of control by Workload Controls Job Admitter Workload Controls Controls Job Admitter Attack.

27. Modifying Inputs (2) • One way to modify an input is to find a component of the input and then to find a way to modify the component Scheduler Scheduler Input-of control by Workload User Workload Component Workload Component User Workload Modify Attack.

28. Access Rights • Each object specifies a set of capabilities required for each operation on that object • Capabilities are organized in an DAG • This generalizes the access mechanisms of all OS’s. • Each actor (user or process) possesses certain capabilities. • An actor can perform an action on an object only if it possesses a capability at least as strong as that required for the operation • This is a generalization of the access mechanisms in all current OS’s. • An access pool is a set of machines that shares resources, password & access right descriptions

29. Doc Life The AI Lab Topology (partial) Router Access pool Netchex Router Netchex Filters out Telnet. Server Switch 8th-Floor-1 8th-Floor-2 7th-Floor-1 Sakharov Wilson Lisp Access Pool Dwarf Access Pool Server Access Pool Truman Dopey Kenmore Sleepy Maytag Quincy-Adams General Access Pool Sneezy Creepy Crawler Jefferson

30. Control- action Typical User Process Obtaining Access (1) • One way to gain access to an operation on an object is to find a process with an adequate capability and take control of the process Typical User File Typical User File Required for Read To Read User Read Typical User Process Posseses Capability User Read

31. Logon as Typical User User Process Obtaining Access (2) • Another way to gain access to an operation on an object is to find a user with an adequate capability and find a way to log in as that user and launch a process with the user’s capabilities Typical User File Typical User File Required for Read To Read User Read Typical User Posseses Capability Launches User Read

32. Logging On • Logging on requires obtaining knowledge of a password • To gain knowledge of a password • Guess it, using guessing attacks • Sniff it • By placing a parasitic virus on the user’s machine • By monitoring network traffic • Hack the password file

33. Monitoring and Changing Network Traffic • Network are broken down into subnet segments • Segments are connected by Routers • Routers can monitor traffic on any connected segment • Each segment may be: • Shared media • Coaxial ethernet • Wireless ethernet • Any connected computer can monitor traffic • Switched media • 10 (100, 100) base-T • Only the switch (or reflected ports) can monitor Traffic • Switches and Routers are computers • They can be controlled • But they may be members of special access pools • To gain knowledge of some information gain the ability to monitor network traffic

34. Residences • Components reside in several places • Main memory • Boot files • Paging Files • They migrate between residences • Through local peripheral controllers • Through networks • To modify/observe a component find a residence of the component and modify/observe it in the residence • To modify/observe a component find a migration path and modify/observe it during the transmission

35. Formats and Transformations • Components live in several different formats • Source code • Compiled binary code • Linked executable images • Processes transform one format into another • Compilation • Linking • To modify a component change an upstream format and cause the transformations to happen • To modify a component gain control of the processes that perform the transformations

36. Modification during Transmission • To control traffic on a network segment launch a “man in the middle attack” • Get control of a machine, redirect traffic to it • To observe network traffic get control of a switch/router and a user machine and then reflect traffic to the user machine • To modify network traffic launch an “inserted packet” packet. • Get control of a machine • Send a packet from the controlled machine with the correct serial number but wrong data before the sender sends the real packet

37. An Example • Affecting reliable performance: • Control the scheduler - • The scheduler is a component that impacts performance • By modifying the scheduler’s policy parameters • The policy parameters are inputs to the scheduler • By gaining root access • The policy parameters require root access for writing • By using a buffer overflow attack on the web-server • The web-server process possesses root capabilities • The web-server process is vulnerable to a buffer-overflow attack. • For this attack to impact the performance all the actions must succeed • Each has an a priori probability based on its inherent difficulty and current evidence suggesting that it occurred.

38. Affecting Data Privacy (1)

39. Affecting Data Privacy (2)

40. Affecting Data Privacy (3)

41. Affecting Performance (1)

42. Affecting Performance (2)

43. Using Attack Scenarios • This information is captured in an Object-Oriented knowledge representation and rule-base system that reasons with it. • The inference process develops multi-stage attack scenarios • The scenarios are transformed into trend templates for recognition purpose • The scenarios are transformed into Bayesian network fragment for diagnostic purposes

44. Integration Opportunities • Projects that provide self-monitoring capabilities • We depend on self-monitoring • We typical assume coarse-grain (e.g. method wrapping) • Could use lower-level tools as well • Projects that provide policy enforcement • Attempted violations of policies should trigger diagnostic activity • Projects that provide recovery capabilities • Participation in framework development