Transcript Engineering a Distributed Intrusion Tolerant

Engineering a Distributed
Intrusion Tolerant Database
System Using COTS Components
Peng Liu
University of Maryland Baltimore County
Feb 2001
1
The problem: Database Intrusion Tolerance
• Attacks can succeed -> Intrusions
•Intrusions can seriously impair data
integrity and availability
connect
Authentication
SQL
Commands
Access control
Integrity control
DBMS
Database
2
Technical Objectives
Engineering using COTS components a database system that
can tolerate intrusions
•Practical Database Intrusion Tolerance
–Our approach: using COTS DBMS as main building blocks
•Cost effective Database Intrusion Tolerance
–Our approach: multi-layer defense, cost-effectiveness-performance
analysis
•Comprehensive Database Intrusion Tolerance
–Our approach: transaction-level intrusion detection, isolation &
masking, damage confinement, assessment, and repair
•Adaptive Database Intrusion Tolerance
–Our approach: self-stabilization by adaptation
3
Assumptions & Policies
•What attacks are you considered?
–All and only the attacks through malicious transactions
•What assumptions are you making?
–The proposed ITS facilities are trusted
–The COTS DBMS executes transactions correctly
•What policies can your project enforce?
–The system will continuously execute transactions even in face of attacks
–Damage caused by attacks will be automatically located and repaired
–Located damage will be confined to not further spread
–Suspicious users will be isolated or masked transparently
–The degree of data integrity will be automatically stabilized
–etc.
4
Existing Practice: Database Assurance
•Authentication and access control cannot prevent all attacks
•Integrity constraints are weak at prohibiting plausible but
incorrect data
•Concurrency control and recovery mechanisms cannot
distinguish legitimate transactions from malicious ones
•Automatic replication facilities and active database triggers
can serve to spread the damage
network
5
Expected major achievements
•A cost-effective intrusion tolerant database system prototype
•A family of innovative database intrusion tolerance techniques
–Transaction-level intrusion detection
–Intrusion isolation and masking
–Multi-phase damage confinement
–On the fly damage assessment and repair (implementation)
–Adaptive database intrusion tolerance
–Optimized load balance among ITS facilities
•Identification and study of such ITS properties as adaptability,
stability, and sensitivity
6
Our Approach
7
Transaction-Level vs. OS-Level Intrusion
Tolerance
Transaction-Level
OS-Level
•Good when attacks are via
transactions
•Good when attacks are via direct
OS operations
•Cannot handle OS-level attacks
•Inefficient in handling malicious
transactions
Although both transaction-level and OS-level intrusion
tolerance are necessary, we focus on transaction-level
intrusion tolerance:
–Most database attacks are (by insiders) through transactions
–OS-level techniques can be easily integrated into our framework
8
Scheme 1: preliminary intrusion tolerance
User SQL Commands
Damage Confinement
Mediator
(Policy Enforcement)
Repair SQL Commands
Intrusion detector
Proofs
COTS DBMS
Damage Repairer
Proof collector
Damage Assessor
9
Transaction-Level Intrusion Detection
•Our goal: applying existing
intrusion detection techniques to
identifying malicious transactions
•Key issues:
–semantics-based intrusion
detection
–proof collection
–using the detector as a
protection tool
–coupling OS-level and
transaction-level intrusion
detection
SSN
Start Date Salary
900000001 01/01/97
$58,000
900000001 01/01/98
$60,000
900000001 01/01/99
$62,000
900000001 01/01/00
$82,000
10
Application-Aware Intrusion Detection
•Features:
–application aware
–portable
–real time
–protect the database
from active bad
transactions
–integrate OS-level,
table-level, sessionlevel, and transactionlevel semantics or
statistics
11
Damage Assessment and Repair
(Liu& Ammann &
Jajodia 98,00)
A history
B1
G2
time
The database
G3
B1: read(x,z); write(x)
G2: read(z); write(z)
G3: read(x,y); write(y)
x
y
z
B1
Read-from
G2
G3
A repair
A dependency graph
Undo B1 & G3
Our goal: implementation and evaluation
12
Current Status of Scheme 1
•A prototype of Scheme 1 is implemented except that
–damage confinement is not implemented
–a simulated intrusion detector is used, the real one is under coding
•The prototype has around 20,000 lines of multi-threaded C++
code, running on top of a NT LAN and an Oracle server
•The prototype proxies every SQL command, maintains the status
of every session and every transaction, collects the proofs for
every transaction, raises warnings, rolls back active bad
transactions, locates the damage as a bad transaction is
identified, and repairs the damage, all on-the-fly
•Now the prototype is under testing and evaluation
•We plan to demo this prototype on DISCEX II in June
13
A Limitation of Scheme 1
•The purpose of confinement is to prevent damage from spreading
•The delay of damage assessment can cause ineffective confinement!
User SQL Commands
Damage Confinement
Mediator
(Policy Enforcement)
B1’s write sets
G2’s write sets
Repair SQL Commands
Intrusion detector
B1
Proofs
Proof collector
B1
G4
Damage Repairer
G2
Damage Assessor
The database
14
Scheme 2: multi-phase confinement
User SQL Commands
Damage Confinement
Phase 1
Later
phases
Mediator
(Policy Enforcement)
Repair SQL Commands
Intrusion detector
Proofs
COTS DBMS
Damage Repairer
Proof collector
Damage Assessor
15
Multi-Phase Confinement: An example
To be confined:
all data objects updated
after time 5
except the data objects
updated by G3
User SQL Commands
Damage Confinement
G3’s write set
is clean
Mediator
(Policy Enforcement)
B1
Repair SQL Commands
Intrusion detector
B1
Proofs
Proof collector
Damage Assessor
B1[5]
G4[15]
Damage Repairer
G2[7]
G3[9]
The database
16
Current Status of Scheme 2
17
A Limitation of Scheme 2
•For accuracy, intrusions can be detected with a significant delay
•The delay can cause serious damage when an intrusion is detected
•Quicker detection can decrease the amount of damage, but could mistake
many legitimate transactions for malicious, and cause denial-of-service
An user’s history
Attack enforced
t1
t2
Attack detected
The database
•Our goal: decreasing the amount of damage without losing detection
accuracy and denial-of-service
18
Scheme 3: Isolation
User SQL Commands
Damage Confinement
Suspicious trans.
Mediator
(Policy Enforcement)
Intrusion detector
Main
database
Isolating ... Isolating
engine 1
engine n
Damage
Repairer
read
Damage Assessor
merge
19
Current Status of Scheme 3
•Our preparation
•Our current focus: design and implementation (is challenging!)
20
A Limitation of Scheme 3
•To reduce cost, very few users (i.e., one) can be isolated within a single engine
•However, to avoid causing damage on the main database, the number of
suspicious transactions can be large
•Hence, isolating every suspicious transaction can be too expensive!
•Our solution
•Treating very suspicious and less suspicious users differently
•Isolating very suspicious users
•Masking less suspicious users
•Advantage: better cost-effectiveness
21
Scheme 4: Masking
User SQL Commands
Damage Confinement
Mediator
(Policy Enforcement)
Less suspicious trans.
Very suspicious trans.
Intrusion detector
Damage Assessor
Damage
Repairer
Masking
engine 1
Main
DB
Isolating
engine 1
...
Isolating
engine n
...
Masking
engine n
read
merge
22
Intrusion Masking: An Example
Ui : Ti1
Three less suspicious users:
Main history
Uj : T j 1
Uk : T k 1
Masking history 1
Masking history 2
Advantages:
•Quicker recovery
•Less cost
clean
T[i1]
T[k1]
T[j1]
•If T[i1], T[j1], and T[k1] are all malicious, the main database is valid
•If T[i1] and T[j1] are malicious, but T[k1] is not, then masking engine 2 is valid
•If T[i1] and T[k1] are malicious, but T[j1] is not, then though none is valid, reexecuting T[j1] on the main history can produce the valid database
23
A Limitation of Scheme 4
•Scheme 4 is not adaptive by nature
•Adaptation can give better resilience and cost-effectiveness
•There is no automatic way for the system to adaptively adjust its
defense behavior according to:
•the characteristics of recent and ongoing attacks
•its current performance against these attacks
•Although the SSO can dynamically reconfigure some of its components, manual
reconfiguration operations are ad-hoc, not scalable, and prone to errors
•Our goal: adaptive database intrusion tolerance
24
Scheme 5: Self-Stabilization
•Self-Stabilization: the degree of data integrity should be able to be
automatically stabilized within a tolerable range no matter how the system
is attacked
User SQL Commands
Damage Confinement
Mediator
(Policy Enforcement)
Intrusion
detector
Damage
Assessor
Damage
Repairer
Tolerable
range
State variable feedback
The controller
Main
database
Isolation
engines
Masking
engines
25
The database
Optimized Load Balance
•Observation:
•Different load configurations usually cause different cost-effectiveness
•A load configuration can cause very different cost-effectiveness in
different situations
•An example of load configuration:
•the percentage of isolated users
•the percentage of masked users
•the percentage of malicious users
•the number of masking engines used
•the average interval of state variable feedback
•...
•Our goal: adaptive load configuration optimization
•Mechanism: the controller can be responsible for this job
26
Metrics to measure success
(better cost-effectiveness)
•Cost
–time, space needed for tolerating intrusions
•Effectiveness
–how many intrusions are detected, isolated, or masked
–how many mistakes are made
–how effectively can the damage be confined
–how quick can the damage be assessed and repaired
–how well can the system be adapted
–availability: how often is a legitimate request rejected
–integrity: how well can data integrity be preserved under attacks
•Performance
–system throughput
–response time
27
Task Schedule
Schedule
FY01
FY02
Intrusion Detection
Assessment & Repair
Confinement
Isolation and Masking
Self-Stabilization
Design
Separate Demonstrations
Integrated Demonstrations
28
Technology Transfer
•Technical papers published in leading technical meetings and
technical reports
• Release and dissemination of the prototype in source and
binary forms
•Pursuing technology transition through major commercial
DBMS vendors. The technologies can either be absorbed into
their DBMS kernels, or be commercialized as intrusion
tolerance wrappers
•Starting a company to commercialize the technologies and
provide flexible services to arm customers' database systems
with necessary intrusion tolerance facilities
29
Questions?
Thank you!
30
Multi-layer representation of our approach
31