Transcript No Slide Title

Intrusion Detection:
New Directions
Teresa Lunt
Xerox Palo Alto Research Center
[email protected]
Detect, isolate, reconfigure, repair
Detection & Response
3. Essential systems
increase their degree
of protection & robustness
2. Intrusion detector alerts
on possible attack
IDS
Emergency Mode
Activator
Sensor
Decoy/
Sensor
Cleanup
Sensor
Sensor
1. Sensors perform
security
monitoring
5. Human-assisted
incident response
restores service
and secure state
Fishbowl
4. Fishbowl created
Critical System
to divert the attacker
and observe the attack
Data Collection
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What level of data to collect
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OS system calls
OS command line
network data (e.g., from router and firewall logs or MIBs)
within applications
keystrokes
all characters transmitted
Tradeoffs in:
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types of intrusions that can be detected
complexity and volume of data
ability to formulate rules that characterize intrusions
ease of playback
ease of damage assessment or evidence gathering
data reliability
degree of privacy invasion
Typical OS Audit Record Fields
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subject
– identifies user, session, and location
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action
– the action attempted
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object
– what the subject acted upon; subfields depend on
type of action
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errorcode
resource-info
– CPU, memory, I/O
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timestamp
State of the Art
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Host-based vs. network-based
– Do not detect attacks that disrupt or manipulate the infrastructure
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Knowledge-based
– Look for patterns associated with known intrusions
– Detect only what you know to look for
– Most systems look for only a dozen or so intrusion types
– Serious foes will use “surprise” attacks we haven’t seen before
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High number of false alarms
– Much flagged activity is of little concern (e.g., password guessing)
– Extremely large numbers of alarms, which must be investigated
manually
– Lack of discrimination between suspicious and normal behaviors
State of the Art cont’d
• Line monitors (eavesdrop on a communications line)
– View is restricted to what passes over a given line
– Too much data must be examined and logged
– Considerably weakened if encryption is used
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Can monitor small numbers of machines/entities
– Audit logs do not scale well
– Monitoring individual users and machines
– No ability for cooperating detectors, which could filter events of
lesser or only local concern
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Lack of robustness
– Cannot deal with missing, incomplete, untimely, or otherwise
faulty data
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Unix-specific
Research Challenges
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Detect a wide variety of intrusion types
Very high certainty
Real-time detection
Develop a network-wide view rather than local views
Analysis must work reliably with incomplete data
Detect unanticipated attack methods
Scale to very large heterogeneous systems
What data to collect for maximal effectiveness; network
instrumentation
Automated response
Discover or narrow down the source of an attack
Integrate with network management and fault diagnosis
Infer intent; forming the big picture
Cooperative problem solving
Methods under Investigation
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Methods to detect highly unusual events or
combinations of events
– Statistical methods
– Neural networks
– Machine learning
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Methods to detect activity outside prescribed
bounds
– Specification-based detection
• New knowledge-based
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Traceback methods
– Thumbprinting
Acceptable
Illegal
Structural
– Graphical intrusion
detection
– State transition models
(model-based detection)
Discrepancy
Statistical
analysis techniques
Model/Pattern
Profile
Match
Cooperating Detectors
IDS
IDS
IDS
IDS
IDS
Sensors
Also needed:
Efficient and effective methods for peer-to-peer cooperative
problem solving to be applied to the detection problem
–To filter events of only local concern
–To assess a larger “region”
Advanced Techniques
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Statistical anomaly detection (SRI, CMU)
– establish a historical behavior profile for each desired entity (e.g.,
user, group, device, process)
– compare current behavior with the profiles
– detects departures from established norms
– continuously update profiles to “learn” changes in subject behavior
– addresses unanticipated intrusion types
• Early statistical studies:
– SRI study (Javitz et al):
• Showed users could be distinguished from each other
based on patterns of use
– Sytek study (Lunt et al):
• Showed behavior characteristics can be found that
discriminate between normal user behavior and
simulated intrusions
Advanced Techniques cont’d
• Machine learning (LANL)
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Builds a massive tree of statistical “rules” (typically 100,000’s of them)
Branches are labeled with conditional probabilities
Prunes the tree to a maximum depth of four to six
Low-occurrence branches are combined
Tree is “trained” from a few days of data
Tree cannot be updated to “learn” as usage patterns change
Activity is considered abnormal if it does not “match” a branch in the
tree or if it matches a branch with low conditional probability last node
• Meta-Learning (Columbia University)
– Meta-learning integrates a number of separately learned classifiers
– Multi-layered approach:
• machine learning and decision procedures detect intrusions locally
• meta-learning and decision procedures to integrate the collective
knowledge acquired by the local agents
Advanced Techniques cont’d
• Computational immunology
– based on biological analogies (e.g., self vs. non-self
discrimination)
– build up a database of observed short sequences of system
calls for a program and detect when the observed program
behavior exhibits short sequences not in that database (U.
of NM)
– allows the detection of tampered or malicious programs or
other suspicious events
– this potentially lightweight method is being implemented in
small, autonomous agents in a CORBA environment (ORA)
Advanced Techniques cont’d
• Model-based detection
– Detects suspicious state transitions (UC Santa Barbara)
• specifies penetration scenarios as a sequence of actions
• keeps track of interesting “state changes”
• attempts to identify attacks in progress before damage is done
– Adapt model-based diagnosis, which has been successful in
diagnosing faults in microprocessors, to intrusion detection (MIT)
• Graphical detection (UC Davis)
– detects intrusions whose activity spans many machines that could
be difficult to detect locally
– specifies intrusion scenarios as graphs of actions covering many
machines
– the graphs provide an intuitive visual display
Advanced Techniques cont’d
• Specification-based detection (UC Davis)
– detects departures from security specifications of
privileged programs
– allows detection of unanticipated attacks
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Thumbprint technique (UC Davis)
– allows limited traceback
– thumbprint is a statistical digest of an interval of a
communications channel
– matching thumbprints can be used to reconstruct
the path of an intruder
Advanced Techniques cont’d
• Signalling Infrastructure Detection (GTE)
– detect anomalous events in a network and signalling
infrastructure typical of telephone service providers
– designed for integration into network operations centers
– uses existing systems/tools for data collection
– uses anomaly detection and specific signalling protocol
“sanity checks”
• Detection in high-speed networks (MCNC)
– Integrates anomaly detection techniques with network
management for ATM networking (IP over ATM)
– Logical analysis of routing protocol operation to detect
anomalous states
Advanced Techniques cont’d
• Automated response (Boeing)
– Integrates firewall, intrusion detection, filtering router, and network
management technologies
– Local intrusion detectors determines threat presence
– Firewalls communicate intrusion detection information to each other
– Firewalls cooperate to locate the intruder
– Network managers automatically reconfigure the network to thwart
the attack
– Firewalls and filtering routers dynamically alter filtering rules to block
the intruder
– Dynamic reconfiguration of logging, monitoring, and access control in
response to detected suspicious activity
– "Fusion" of intrusion-detection data reported by different detectors
– The monitoring is also adapted as part of the response, to help
pinpoint the problem and its source
Advanced Techniques cont’d
• Survivable Active Networks (Bellcore)
– Will allow highly configurable network elements to cooperate
with networked hosts to detect, isolate, and recover quickly and
automatically from damage due to errors or malicious attacks
– "Ablative software" will allow suspect activity to be "peeled off"
the system while continuing to operate in a microenvironment
• Planning and procedural reasoning (SRI)
– Suggest and implement incident recovery procedures
– Uses AI-based automated planning technology for both analysis
and recovery and repair
– Generates explanations to help the sys admin understand what
happened and what to do about it
– Integrate intrusion response tools, to combine the functionality of
many tools that specialize in particular areas of incident
management, into a security anchor desk (USC-ISI)
Open Questions
• Detection performance in realistic settings with
single methods and combinations of methods
• Detection performance with faulty and missing
data
• False positive and false negative rates
• Time to detection
• Scalability
• Dependence on good intruder models
• Distinction from common failure modes
• What data to collect/observe
Common Intrusion
Detection Framework
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E1
E2
Standard Interfaces
– an interconnection framework
for data collection, analysis,
and response components
– extensible architecture
– reuse of core technology
– facilitate tech transfer
– reduce cost
E3
A1
C
A2
D
Reference Architecture
E
A
D
C
Standard API
Event Generator
Event Analyzer
Event Database
System-specific Controller
Strategic Intrusion Assessment
• In a two-week period, AFIWC’s intrusion detectors at 100 AFBs alarmed on 2
million sessions
• After manual review, these were reduced to 12,000 suspicious events
• After further manual review, these were reduced to four actual incidents
National
Reporting Centers
Regional Reporting
Centers (CERTs)
DoD Reporting
Centers
International/Allied
Reporting Centers
Organizational
Security Centers
Local Intrusion
Detectors
•Most alarms are false positives
•Most true positives are trivial incidents
•Of the significant incidents, most are isolated
attacks to be dealt with locally
Correlation
Patterns
Classification
Infer intent
Assess damage
Predict future status
Assess certainty
Strategic Intrusion Assessment
Suppress false alarms
Correlate & infer intent
Plan recognition
• Peer-to-peer cooperation among
detectors to decide what to report to
higher levels.
Detectors must be able to:
• discover each other
• negotiate requirements
• collaborate on diagnosis/response
• Improve individual detectors
• Distinguish what is trivial from
significant
• Distinguish what is locally relevant
– Hypothesize goals for IW
adversaries
– Develop plans for accomplishing
each goal
– automated planning
technology
– Overlay with observed incident
data to discover intent
– plan recognition technology
– Estimate certainty
Security Detection and Response Center
Functions:
• Detection: Analyzes and filters
events reported from lower layers
• for items of interest to this
layer, and
• for reporting to higher layers
• Assessment: to understand
coordinated events
• of interest at this layer, and
• for reporting to higher layers
reporting to higher layers
Assessment
Tracing
Detection
Response
• Tracing (e.g., IDIP, active nets)
• Automated response (e.g., IDIP
for connection closing/filtering)
• Event notification
Significant investment
Early speculative investigations
No research
Notification
reported events
from lower layers
to peers
DARPA/AFRL Evaluations
• Evaluations intended to drive improvements
• Two rounds: one in 1998 (completed) and one in 1999
– results reported at Dec 1998 DARPA PI meeting
– Data sources for 1998 were TCP dump and Unix audit logs
– 1999 evaluation will include NT and other data sources
• Live evaluation on a network at MIT/LL using simulated
data similar to AFB data
– Generated large amounts of realistic background traffic similar to
observed/collected AFB traffic
– Created the largest known collection of automated attacks with
signatures (audit and sniffing)
– Considered both known and new (never seen before) attacks
– Capable of measuring both detection and false alarm rates
• Projects also performed self-evaluations using extensive
training and testing data sets
Live Testbed Configuration for 1999 Evaluation
“INSIDE”
“OUTSIDE”
(172.16 - eyrie.af.mil)
(Many IP Addresses)
PC
Work Station
PC
Work Station
PC
Work Station
OUTSIDE
WS
GATEWAY
INSIDE
GATEWAY
Work Station
OUTSIDE
WEB
GATEWAY
Web Server
Work Station
P2
Work Station
P2
P2
CISCO
ROUTER
Ultra
Ultra
486
Solaris
Sparc
Sparc
Linux
SunOS
486
486
NT
NT
Solaris
Audit Host
DISK
DUMPS
Solaris
Sniffer
AUDIT
DATA
Web Server
SNIFFED
DATA
Web Server
Best combination of research prototypes
ATTACKS DETECTED (%)
100
80
60
BEST
COMBINATION
Keyword
baseline similar
to COTS and
GOTS products
40
20
0
0.001
KEYWORD
BASELINE
0.01
0.1
1
FALSE ALARMS (%)
• Over two orders of magnitude
reduction in false alarms with
improved detection accuracy
10
100
Conclusions
• Currently available technology is not
adequate for the problem
• Promising methods under investigation
show significant improvement over
current technology
• There is still a lot more to be done