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José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern Recognition Motivation and Introduction Sequence classification Our approach L ibrary Creation Classification Target Environment Description Experiments and Results Conclusions and Future Works 1 Outline
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José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Sequence Classification Using Statistical Pattern Recognition
José Antonio Iglesias, Agapito Ledezma, and Araceli Sanchis
Computer Science Department Universidad Carlos III de Madrid
Avda. de la Universidad, 30. 28911 Leganés, Spain{jiglesia, ledezma, masm}@inf.uc3m.es
.
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Motivation and Introduction Sequence classification Our approach
Library Creation Classification
Target Environment Description Experiments and Results
Conclusions and Future Works
OutlineOutline
.
1
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Motivation and IntroductionMotivation and Introduction Sequence classification Our approach
Library Creation Classification
Target Environment Description Experiments and Results
Conclusions and Future Works
1
OutlineOutline
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Opponent behavior Modelling / Classification(Environment: soccer simulation domain)
MotivationMotivation
.
2
Opponent Modeling
Pattern Recognition
Off-Line Analysis
No-Pattern LogFile
Pattern LogFile
Base Estrategy
Pattern
Recognized Patterns
On-Line Comparing Method
Pattern Detection
On-Line Detection
Environment Information Advices to
Players
RoboCup Soccer Server
Pattern Recognized
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Behavior ClassificationBehavior Classification
Behavior as sequence of elements
Sequence ClassificationSequence Classification
IntroductionIntroduction
.
3
SequenceSequence::“set of elements ordered so that they can be labelled with the positive integers” (Merriam-Webster Dictionary)
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Motivation and Introduction Sequence classificationSequence classification Our approach
Library Creation Classification
Target Environment Description Experiments & Results
Conclusions and Future Works
4
OutlineOutline
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
• Given:
Classes = {cClasses = {c11, c, c22, … c, … cnn}}
Sequence E = {eSequence E = {e11, e, e22, … e, … enn}}
• Determine: Which class ci Є C does the sequence E belong to.
Sequence classificationSequence classification
5
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Motivation and Introduction Sequence classification Our approachOur approach
Library Creation Classification
Target Environment Description Experiments & Results
Conclusions and Future Works
.
6
OutlineOutline
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Our approachOur approachpwdfsfg…
vimanls…
…fingermorels...
Sequence 1 Class 1
Sequence 2 Class 2
Sequence n Class n
Pattern 1 Pattern 2 Pattern 3
…
Pattern Library
Library Creation Classification
vimorels…
Pattern to classify
Sequence to classify
Compare_Patterns
Compare_Patterns
Compare_Patterns
…
On-Line Sequence
Classification
SEQUENCE CLASS
Classification Result
.
7
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Motivation and Introduction Sequence classification Our approachOur approach
Library CreationLibrary Creation Classification
Target Environment Description Experiments & Results
Conclusions and Future Works
8
OutlineOutline
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library CreationLibrary Creation
.
TrieTrie (re (retrietrieval)val) data structure data structure::
Special search tree used for storing elements and its prefixes.Special search tree used for storing elements and its prefixes.
Every node: Every node: – represents an element– stores useful information (times appeared,…)
9
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library Creation - Library Creation - An example trieAn example trie
Sequence to insert initially in the trie: {pwd vi pwd vi pwd ls}
pwdvipwdvipwdls
Sequence
10
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library Creation - Library Creation - An example trieAn example trie
Sequence to insert initially in the trie: {pwd vi pwd vi pwd ls}
Sub-sequence length: 3Sub-sequence length: 3 {pwd vi pwd vi pwd ls}
Sub-sequences to insert in the trie: {pwd vi pwd} and {vi pwd ls}
pwdvipwdvipwdls
Sequence
10
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library CreationLibrary Creation - - An example trieAn example trie
Sub-sequences to insert in the trie: {pwd pwd vi vi pwd pwd} and {vi pwd ls}
Root
11
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library CreationLibrary Creation - - An example trieAn example trie
Sub-sequences to insert in the trie: {pwd pwd vi vi pwd pwd} and {vi pwd ls}
Root
pwd [1]
vi [1]
pwd [1]
11
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library CreationLibrary Creation - - An example trieAn example trie
Sub-sequences to insert in the trie: {pwd vi vi pwd pwd} and {vi pwd ls}
Root
pwd [1]
vi [1]
pwd [1]
vi [1]
pwd [1]
11
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library CreationLibrary Creation - - An example trieAn example trie
Sub-sequences to insert in the trie: {pwd vi vi pwdpwd} and {vi pwd ls}
Root
pwd [2]
vi [1]
pwd [1]
vi [1]
pwd [1]
11
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library Creation - Library Creation - An example trieAn example trie
Sub-sequences to insert in the trie: {pwd vi vi pwdpwd} and {vi pwd ls}
Root
pwd [2]
vi [1]
pwd [1]
vi [2]
pwd [2]
ls [1]
11
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library CreationLibrary Creation - - An example trieAn example trie
Sub-sequences to insert in the trie: {pwd vi vi pwdpwd} and {vi pwd ls}
Root
pwd [3]
vi [1]
pwd [1]
vi [2]
pwd [2]
ls [1]
ls [1]
11
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library Creation - Library Creation - An example trieAn example trie
Sub-sequences to insert in the trie: {pwd vi vi pwdpwd} and {vi pwd ls}
Root
pwd [3]
vi [1]
pwd [1]
vi [2]
pwd [2]
ls [1]
ls [1]
ls [1]
11
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library Creation - Library Creation - An example trieAn example trie
{pwd vi vi pwd pwd vi pwd ls}
Root
pwd [3]
vi [1]
pwd [1]
vi [2]
pwd [2]
ls [1]
ls [1]
ls [1]
11
pwdvipwdvipwdls
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Evaluate the relation/dependence between an element and its prefix
Two approaches:– Frequency-based method. Statistical dependence method.
Our approach: Statistical Value used: Chi-square value.This value is stored in every node of the trie
Library Creation - Library Creation - Evaluating DependencesEvaluating Dependences
12
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Event Different event Total
Prefix O11 O12 O11 + O12
Different Prefix
O21 O22 O21 + O22
Total O11+ O21 O12+ O22
O11 + O12+O21 + O22
O11: How many times the current node/element is followed by its prefix.O12: How many times the current node/element is followed by a different prefix.O21: How many times a different prefix (of the same length) is followed by the same node.O22: How many times a different prefix (of the same length) is followed by a different node.
Expected (Eij)= (Rowi Total x Columnj Total)
Grand Total
X2 = ∑ ∑(Oij - Eij ) 2
Eiji=1
r k
2 x 2 Contingency Table
Library Creation - Library Creation - Evaluating DependencesEvaluating Dependences
j=1
.
13
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Library Creation - Library Creation - Evaluating DependencesEvaluating Dependences
.
pwd [3]
vi [1] [5.1][5.1]
pwd [1] [4.3][4.3]
vi [2]
pwd [2] [3.5][3.5]
ls [1] [4.3][4.3]
ls [1] [4.3][4.3]
ls [2]
Sequence Pattern Trie
Root
A Sequence Pattern Trie is created for each class.
14
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Motivation and Introduction Sequence classification Our approachOur approach
Library Creation ClassificationClassification
Target Environment Description Experiments & Results
Conclusions and Future Works
15
OutlineOutline
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
ClassificationClassificationpwdfsfg…
vimanls…
…fingermorels...
Sequence 1 Class 1
Sequence 2 Class 2
Sequence n Class n
Pattern 2 Pattern 3
…
Pattern Library
Classification
vimorels…
Sequence to classify
Compare_Patterns
Compare_Patterns
…
On-Line Sequence
Classification
ONLINE SEQUENCE
CLASS
.
Library Creation
Pattern to classify
TestingTesting TrieTrie
Pattern 1
Compare_PatternsClassClass TrieTrie
16
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
.
17
pwd [3]
vi [1] [5.1][5.1]
who [1] [4.3][4.3]
vi [2]
who [2] [3.5][3.5]
Root
Classification – Comparing ProcessClassification – Comparing ProcessClass Trie Testing Trie
pwd [3]
vi [1] [7.1][7.1]
pwd [1] [[7.3]7.3]
vi [2]
pwd [2] [1.5][1.5]
ls [1] [0.3][0.3]
ls [2]
Root
…
If the node (and its prefix) are in both Tries:
If ( abs(chi2TestingTrie – chi2
ClassTrie) ≤ ThresholdValue ):
SimilaritySimilarity between both tries.
Result [ElementTestingTrie, PrefixTestingTrie, ChiChi22TestingTrieTestingTrie]
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
.
17
pwd [3]
vi [1] [5.1][5.1]
who [1] [4.3][4.3]
vi [2]
who [2] [3.5][3.5]
Root
Classification – Comparing ProcessClassification – Comparing ProcessClass Trie Testing Trie
pwd [3]
vi [1] [7.1][7.1]
pwd [1] [[7.3]7.3]
vi [2]
pwd [2] [1.5][1.5]
ls [1] [0.3][0.3]
ls [2]
Root
…
If the node (and its prefix) are in both Tries: If (abs(5.1 – 7.1) ≤ ThresholdValue ):
SimilaritySimilarity between both tries.
Result [vi , pwd, 5.15.1]
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
.
17
pwd [3]
vi [1] [5.1][5.1]
who [1] [4.3][4.3]
vi [2]
who [2] [3.5][3.5]
Root
Classification – Comparing ProcessClassification – Comparing ProcessClass Trie Testing Trie
pwd [3]
vi [1] [7.1][7.1]
pwd [1] [[7.3]7.3]
vi [2]
pwd [2] [1.5][1.5]
ls [1] [0.3][0.3]
ls [2]
Root
…
If the node (and its prefix) are only in the Testing Trie:
DifferenceDifference between both tries.
Result Result [Element [ElementTestingTrieTestingTrie, Prefix, PrefixTestingTrieTestingTrie, , (Chi(Chi22TestingTrieTestingTrie * -1) * -1)]]
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
.
17
pwd [3]
vi [1] [5.1][5.1]
who [1] [4.3][4.3]
vi [2]
who [2] [3.5][3.5]
Root
Classification – Comparing ProcessClassification – Comparing ProcessClass Trie Testing Trie
pwd [3]
vi [1] [7.1][7.1]
pwd [1] [[7.3]7.3]
vi [2]
pwd [2] [1.5][1.5]
ls [1] [0.3][0.3]
ls [2]
Root
…
If the node (and its prefix) are only in the Testing Trie:
DifferenceDifference between both tries.
Result Result [who, pwd [who, pwd vi, vi, (-4.3)(-4.3)]]
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
.
17
pwd [3]
vi [1] [5.1][5.1]
who [1] [4.3][4.3]
vi [2]
who [2] [3.5][3.5]
Root
Classification – Comparing ProcessClassification – Comparing ProcessClass Trie Testing Trie
pwd [3]
vi [1] [7.1][7.1]
pwd [1] [[7.3]7.3]
vi [2]
pwd [2] [1.5][1.5]
ls [1] [0.3][0.3]
ls [2]
Root
…
If the node (and its prefix) are only in the Testing Trie:
DifferenceDifference between both tries.
Result Result [who, vi, [who, vi, (-3.5)(-3.5)]]
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
ResultResult:[Element1, Prefix1, ValueValue11]
[Element2, Prefix2, ValueValue22]
[Element3, Prefix3, ValueValue33]
[Element4, Prefix4, ValueValue44]
…[Elementn, Prefixn, ValueValuenn]
Each comparison (ClassTrie, TestingTrie):
A comparision value
.
Comparison Value
18
Classification – Comparing ProcessClassification – Comparing Process
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
ResultResult:[vi, pwd, + 5.1+ 5.1][who, pwd vi, - 4.3- 4.3][who, pwd, - 3.5- 3.5]
.
- 2.7
18
Classification – Comparing ProcessClassification – Comparing Process
Comparison Value
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
ClassificationClassificationpwdfsfg…
vimanls…
…fingermorels...
Sequence 1 Class 1
Sequence 2 Class 2
Sequence n Class n
Pattern 1 Pattern 2 Pattern 3
…
Pattern Library
Library Creation Classification
vimorels…
Pattern to classify
Sequence to classify
…
ONLINE SEQUENCE
CLASS
On-Line Sequence
Classification
Compare_Patterns
Compare_Patterns
Compare_Patterns
comparision value
comparision value
comparision value
.
19
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
comparision value
comparision value
comparision value
ClassificationClassificationpwdfsfg…
vimanls…
…fingermorels...
Sequence 1 Class 1
Sequence 2 Class 2
Sequence n Class n
Pattern 1 Pattern 2 Pattern 3
…
Pattern Library
Library Creation Classification
vimorels…
Pattern to classify
Sequence to classify
Compare_Patterns
Compare_Patterns
Compare_Patterns
…
ONLINE SEQUENCE
CLASS
On-Line Sequence
Classification
Greatest Comparison
Value
.
20
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Motivation and Introduction Sequence classification Our approachOur approach
Library Creation Classification
Target EnvironmentTarget Environment DescriptionDescription Experiments & Results
Conclusions and Future Works
21
OutlineOutline
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Environment –Environment – UNIX command line sequences UNIX command line sequences
# Start session 1# Start session 1 cd ~/private/docs ls -laF | more cat foo.txt bar.txt zorch.txt > a.txt exit# End session 1# End session 1
# Start session 2 cd ~/games/ xquake & fg …
**SOF****SOF**cd<1>ls-laF|morecat<3>><1>exit**EOF****EOF**…
one "file name" argument
three "file name" arguments
one "file name" argument
Command histories of 9 UNIX computer usersUNIX computer users at over 2 yearsUCI Repository of ML Database [Newman C., Hettich S., Merz, C. (1998)]
22
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Motivation and Introduction Sequence classification Our approachOur approach
Library Creation Classification
Target EnvironmentTarget Environment Description Experiments & ResultsExperiments & Results
Conclusions and Future Works
23
OutlineOutline
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
9 files (users) containing from about 10.000 to 60.000 commands each. 1. 1. Extracting Patterns:Extracting Patterns: A trie is created for each user Pattern Library
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences
.
24
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
9 files (users) containing from about 10.000 to 60.000 commands each. 1. 1. Extracting Patterns:Extracting Patterns: A trie is created for each user Pattern Library
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences
.
24
2. 2. Classification Algorithm:Classification Algorithm: Sequence to classify (sequences of very different sizes) Classified in the class with the greatest value (result value).
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
9 files (users) containing from about 10.000 to 60.000 commands each. 1. 1. Extracting Patterns:Extracting Patterns: A trie is created for each user Pattern Library
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences
.
24
2. 2. Classification Algorithm:Classification Algorithm: Sequence to classify (sequences of very different sizes) Classified in the class with the greatest value (result value).
3. 3. Evaluating the result:Evaluating the result: Calculate:
difference between the greatest value and the second greatest value (+)(+)x difference between the real classification value and the greatest value (-)(-)
(The greater the difference, the better the classification)
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Results Results – – UNIX command line sequencesUNIX command line sequences
Unix Commands Classification – User 6
.
average of 25 simulation results
25
Cla
ssifi
catio
n Va
lue
Length of the Sequence to classify
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Results Results – – UNIX command line sequencesUNIX command line sequences
Minimum length for classifying a UNIX Computer User correctly
.
26
Unix Computer User (Class)
Leng
th o
f the
Seq
uenc
e to
cla
ssify
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Motivation and Introduction Sequence classification Our approachOur approach
Library Creation Classification
Target Environment Description Experiments & Results
Conclusions and Future WorksConclusions and Future Works
27
OutlineOutline
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
A threshold must be found
Long time for creating the tries
Results depend on the length of the sub-sequences used to create the trie
ConclusionsConclusions
28
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Effective method to classify UNIX users
If a behavior can be represented by sequences, the proposed classification method can be used
If a new class is added, only its trie must be created (the others are not modified)
This method could be used for other tasks: sequence prediction, sequence clustering…RoboCup Coach 2006 Competition (succesfully results)
ConclusionsConclusions
29
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Pattern Library One Trie for all classes (users).
Classification method without threshold value
Analysis comparing our approach to others (HMMs)
Future WorksFuture Works
30
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Sequence Classification Using Statistical Pattern Recognition
José Antonio Iglesias, Agapito Ledezma, and Araceli Sanchis
Computer Science Department Universidad Carlos III de Madrid
Avda. de la Universidad, 30. 28911 Leganés, Spain{jiglesia, ledezma, masm}@inf.uc3m.es
.
Thank you!Thank you!
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Sequence Classification Using Statistical Pattern Recognition
José Antonio Iglesias, Agapito Ledezma, and Araceli Sanchis
Computer Science Department Universidad Carlos III de Madrid
Avda. de la Universidad, 30. 28911 Leganés, Spain{jiglesia, ledezma, masm}@inf.uc3m.es
.
QuestionsQuestions
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Sequence Classification Using Statistical Pattern Recognition
José Antonio Iglesias, Agapito Ledezma, and Araceli Sanchis
Computer Science Department Universidad Carlos III de Madrid
Avda. de la Universidad, 30. 28911 Leganés, Spain{ jiglesia, ledezma, masm}@inf.uc3m.es
.
Related to Questions...Related to Questions...
29
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences**SOF****SOF**cd<1>ls-laF|morecat<3>>……
Pattern/Class User0
**SOF****SOF**ls<1>exit<1>ls-laFxquake&fg……
**SOF****SOF**vi<1>vi<3>ls-lacat<2>……
USER 0Class0
USER 1Class1
USER 8Class8
…
…
Pattern Library
**SOF****SOF**ls-laF|Morecd<4>
…Test User
Sequence ClassificationPattern/Class
User1Pattern/Class
User8
User On-Line Є Class c
User On-Line vs Class User0 21User On-Line vs Class User1 49User On-Line vs Class User2 9User On-Line vs Class User3 3User On-Line vs Class User4 12User On-Line vs Class User5 29User On-Line vs Class User6 -1User On-Line vs Class User7 0User On-Line vs Class User8 11
ClassUser1
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences**SOF****SOF**cd<1>ls-laF|morecat<3>>……
Pattern/Class User0
**SOF****SOF**ls<1>exit<1>ls-laFxquake&fg……
**SOF****SOF**vi<1>vi<3>ls-lacat<2>……
USER 0Class0
USER 1Class1
USER 8Class8
…
…
Pattern Library
**SOF****SOF**ls-laF|Morecd<4>
…Test User
Sequence ClassificationPattern/Class
User1Pattern/Class
User8
User On-Line Є Class c
User On-Line vs Class User0 21User On-Line vs Class User1 User On-Line vs Class User1 49 49User On-Line vs Class User2 9User On-Line vs Class User3 3User On-Line vs Class User4 12User On-Line vs Class User5 29User On-Line vs Class User6 -1User On-Line vs Class User7 0User On-Line vs Class User8 11
ClassUser1
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences**SOF****SOF**cd<1>ls-laF|morecat<3>>……
Pattern/Class User0
**SOF****SOF**ls<1>exit<1>ls-laFxquake&fg……
**SOF****SOF**vi<1>vi<3>ls-lacat<2>……
USER 0Class0
USER 1Class1
USER 8Class8
…
…
Pattern Library
**SOF****SOF**ls-laF|Morecd<4>
…Test User
Sequence ClassificationPattern/Class
User1Pattern/Class
User8
User On-Line Є Class c
User On-Line vs Class User0 21User On-Line vs Class User1 User On-Line vs Class User1 49 49User On-Line vs Class User2 9User On-Line vs Class User3 3User On-Line vs Class User4 12User On-Line vs Class User5 29User On-Line vs Class User6 -1User On-Line vs Class User7 0User On-Line vs Class User8 11
ClassUser1
Correctly ClassifiedCorrectly Classified
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences**SOF****SOF**cd<1>ls-laF|morecat<3>>……
Pattern/Class User0
**SOF****SOF**ls<1>exit<1>ls-laFxquake&fg……
**SOF****SOF**vi<1>vi<3>ls-lacat<2>……
USER 0Class0
USER 1Class1
USER 8Class8
…
…
Pattern Library
**SOF****SOF**ls-laF|Morecd<4>
…Test User
Sequence ClassificationPattern/Class
User1Pattern/Class
User8
User On-Line Є Class c
User On-Line vs Class User0 21User On-Line vs Class User1 User On-Line vs Class User1 49 49User On-Line vs Class User2 9User On-Line vs Class User3 3User On-Line vs Class User4 12User On-Line vs Class User5 29User On-Line vs Class User6 -1User On-Line vs Class User7 0User On-Line vs Class User8 11
ClassUser1
Correctly ClassifiedCorrectly Classified
20
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences**SOF****SOF**cd<1>ls-laF|morecat<3>>……
Pattern/Class User0
**SOF****SOF**ls<1>exit<1>ls-laFxquake&fg……
**SOF****SOF**vi<1>vi<3>ls-lacat<2>……
USER 0Class0
USER 1Class1
USER 8Class8
…
…
Pattern Library
**SOF****SOF**ls-laF|Morecd<4>
…Test User
Sequence ClassificationPattern/Class
User1Pattern/Class
User8
User On-Line Є Class c
User On-Line vs Class User0 21User On-Line vs Class User1 49User On-Line vs Class User2 9User On-Line vs Class User3 3User On-Line vs Class User4 12User On-Line vs Class User5 29User On-Line vs Class User6 -1User On-Line vs Class User7 0User On-Line vs Class User8 11
ClassUser2
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences**SOF****SOF**cd<1>ls-laF|morecat<3>>……
Pattern/Class User0
**SOF****SOF**ls<1>exit<1>ls-laFxquake&fg……
**SOF****SOF**vi<1>vi<3>ls-lacat<2>……
USER 0Class0
USER 1Class1
USER 8Class8
…
…
Pattern Library
**SOF****SOF**ls-laF|Morecd<4>
…Test User
Sequence ClassificationPattern/Class
User1Pattern/Class
User8
User On-Line Є Class c
User On-Line vs Class User0 21User On-Line vs Class User1 49User On-Line vs Class User2 User On-Line vs Class User2 9 9User On-Line vs Class User3 3User On-Line vs Class User4 12User On-Line vs Class User5 29User On-Line vs Class User6 -1User On-Line vs Class User7 0User On-Line vs Class User8 11
ClassUser2
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences**SOF****SOF**cd<1>ls-laF|morecat<3>>……
Pattern/Class User0
**SOF****SOF**ls<1>exit<1>ls-laFxquake&fg……
**SOF****SOF**vi<1>vi<3>ls-lacat<2>……
USER 0Class0
USER 1Class1
USER 8Class8
…
…
Pattern Library
**SOF****SOF**ls-laF|Morecd<4>
…Test User
Sequence ClassificationPattern/Class
User1Pattern/Class
User8
User On-Line Є Class c
User On-Line vs Class User0 21User On-Line vs Class User1 User On-Line vs Class User1 49 49User On-Line vs Class User2 9User On-Line vs Class User3 3User On-Line vs Class User4 12User On-Line vs Class User5 29User On-Line vs Class User6 -1User On-Line vs Class User7 0User On-Line vs Class User8 11
NO Correctly NO Correctly ClassifiedClassified
ClassUser2
José Antonio Iglesias, Agapito Ledezma and Araceli Sanchis Sequence Classification Using Statistical Pattern
Recognition
Experiments Experiments – – UNIX command line sequencesUNIX command line sequences**SOF****SOF**cd<1>ls-laF|morecat<3>>……
Pattern/Class User0
**SOF****SOF**ls<1>exit<1>ls-laFxquake&fg……
**SOF****SOF**vi<1>vi<3>ls-lacat<2>……
USER 0Class0
USER 1Class1
USER 8Class8
…
…
Pattern Library
**SOF****SOF**ls-laF|Morecd<4>
…Test User
Sequence ClassificationPattern/Class
User1Pattern/Class
User8
User On-Line Є Class c
User On-Line vs Class User0 21User On-Line vs Class User1 User On-Line vs Class User1 49 49User On-Line vs Class User2 User On-Line vs Class User2 9 9User On-Line vs Class User3 3User On-Line vs Class User4 12User On-Line vs Class User5 29User On-Line vs Class User6 -1User On-Line vs Class User7 0User On-Line vs Class User8 11
NO Correctly NO Correctly ClassifiedClassified
- 40
ClassUser2