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Data Mining Approaches for Intrusion Detection. Wenke Lee and Salvatore J. Stolfo Computer Science Department Columbia University. Overview. Intrusion detection and computer security Current intrusion detection approaches Our proposed approach Data mining - PowerPoint PPT Presentation
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Data Mining Approaches for Intrusion Detection
Wenke Lee and Salvatore J. Stolfo
Computer Science DepartmentColumbia University
Overview• Intrusion detection and computer security• Current intrusion detection approaches• Our proposed approach• Data mining• Classification models for intrusion detection• Mining patterns from audit data• System architecture• Current status• Research plans
Overview• Current intrusion detection approaches and
problems• Our proposed approach• Data mining• Classification models for intrusion detection• Mining patterns from audit data• System architecture• Current status• Research plans
Intrusion Detection and Computer Security
• Computer security goals: confidentiality, integrity, and availability
• Intrusion is a set of actions aimed to compromise these security goals
• Intrusion prevention (authentication, encryption, etc.) alone is not sufficient
• Intrusion detection is needed
Intrusion Detection
• Primary assumption: user and program activities can be monitored and modeled
• Key elements:– Resources to be protected– Models of the “normal” or “legitimate”
behavior on the resources– Efficient methods that compare real-time
activities against the models and report probably “intrusive” activities.
InductiveLearning Engine
Audit Data Preprocessor
Audit Records
Activity Data
Detection Models
Decision Table
(Base) Detection Engine
Rules
Evidence
(Meta) Detection Engine
Evidence from Other Agents
Final Assertion
Decision EngineAction/Report
Learning Agent
Base Detection Agent
Meta Detection Agent
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tcpdumptime dur src dst bytes srv …10:35:41 1.2 A B 42 http …
10:35:41 0.5 C D 22 user …
10:35:41 10.2 E F 1036 ftp …
… … … … … ... …
Connection Records
Profileexecve(“/usr/ucb/finger”, …open(“/dev/zero …mmap(…...
truss
execveopenmmap...
System callSequence
Profile
Learning
Learning
Intrusion Detection
• Two categories of techniques:– Misuse detection: use patterns of well-known
attacks to identify intrusions– Anomaly detection: use deviation from normal
usage patterns to identify intrusions
Current Intrusion Detection Approaches
• Misuse detection:– Record the specific patterns of intrusions– Monitor current audit trails (event sequences)
and pattern matching– Report the matched events as intrusions– Representation models: expert rules, Colored
Petri Net, and state transition diagrams
Current Intrusion Detection Approaches
• Anomaly detection:– Establishing the normal behavior profiles– Observing and comparing current activities
with the (normal) profiles– Reporting significant deviations as intrusions– Statistical measures as behavior profiles:
ordinal and categorical (binary and linear)
Current Intrusion Detection Approaches
• Main problems: manual and ad-hoc– Misuse detection:
• Known intrusion patterns have to be hand-coded• Unable to detect any new intrusions (that have no
matched patterns recorded in the system)– Anomaly detection:
• Selecting the right set of system features to be measured is ad hoc and based on experience
• Unable to capture sequential interrelation between events
Our Proposed Approach
• A systematic framework to:– Build good models:
• select appropriate features of audit data to build intrusion detection models
– Build better models:• architect a hierarchical detector system that combines
multiple detection models– Build updated models:
• dynamically update and deploy new detection system as needed
Our Proposed Approach
• Support for the feature selection and model construction process:– Apply data mining algorithms to find consistent
inter- and intra- audit record (event) patterns– Use the features and time windows in the
discovered patterns to build detection models– A support environment to semi-automate this
process
Our Proposed Approach
• Combining multiple detection models:– Each (base) detector model monitors one aspect
of the system– They can employ different techniques and be
independent of each other– The learned (meta) detector combines evidence
from a number of base detectors
Our Proposed Approach
• An intelligent agent-based architecture:– learning agents: continuously compute (learn)
the detection models– detection agents: use the (updated) models to
detect intrusions
Data Mining
• KDD (Knowledge Discovery in Database):– The process of identifying valid, useful and
understandable patterns in data– Steps: understanding the application domain,
data preparation, data mining, interpretation, and utilizing the discovered knowledge
– Data mining: applying specific algorithms to extract patterns from data
Data Mining
• Relevant data mining algorithms:– Classification: maps a data item into one of
several pre-defined categories– Link analysis: determines relations between
fields in the database– Sequence analysis: models sequence patterns
Data Mining
• Why is it applicable to intrusion detection?– Normal and intrusive activities leave evidence
in audit data– From the data-centric point view, intrusion
detection is a data analysis process– Successful applications in related domains, e.g.,
fraud detection, fault/alarm management
Building Classifiers for Intrusion Detection
• Experiments in constructing classification models for anomaly detection
• Two experiments:– sendmail system call data– network tcpdump data
• Use meta classifier to combine multiple classification models
Classification Models on sendmail
• The data: sequence of system calls made by sendmail.
• Classification models (rules): describe the “normal” patterns of the system call sequences.
• The rule set is the normal profile of sendmail• Detection: calculate the deviation from the profile
– large number/high scores of “violations” to the rules in a new trace suggests an exploit
Classification Models on sendmail
• The sendmail data:– Each trace has two columns: the process ids
and the system call numbers– Normal traces: sendmail and sendmail daemon– Abnormal traces: sunsendmailcap, syslog-
remote, syslog-remote, decode, sm5x and sm56a attacks.
Classification Models on sendmail
• Data preprocessing:– Use sliding window to create sequence of
consecutive system calls– Label the sequences to create training data:
sequences (length 7) class labels
4 2 66 66 4 138 66 “normal”
5 5 5 4 59 105 104 “abnormal”
… …
Classification Models on sendmail
• Experiment 1 - learning patterns of normal sequences:– Each record: n consecutive system calls plus a
class label, “normal” or “abnormal”– Training data: sequences from 80% of the
normal traces plus some of the attack traces– Testing data: traces not used in training– Use RIPPER to learn specific rules for the
minority classes
sendmail Experiment 1
• Examples of output RIPPER rules:– if the 2nd system call is vtimes and the 7th is
vtrace, then the sequence is “normal” – if the 6th system call is lseek and the 7th is
sigvec, then the sequence is “normal”– …– if none of the above, then the sequence is
“abnormal”
sendmail Experiment 1
• Using the learned rules to analyze a new trace:– label all sequences according to the rules– define a region as l consecutive sequences– define a “abnormal” region as having more
“abnormal” sequences than normal ones– calculate the percentage of “abnormal” regions– the trace is “abnormal” if the percentage is above
a threshold
sendmail Experiment 1• Hypothesis: need specific rules of “normal”
sequences to detect “unknown/new” intrusions
• Some results using various normal v.s. abnormal distributions:– Experiment A: 46% normal, length 11– Experiment B: 46% normal, length 7– Experiment C: 54% normal, length 11– Experiment D: 54% normal, length 7
sendmail Experiment 1• All 4 experiments:
– Training data includes sequences from intrusion traces in Bold and Italic, and sequences from 80% of the normal sendmail traces
– Percentage of abnormal “regions” of each trace (showed in the table) is used as the intrusion indicator
– The output rule sets contain ~250 rules, each with 2 or 3 attribute tests. This compares with the total ~1,500 different sequences.
• Experiment A and B generate rules that characterize “normal” sequences of length 11 and 7 respectively
• Experiment C and D generate rules that characterize “abnormal” sequences of length 11 and 7 respectively
sendmail Experiment 1traces Forrest et al. A B C Dsscp-1 5.2 41.9 32.2 40.0 33.1sscp-2 5.2 40.4 30.4 37.6 33.3sscp-3 5.2 40.4 30.4 37.6 33.3syslog-remote-1 5.1 30.8 21.2 30.3 21.9syslog-remote-2 1.7 27.1 15.6 26.8 16.5syslog-local-1 4.0 16.7 11.1 17.0 13.0syslog-local-2 5.3 19.9 15.9 19.8 15.9decode-1 0.3 4.7 2.1 3.1 2.1decode-2 0.3 4.4 2.0 2.5 2.2sm565a 0.6 11.7 8.0 1.1 1.0sm5x 2.7 17.7 6.5 5.0 3.0sendmail 0 1.0 0.1 0.2 0.3
3.4 1.9 0.9 0.7Anomaly detectors A and B performs better then misuse detectors C and D.
Classification Models on sendmail
• Experiment 2 - learning to predict normal system call:– Each record: n-1 consecutive system calls plus
a class label, the nth or the middle system call– Training data: sequences from 80% of the
normal traces (no abnormal traces)– Testing data: traces not used in training– Use RIPPER to learn rules
sendmail Experiment 2
• Examples of output RIPPER rules:– if the 3rd system call is lstat and the 4th is
write, then the 7th is stat – if the 1st system call is sigblock and the 4th is
bind, then the 7th is setsockopt– …– if none of the above, then the 7th is open
sendmail Experiment 2
• Using the learned rules to analyze a new trace:– predict system calls according to the rules– if a rule is violated, the “violation” score is
increased by 100 times the accuracy of the rule– the trace is “abnormal” if the violation score is
above a threshold
sendmail Experiment 2
• Some results:– Experiment A: predict the 11th system call– Experiment B: predict the middle system call in
a sequence of length 7– Experiment C: predict the middle system call in
a sequence of length 11– Experiment D: predict the 7th system call
sendmail Experiment 2
• All 4 experiments:– Training data includes only the sequences from 80% of
the normal sendmail traces– Output rules predict what should be the “normal” nth or
the middle system call– Score of rule “violation” (mismatch) of each trace
(showed in the table) is used as the intrusion indicator– The output rule sets contain ~250 rules, each with 2 or
3 attribute tests. This compares with the total ~1,500 different sequences.
sendmail Experiment 2Traces A B C Dsscp-1 24.1 13.5 14.3 24.7sscp-2 23.5 13.6 13.9 24.4sscp-3 23.5 13.6 13.9 24.4syslog-remote-1 19.3 11.5 13.9 24.0syslog-remote-2 15.9 8.4 10.9 23.0syslog-local-1 13.4 6.1 7.2 19.0syslog-local-2 15.2 8.0 9.0 20.2decode-1 9.4 3.9 2.4 11.3decode-2 9.6 4.2 2.8 11.5sm565a 14.4 8.1 9.4 20.6sm5x 17.2 8.2 10.1 18.0*sendmail 5.7 0.6 1.2 12.6
3.7 3.3 1.2 1.3The 11th (A) and 4th (B) system call are more predictable
Classification Models on sendmail
• Lessons learned:– Normal behavior can be established and used to
detect anomalous usage– Need to collect near “complete” normal data in
order to build the “normal” model– But how do we know when to stop collecting? – Need tools to guide the audit data gathering
process
Classification Models on tcpdump
• The tcpdump data (part of a public data visualization contest):– Packets of incoming, out-going, and internal
broadcast traffic– One trace of normal network traffic– Three traces of network intrusions
Classification Models on tcpdump
• Data preprocessing:– Extract the “connection” level features:
• Record connection attempts• Monitor data packets and count: # of bytes in each
direction, resent rate, hole rate, etc.• Watch how connection is terminated
Classification Models on tcpdump
• Data Preprocessing:– Each record has:
• start time and duration• participating hosts and ports (applications)• statistics (e.g., # of bytes)• flag: “normal” or a connection/termination error• protocol: TCP or UDP
– Divide connections into 3 types: incoming, out-going, and inter-lan
Classification Models on tcpdump
• Building classifier for each type of connections:– Use the destination service (port) as the class
label– Training data: 80% of the normal connections– Testing data: 20% of the normal connections
and connections in the 3 intrusion traces– Apply RIPPER to learn rules
Classification Models on tcpdump• The output RIPPER rules describe the
“normal” characteristics of the destination services. The rule set is the profile of the normal network traffic.
• Using the rules to analyze tcpdump traces:– Examine each connection record according to
the rules– Calculate the percentage of misclassification
(violation of a rule). This percentage is the deviation from the profile.
Classification Models on tcpdump
• Results - misclassification rate on each type of connections:
Connection data Out-going In-coming Inter-lanNormal 3.91% 4.68% 4%Intrusion1 3.81% 6.76% 22.65%Intrusion2 4.76% 7.47% 8.7%Intrusion3 3.71% 13.7% 7.86%
This model is not very effective in detecting intrusions
Classification Models on tcpdump
• Adding temporal features for better models:– Examine all connections in the past n seconds,
and count:• the number of connection errors, all other errors,
connections to system services, user applications, and connection to the same service as the current connection
• average duration and data bytes of all connections; and the same averages of connections to the same service.
Classification Models on tcpdump
• Results of adding the temporal features, the time window is 30 seconds:
Connection data Out-going In-coming Inter-lanNormal 0.88% 0.31% 1.43%Intrusion1 2.54% 27.37% 20.48%Intrusion2 3.04% 27.42% 5.63%Intrusion3 2.32% 42.20% 6.80%
Adding temporal statistical features improves the effectiveness of the detection models
0
0.05
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0.15
0.2
0.25
0.3
0.35
0.4
0.45
0 20 40 60 80 100
time window in seconds
mis
clas
sific
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normalattack1attack2attack3
How do we obtain the optimal time window length?
Effects of time window length on misclassification rate
Classification Models on tcpdump
• Lessons learned:– Data preprocessing requires extensive domain
knowledge– Adding temporal features improves
classification accuracy– Need tools to guide (temporal) feature selection
Building Classifiers for Intrusion Detection
• Meta classifier that combines evidence from multiple detection models:– Build base classifiers that each model one aspect
of the system– The meta learning task:
• each record has a collection of evidence from base classifiers, and a class label “normal” or “abnormal” on the state of the system
– Apply a learning algorithm to produce the meta classifier
Mining Patterns from Audit Data
• Association rules: describe multi-feature (attribute) correlation from a database
• X => Y , confidence, support:– X and Y are subsets of the attribute values in a
record– support is the percentage of records that contain
X and Y– confidence is support(X+Y)/support(X)
Association Rules• Motivations:
– Audit data can be easily formatted into a database table
– Program executions and user activities have frequent correlation among system features
– Incremental updating of the rule set is easy• An example from the .sh_history :
– trn => rec.humor, [0.3, 0.1]– Meaning: 30% of the time when using trn, the
user is reading rec.humor; and reading this newsgroup constitutes 10% of all sh commands
Mining Patterns from Audit Data
• Frequent Episodes: frequent events occurring within a time window
• X => Y, confidence, support, window:– X and Y are subsets of the attribute values in a
record– support is the percentage of (sliding) windows
that contain X and Y– confidence is support(X+Y)/support(X)
Frequent Episodes• Motivation:
– Sequence information needs to be included in a detection model
• An example from a department’s web log:– home, research => theory, [0.2, 0.05], [30]– Meaning: 20% of the time, after home and
research pages are visited (in that order), the theory is then visited within 30 seconds from when home is visited; and visiting these three pages constitutes 5% of all visits to the web site
Using the Mined Patterns
• Guide the audit data gathering process:– Run a program under different settings– For each run, calculate the association rules and
frequent episodes from its audit data– Merge them into an aggregate rule set– Stop gathering audit data when no rules can be
added from a new run
Using the Mined Patterns
• Support the feature selection process:– System features in the association rules and
frequent episodes should be included in the classification models
– Time window and features in the frequent episodes suggest additional temporal features should be considered
Using the Mined Patterns
• Alternatives and complement to classification models:– Examine new audit trace and calculate
“violation” scores: missing rules, new rules, deviations in confidence and support, etc.
– Study the “unique” patterns in the trace of suspected attack to further pin point the cause of the intrusion alarms.
Using the Mined Patterns
• tcpdump data revisited:– How to select the right time window? – Hypothesis: the appropriate window should
contain stable sets of frequent episodes– Experiments: mine frequent episodes using
different window lengths, and count the number of episodes
0
50
100
150
200
250
300
0 50 100 150 200 250
time window in seconds
# of
epi
sode
s
raw episodesepisode rules, conf=0.8episode rules, conf=0.6
The optimal time window length for classification has stable # of episodes
Results on time window length v.s. # of episodes:
Using the Mined Patterns• tcpdump data revisited:
– “unique” patterns in intrusion data may provide some insights
– intrusion 3:• one of the unique frequent episode rules:
– dst_srv=“auth” => flag=“unwanted_syn_ack”, [0.82, 0.1], [30]
• one of the unique association rules:– src_srv=“smtp” => duration=0, flag=“unwanted_syn_ack”,
dst_srv=“user_apps”, [1.0, 0.38]
Architecture Support• Dedicated learning agents are responsible for
building detection models• Base and meta detection agents are equipped
with learned models• Detection agents provide new audit data to the
learning agents• Learning agents dispatch updated models• JAM (Java Agents for Meta-learning) on
fraud detection is the model architecture
InductiveLearning Engine
Audit Data Preprocessor
Audit Records
Activity Data
Detection Models
Decision Table
(Base) Detection Engine
Rules
Evidence
(Meta) Detection Engine
Evidence from Other Agents
Final Assertion
Decision EngineAction/Report
Learning Agent
Base Detection Agent
Meta Detection Agent
Current Status
• Accomplished:– Experiments on sendmail and tcpdump data– Implementation of the association rules and the
frequent episodes algorithms. Testing on medium size data sets (30,000+ records, each with 6+ fields) has been completed.
– Design and 35% of the implementation of a support environment for mining patterns from audit data
– High level design system architecture design
Research Plans
• To be completed within the next year and a half:– Finish the implementation of the support
environment for mining patterns– Experiments on using the algorithms and the
environment to gather audit data and select features
– Experiments on building meta detection models
Research Plans
• To be completed within the next year and a half:– Detailed architecture design– Implementing a prototype intrusion detection
system– Final evaluation using “standard/public” data
sets
Conclusions• We demonstrated the effectiveness of
classification models for intrusion detection• We propose to use systematic data mining
approaches to select the relevant system features to build better detection models
• We propose to use (meta) learning agent-based architecture to combine multiple models, and to continuously update the detection models.