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Data Mining Approaches for ID
• A systematic data mining framework to:
– Build good models:
• select appropriate features of audit data to build
(inductively learned) intrusion detection models
– Build better models:
• architect a hierarchical system that combines
multiple detection models
– Build updated models:
• dynamically update and deploy new detection
system as needed
– Automate the development of IDSs.
Learning
Agent
Base
Detection
Agent
Audit
Records
Inductive
Learning
Engine
Audit Data
Preprocessor
Activity
Data
Detection
Models
Rules
(Base) Detection
Engine
Evidence
Meta
Detection
Agent
Decision
Table
Evidence from
Other Agents
(Meta) Detection Engine
Final Assertion
Decision Engine
Action/Report
Connection
Records
10:35:41.5 128.59.23.34.30 > 113.22.14.65.80 : . 512:1024(512) ack 1 win 9216
10:35:41.5 102.20.57.15.20 > 128.59.12.49.3241: . ack 1073 win 16384
10:35:41.6 128.59.25.14.2623 > 115.35.32.89.21: . ack 2650 win 16225
tcpdump
time
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
…
…
…
…
…
…
...
…
Learning
header,86,2,inetd, …
subject,root,…
text,telnet,...
...
BSM
Network
Model
Session
Records
11:01:35,telnet,-3,0,0,0,...
11:05:20,telnet,0,0,0,6,…
11:07:14,ftp,-1,0,0,0,...
...
Learning
Host
Model
Combined
Model
Data Mining
• Relevant data mining algorithms for ID:
– Classification: maps a data item into one of
several pre-defined categories (e.g., normal or
an intrusion)
• RIPPER: a classification rule learner
– Link analysis: determines relations between
fields in database
• Association Rules
– Sequence analysis: models sequence patterns
• Frequent Episodes
Classifiers as ID Models
• Motivation:
– automatically learn “detection rules” from
audit data, instead of hand-coding.
• Critical requirement:
– select the right set of features (attributes).
• how to automate feature selection?
Classifiers as ID Models (continued)
• RIPPER: computes classification rules that
consist of the most concise and distinguishing
attribute/value tests for each class label.
• Example RIPPER rules:
– pod :- wrong_fragment >= 1, protocol_type = icmp.
– teardrop :- wrong_fragment >= 1.
– smurf :- protocol = ecr_i, short_count >= 3,
srv_short_count >= 3.
– ...
– normal :- true.
Association Rules
• Motivation:
– Program executions and user activities have frequent
correlation among system features
– Incremental updating of the rule set is easy
• An example from “telnet/login” sessions
commands (of a user):
– mail => am, 135.21.59.169 [0.3, 0.1]
– Meaning: 30% of the time when the user is sending
emails, it is in the morning and from the host
135.21.59.169; and this pattern accounts for 10% of all
his/her commands.
Frequent Episodes
• Motivation:
– Sequence information needs to be included in a
detection model
• An example from telnet sessions commands:
– (vi, C, am) => (gcc, C, am) [0.6, 0.2, 5]
– Meaning: 60% of the time, after vi (edits) a C
file, the user gcc (compiles) a C file within the
window of next 5 commands; this pattern
occurs 20% of the time.
Using the Mined Patterns
• Support the feature selection process:
– the “unique” patterns (association rules and
frequent episodes) from intrusion data are used
to construct temporal statistical features for
misuse detection.
– for example, “neptune” patterns (relative to the
same dst_host):
• (service = X, flag = S0), (service = X, flag = S0) ->
(service = X, flag = S0) [0.6, 0.1, 2s]
• add features: for connections to the same dst_host in
the past 2 seconds, the # with the same service, and
the # with S0 flag.
Using the Mined Patterns (cont’d)
• Anomaly detection:
– patterns mined from user commands in “normal”
sessions represent normal behavior.
– aggregate patterns from many sessions to a rule set
to form the normal profile.
– inspecting a session:
• mined patterns from commands of this session
• compare them with the normal profile, if the measured
similarity is below the threshold, then the session is
anomalous.
• similarity = p/n, where n is the total number of patterns
from this session, p is the number of patterns matched in
the profile.
DARPA ID Evaluation
• Our approach:
– Misuse detection:
• Process packet-level tcpdump data into connection records
• Apply association rules and frequent episodes program to
connection records.
• Use the mined patterns to construct additional temporal
statistical features into each record.
• Apply RIPPER to learn detection rules for each intrusion.
– Anomaly detection:
• Process user commands from each session into command
records.
• Mine patterns and construct normal profile.
• Establish the similarity threshold.
DARPA ID Evaluation (cont’d)
• Preprocessing tcpdump data:
– Use Bro (from LBL) as a TCP packet filtering and
reassembling platform.
– Develop a set of Bro “policy scripts” to gather an
extensive set of features for each network
connection:
• “generic” features:
–
–
–
–
protocol (service),
protocol type (tcp, udp, icmp, etc.)
duration of the connection,
flag (connection established and terminated properly, SYN
error, rejected, etc.),
– # of wrong fragments,
– whether the connection is from/to the same ip/port pair.
DARPA ID Evaluation (cont’d)
• “content” features (only useful for TCP connections):
–
–
–
–
–
–
–
–
–
–
–
# of failed logins,
successfully logged in or not,
# of root shell prompts,
“su root” attempted or not,
# of access to security control files,
# of compromised states (e.g., “Jumping to address”, “path not
found” …),
# of write access to files,
# of outbound commands,
# of hot (the sum of all the above “hot” indicators),
is login a “guest” or not,
is login a root or not.
DARPA ID Evaluation (cont’d)
• Features constructed from mined patterns:
• temporal and statistical “traffic” features:
– # of connections to the same destination host as the
current connection in the past 2 seconds, and among these
connections,
– # of rejected connections,
– # of connections with “SYN” errors,
– # of different services,
– rate (%) of connections that have the same service,
– rate (%) of different (unique) services.
DARPA ID Evaluation (cont’d)
• Features constructed from mined patterns:
• temporal and statistical “traffic” features (cont’d):
– # of connections that have the same service as the current
connection, and among these connections,
– # of rejected connections,
– # of connection with “SYN” errors,
– # of different destination hosts,
– rate (%) of the connections that have the same destination
host,
– rate (%) of different (unique) destination hosts.
DARPA ID Evaluation (cont’d)
• Learning RIPPER rules:
– the “content” model for TCP connections:
• each record has the “generic” features + the
“content” features, total 22 features.
• rules to detect many u2r and r2l attacks.
• total 55 rules, each with less than 4 attribute tests.
DARPA ID Evaluation (cont’d)
• example “content” connection records:
dur
p_type proto flag l_in root su compromised hot … label
92
tcp
telnet SF
1
0
0
0
0
… normal
26
tcp
telnet SF
1
1
1
0
2
… normal
2
tcp
http
SF
1
0
0
0
0
… normal
149 tcp
telnet SF
1
1
0
1
3
… buffer
2
http
1
0
0
1
1
… back
tcp
SF
• example rules:
– buffer_overflow :- hot >= 3, compromised >= 1,
su_attempted <= 0, root_shell >= 1.
– back :- compromised >= 1, protocol = http.
DARPA ID Evaluation (cont’d)
• Learning RIPPER rules (cont’d):
– the “traffic” model for all connections:
• each record has the “generic” features + the “traffic”
features, total 20 features.
• rules to detect many DOS and probing attacks.
• total 26 rules, each with less than 4 attribute tests.
DARPA ID Evaluation (cont’d)
• example “traffic” connection records:
dur
p_type proto flag count srv_count r_error diff_srv_rate … label
0
icmp
ecr_i
SF
1
1
0
1
… normal
0
icmp
ecr_i
SF
350
350
0
0
… smurf
0
tcp
other REJ 231
1
198
1
… satan
2
tcp
http
0
0
1
SF
1
normal
• example rules:
– smurf :- protocol = ecr_i, count > =5, srv_count >= 5.
– satan :- r_error >= 3, diff_srv_rate >= 0.8.
DARPA ID Evaluation (cont’d)
• Learning RIPPER rules (cont’d):
– the host-based “traffic” model for all
connections:
• sort connection records by destination hosts and
construct a set of host-based traffic features, similar
to the (time-based) temporal statistical features.
• each record has the “generic” features + the hostbased “traffic” features, total 14 features.
• rules to detect slow probing attacks.
• total 8 rules, each with less than 4 attribute tests.
DARPA ID Evaluation (cont’d)
• example host-based “traffic” connection records:
dur p_type proto flag count srv_count srv_diff_host_rate … label
2
tcp
http
SF
0
0
0
… normal
0
icmp
eco_i SF
1
40
0.5
… ipsweep
0
icmp
ecr_i SF
112
112
0
… normal
• example rules:
– ipsweep :- protocol = eco_i, srv_diff_host_rate > = 0.5,
count <= 2, srv_count >= 6.
DARPA ID Evaluation (cont’d)
• Learning RIPPER rules - a summary:
Models
Attacks
Features
# Features
# Rules
contents
u2r, r2l
generic +
content
22
55
traffic
DOS, probing generic +
traffic
20
26
14
8
host traffic slow probing generic +
host traffic
DARPA ID Evaluation (cont’d)
• Learning RIPPER rules - the main
difficulties:
– Accuracy depends on selecting the right set of
features.
– For example, using only “# of failed logins” to
detect “guessing passwd” may not be adequate:
• guess_passwd :- #_failed_login >= 4.
• this rule has high FP rate since a legitimate user can
have typos when entering passwd.
• need additional features that describe how the
passwd is entered wrong.
DARPA ID Evaluation (cont’d)
• Results:
– Very good detection rate for probing, and
acceptable detection rates for u2r and DOS
attacks
• variations of the attacks are relatively limited.
• training data contains representative instances.
• predictive features are constructed.
– Poor detection rate for r2l attacks
• too many variations
• lack of representative instances in training data
Next Steps
• Improving our approach:
– apply the same methods to DAPAP/MIT LL
BSM data, and see whether a combined model
yields better detection rate.
– develop anomaly detection strategies for
network activities (e.g., to detect “new” r2l
attacks).
– test the efficiency of our models in real-time
systems:
• translate RIPPER rules into NFR N-codes for realtime ID.