Network anomaly detection: a machine learning perspective
Gespeichert in:
| Hauptverfasser: | , |
|---|---|
| Format: | Buch |
| Sprache: | English |
| Veröffentlicht: |
Boca Raton, Fla.[u.a.]
CRC Press, Francis & Taylor Group
2014
|
| Schlagworte: | |
| Online-Zugang: | Inhaltsverzeichnis |
| Beschreibung: | Includes bibliographical references and index |
| Beschreibung: | XXV, 340 S. graph. Darst. |
| ISBN: | 9781466582088 |
Internformat
MARC
| LEADER | 00000nam a2200000zc 4500 | ||
|---|---|---|---|
| 001 | BV041149425 | ||
| 003 | DE-604 | ||
| 005 | 20130906 | ||
| 007 | t | ||
| 008 | 130717s2014 xxud||| |||| 00||| eng d | ||
| 010 | |a 2013014913 | ||
| 020 | |a 9781466582088 |c hardback |9 978-1-4665-8208-8 | ||
| 035 | |a (OCoLC)859373359 | ||
| 035 | |a (DE-599)BVBBV041149425 | ||
| 040 | |a DE-604 |b ger |e aacr | ||
| 041 | 0 | |a eng | |
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| 049 | |a DE-473 |a DE-83 |a DE-858 | ||
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| 084 | |a ST 276 |0 (DE-625)143642: |2 rvk | ||
| 100 | 1 | |a Bhattacharyya, Dhruba K. |e Verfasser |4 aut | |
| 245 | 1 | 0 | |a Network anomaly detection |b a machine learning perspective |c Dhruba Kumar Bhattacharyya ; Jugal Kumar Kalita |
| 264 | 1 | |a Boca Raton, Fla.[u.a.] |b CRC Press, Francis & Taylor Group |c 2014 | |
| 300 | |a XXV, 340 S. |b graph. Darst. | ||
| 336 | |b txt |2 rdacontent | ||
| 337 | |b n |2 rdamedia | ||
| 338 | |b nc |2 rdacarrier | ||
| 500 | |a Includes bibliographical references and index | ||
| 650 | 4 | |a Computer networks |x Security measures | |
| 650 | 4 | |a Intrusion detection systems (Computer security) | |
| 650 | 4 | |a Machine learning | |
| 650 | 7 | |a COMPUTERS / Machine Theory |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Security / General |2 bisacsh | |
| 650 | 7 | |a COMPUTERS / Security / Cryptography |2 bisacsh | |
| 650 | 0 | 7 | |a Computersicherheit |0 (DE-588)4274324-2 |2 gnd |9 rswk-swf |
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| 689 | 0 | 1 | |a Computersicherheit |0 (DE-588)4274324-2 |D s |
| 689 | 0 | |5 DE-604 | |
| 700 | 1 | |a Kalita, Jugal Kumar |e Verfasser |0 (DE-588)1038035732 |4 aut | |
| 856 | 4 | 2 | |m Digitalisierung UB Bamberg - ADAM Catalogue Enrichment |q application/pdf |u http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026124922&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |3 Inhaltsverzeichnis |
| 999 | |a oai:aleph.bib-bvb.de:BVB01-026124922 | ||
Datensatz im Suchindex
| _version_ | 1804150550675062784 |
|---|---|
| adam_text | Contents
List of Figures
xv
List of Tables
xvii
Preface
xix
Acknowledgments
xxi
Abstract
xxiii
Authors
xxv
1
Introduction
1
1.1
The Internet and Modern Networks
........... 3
1.2
Network Vulnerabilities
.................. 4
1.3
Anomalies and Anomalies in Networks
......... 5
1.4
Machine Learning
..................... 7
1.5
Prior Work on Network Anomaly Detection
....... 9
1.6
Contributions of This Book
................ 11
1.7
Organization
........................ 13
2
Networks and Anomalies
15
2.1
Networking Basics
..................... 15
2.1.1
Typical View of a Network
............ 16
2.1.2
Communication Media
............... 16
2.1.2.1
Guided Media
.............. 17
2.1.2.2
Unguided Media
............. 18
2.1.3
Network Software
.................. 20
2.1.3.1
Layered Architecture
.......... 21
2.1.3.2
Connection-Oriented and Connection¬
less Services
............... 21
2.1.3.3
Service Primitives
............ 21
2.1.3.4
Services and Protocols
.......... 22
VII
viii Contents
2.1.4
Reference Models
.................. 22
2.1.4.1
The ISO
OSI
Reference Model
..... 23
2.1.4.2
TCP/IP Reference Model
........ 24
2.1.5
Protocols
...................... 26
2.1.5.1
Transport Control Protocol
....... 27
2.1.5.2
User Datagram Protocol
........ 27
2.1.5.3
Internet Protocol (IP)
.......... 28
2.1.5.4
SMTP
................... 29
2.1.5.5
SNMP
.................. 29
2.1.5.6
ICMP
................... 29
2.1.5.7
FTP
.................... 30
2.1.5.8
Telnet
................... 30
2.1.6
Types of Networks
................. 31
2.1.6.1
Local Area Networks (LAN)
...... 31
2.1.6.2
Wide Area Networks (WAN)
...... 31
2.1.6.3
Metropolitan Area Network (MAN)
. . 32
2.1.6.4
Wireless Networks
............ 32
2.1.6.5
Internetworks
............... 33
2.1.6.6
The Internet
............... 33
2.1.7
Scales of Networks
................. 34
2.1.8
Network Topologies
................ 35
2.1.8.1
Bus
.................... 35
2.1.8.2
Ring
.................... 36
2.1.8.3
Tree
.................... 36
2.1.8.4
Star
.................... 37
2.1.9
Hardware Components
............... 37
2.1.9.1
Network Communication Devices
.... 37
2.1.9.2
Network Interface Card (NIC)
..... 41
2.1.9.3
Transceivers
............... 42
2.1.9.4
Media Converter
............. 43
2.1.10
Network Performance
............... 43
2.1.10.1
Network Performance Constraints
... 43
2.1.10.2
Network Performance Parameter Tun¬
ing
..................... 44
2.1.10.3
Performance Oriented System Design
. 44
2.1.10.4
Protocols for Gigabit Networks
..... 45
2.1.10.5
Faster Processing of TPDU
....... 45
2.2
Anomalies in a Network
.................. 45
2.2.1
Network Vulnerabilities
.............. 46
Contents ix
2.2.1.1 Network
Configuration
Vulnerabilities
. 46
2.2.1.2 Network Hardware
Vulnerabilities
... 47
2.2.1.3 Network Perimeter
Vulnerabilities
... 48
2.2.1.4 Network Monitoring
and Logging Vul¬
nerabilities
................ 48
2.2.1.5
Communication Vulnerabilities
..... 49
2.2.1.6
Wireless Connection Vulnerabilities
. . 49
2.2.2
Security-Related Network Anomalies
....... 49
2.2.3
Who Attacks Networks
.............. 50
2.2.4
Precursors to an Attack
.............. 51
2.2.5
Network Attacks Taxonomy
............ 52
2.2.5.1
Denial of Service (DoS)
......... 53
2.2.5.2
User to Root Attacks (U2R)
...... 54
2.2.5.3
Remote to Local (R2L)
......... 54
2.2.5.4
Probe
................... 55
2.2.6
Discussion
...................... 55
An Overview of Machine Learning Methods
57
3.1
Introduction
........................ 57
3.2
Types of Machine Learning Methods
........... 59
3.3
Supervised Learning: Some Popular Methods
...... 60
3.3.1
Decision and Regression Trees
.......... 61
3.3.1.1
Classification and Regression Tree
... 62
3.3.2
Support Vector Machines
............. 67
3.4
Unsupervised Learning
.................. 69
3.4.1
Cluster Analysis
.................. 70
3.4.1.1
Various Types of Data
.......... 71
3.4.1.2
Proximity Measures
........... 72
3.4.1.3
Clustering Methods
........... 73
3.4.1.4
Discussion
................ 87
3.4.2
Outlier Mining
................... 88
3.4.3
Association Rule Learning
............. 96
3.4.3.1
Basic Concepts
.............. 97
3.4.4
Frequent Itemset Mining Algorithms
....... 99
3.4.5
Rule Generation Algorithms
............ 103
3.4.6
Discussion
...................... 105
3.5
Probabilistic Learning
................... 106
3.5.1
Learning
Bayes
Nets
................ 106
3.5.2
Simple Probabilistic Learning: Naive
Bayes
. . . 107
3.5.3
Hidden Markov Models
.............. 108
Contents
3.5.4
Expectation Maximization Algorithm
......110
3.6
Soft Computing
......................112
3.6.1
Artificial Neural Networks
............. 113
3.6.2
Rough Sets
..................... 113
3.6.3
Fuzzy Logic
..................... 114
3.6.4
Evolutionary Computation
............ 115
3.6.5
Ant Colony Optimization
............. 115
3.7
Reinforcement Learning
.................. 116
3.8
Hybrid Learning Methods
................. 117
3.9
Discussion
......................... 118
Detecting Anomalies in Network Data
121
4.1
Detection of Network Anomalies
............. 121
4.1.1
Host-Based IDS (HIDS)
.............. 121
4.1.2
Network-Based IDS
(NIDS)
............ 122
4.1.3
Anomaly-Based Network Intrusion Detection
. . 123
4.1.4
Supervised Anomaly Detection Approach
.... 124
4.1.5
Issues
........................ 129
4.1.6
Unsupervised Anomaly Detection Approach
. . . 129
4.1.7
Issues
........................ 132
4.1.8
Hybrid Detection Approach
............ 132
4.1.9
Issues
........................ 133
4.2
Aspects of Network Anomaly Detection
......... 133
4.2.1
Proximity Measure and Types of Data
...... 134
4.2.2
Relevant Feature Identification
.......... 135
4.2.3
Anomaly Score
................... 135
4.3
Datasets
.......................... 140
4.3.1
Public
Datasets
................... 141
4.3.1.1
KDD Cup
1999
Dataset
......... 141
4.3.1.2
NSL-KDD
Dataset
............ 143
4.3.2
Private
Datasets:
Collection and Preparation
. . 144
4.3.2.1
TUIDS Intrusion
Dataset
........ 144
4.3.3
Network Simulation
................ 151
4.4
Discussion
......................... 151
Feature Selection
157
5.1
Feature Selection vs. Feature Extraction
........ 158
5.2
Feature Relevance
..................... 158
5.3
Advantages
......................... 160
5.4
Applications of Feature Selection
............. 160
Contents xi
5.4.1 Bioinformatics................... 160
5.4.2 Network
Security
.................. 161
5.4.3 Text
Categorization
................ 162
5.4.4
Biometrics......................
162
5.4.5
Content-Based
Image
Retrieval
.......... 162
5.5 Prior
Surveys on Feature Selection
............ 163
5.5.1
A Comparison with Prior Surveys
........ 163
5.6
Problem Formulation
................... 166
5.7
Steps in Feature Selection
................. 167
5.7.1
Subset Generation
................. 168
5.7.1.1
Random Subset Generation
....... 168
5.7.1.2
Heuristic Subset Generation
...... 168
5.7.1.3
Complete Subset Generation
...... 169
5.7.2
Feature Subset Evaluation
............. 169
5.7.2.1
Dependent Criteria
........... 169
5.7.2.2
Independent Criteria
.......... 169
5.7.3
Goodness Criteria
................. 169
5.7.4
Result Validation
.................. 170
5.7.4.1
External Validation
........... 170
5.7.4.2
Internal Validation
............ 170
5.8
Feature Selection Methods: A Taxonomy
........ 171
5.9
Existing Methods of Feature Selection
.......... 173
5.9.1
Statistical Feature Selection
............ 174
5.9.2
Information Theoretic Feature Selection
..... 176
5.9.3
Soft Computing Methods
............. 178
5.9.4
Clustering and Association Mining Approach
. . 179
5.9.5
Ensemble Approach
................ 180
5.10
Subset Evaluation Measures
............... 181
5.10.1
Inconsistency Rate
................. 181
5.10.2
Relevance
...................... 182
5.10.3
Symmetric Uncertainty
.............. 182
5.10.4
Dependency
..................... 183
5.10.5
Fuzzy Entropy
................... 183
5.10.6
Hamming Loss
................... 184
5.10.7
Ranking Loss
.................... 184
5.11
Systems and Tools for Feature Selection
......... 184
5.12
Discussion
......................... 189
xii Contents
6
Approaches to Network Anomaly Detection
191
6.1
Network Anomaly Detection Methods
.......... 191
6.1.1
Requirements
.................... 192
6.2
Types of Network Anomaly Detection Methods
..... 192
6.3
Anomaly Detection Using Supervised Learning
..... 193
6.3.1
Parametric Methods
................ 194
6.3.2
Nonparametric Methods
.............. 195
6.4
Anomaly Detection Using Unsupervised Learning
. . . 199
6.4.1
Clustering-Based Anomaly Detection Methods
. 199
6.4.2
Anomaly Detection Using the Outlier Mining
. . 202
6.4.3
Anomaly Detection Using Association Mining
. . 203
6.5
Anomaly Detection Using Probabilistic Learning
.... 207
6.5.1
Methods Using the Hidden Markov Model
.... 207
6.5.2
Methods Using Bayesian Networks
........ 209
6.5.3
Naive
Bayes
Methods
............... 210
6.5.4
Gaussian Mixture Model
.............. 211
6.5.5
Methods Using the EM Algorithm
........ 214
6.6
Anomaly Detection Using Soft Computing
....... 216
6.6.1
Genetic Algorithm Approaches
.......... 216
6.6.2
Artificial Neural Network Approaches
...... 217
6.6.3
Fuzzy Set Theoretic Approach
.......... 218
6.6.4
Rough Set Approaches
............... 218
6.6.5
Ant Colony and
AIS
Approaches
......... 219
6.7
Knowledge in Anomaly Detection
............ 222
6.7.1
Expert System and Rule-Based Approaches
. . . 223
6.7.2
Ontology- and Logic-Based Approaches
..... 225
6.8
Anomaly Detection Using Combination Learners
.... 226
6.8.1
Ensemble Methods
................. 226
6.8.2
Fusion Methods
................... 227
6.8.3
Hybrid Methods
.................. 228
6.9
Discussion
......................... 229
7
Evaluation Methods
235
7.1
Accuracy
.......................... 235
7.1.1
Sensitivity and Specificity
............. 236
7.1.2
Misclassification Rate
............... 237
7.1.3
Confusion Matrix
.................. 237
7.1.4
Precision, Recall and F-measure
......... 238
7.1.5
Receiver Operating Characteristics Curves
.... 240
7.2
Performance
........................ 241
Contents xiii
7.3
Completeness
........................ 242
7.4
Timeliness
......................... 242
7.5
Stability
.......................... 243
7.6
Interoperability
...................... 243
7.7
Data Quality, Validity and Reliability
.......... 243
7.8
Alert Information
..................... 245
7.9
Unknown Attacks Detection
............... 245
7.10
Updating References
.................... 245
7.11
Discussion
......................... 246
8
Tools and Systems
247
8.1
Introduction
........................ 247
8.1.1
Attacker s Motivation
............... 247
8.1.2
Steps in Attack Launching
............. 248
8.1.3
Launching and Detecting Attacks
......... 248
8.1.3.1
Attack Launching Tools and Systems
. 250
8.1.3.2
Attack Detecting Tools and Systems
. . 250
8.2
Attack Related Tools
................... 251
8.2.1
Taxonomy
...................... 252
8.2.2
Information Gathering Tools
........... 252
8.2.2.1
Sniffing Tools
............... 253
8.2.2.2
Network Mapping or Scanning Tools
. . 259
8.2.3
Attack Launching Tools
.............. 261
8.2.3.1
Trojans
.................. 262
8.2.3.2
Denial of Service Attacks
........ 264
8.2.3.3
Packet Forging Attack Tools
...... 267
8.2.3.4
Application Layer Attack Tools
.... 270
8.2.3.5
Fingerprinting Attack Tools
...... 271
8.2.3.6
User Attack Tools
............ 273
8.2.3.7
Other Attack Tools
........... 275
8.2.4
Network Monitoring Tools
............. 277
8.2.4.1
Visualization Tools
........... 277
8.3
Attack Detection Systems
................. 280
8.4
Discussion
......................... 286
9
Open Issues, Challenges and Concluding Remarks
289
9.1
Runtime Limitations for Anomaly Detection Systems
. 290
9.2
Reducing the False Alarm Rate
............. 290
9.3
Issues in Dimensionality Reduction
........... 290
9.4
Computational Needs of Network Defense Mechanisms
291
xiv Contents
9.5
Designing
Generic
Anomaly Detection Systems
..... 291
9.6
Handling Sophisticated Anomalies
............ 291
9.7
Adaptability to Unknown Attacks
............ 292
9.8
Detecting and Handling Large-Scale Attacks
...... 292
9.9
Infrastructure Attacks
................... 292
9.10
High Intensity Attacks
.................. 292
9.11
More Inventive Attacks
.................. 293
9.12
Concluding Remarks
................... 293
References
295
Index
337
|
| any_adam_object | 1 |
| author | Bhattacharyya, Dhruba K. Kalita, Jugal Kumar |
| author_GND | (DE-588)1038035732 |
| author_facet | Bhattacharyya, Dhruba K. Kalita, Jugal Kumar |
| author_role | aut aut |
| author_sort | Bhattacharyya, Dhruba K. |
| author_variant | d k b dk dkb j k k jk jkk |
| building | Verbundindex |
| bvnumber | BV041149425 |
| callnumber-first | T - Technology |
| callnumber-label | TK5105 |
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| callnumber-search | TK5105.59 |
| callnumber-sort | TK 45105.59 |
| callnumber-subject | TK - Electrical and Nuclear Engineering |
| classification_rvk | ST 276 |
| ctrlnum | (OCoLC)859373359 (DE-599)BVBBV041149425 |
| dewey-full | 005.8 |
| dewey-hundreds | 000 - Computer science, information, general works |
| dewey-ones | 005 - Computer programming, programs, data, security |
| dewey-raw | 005.8 |
| dewey-search | 005.8 |
| dewey-sort | 15.8 |
| dewey-tens | 000 - Computer science, information, general works |
| discipline | Informatik |
| format | Book |
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| id | DE-604.BV041149425 |
| illustrated | Illustrated |
| indexdate | 2024-07-10T00:40:43Z |
| institution | BVB |
| isbn | 9781466582088 |
| language | English |
| lccn | 2013014913 |
| oai_aleph_id | oai:aleph.bib-bvb.de:BVB01-026124922 |
| oclc_num | 859373359 |
| open_access_boolean | |
| owner | DE-473 DE-BY-UBG DE-83 DE-858 |
| owner_facet | DE-473 DE-BY-UBG DE-83 DE-858 |
| physical | XXV, 340 S. graph. Darst. |
| publishDate | 2014 |
| publishDateSearch | 2014 |
| publishDateSort | 2014 |
| publisher | CRC Press, Francis & Taylor Group |
| record_format | marc |
| spelling | Bhattacharyya, Dhruba K. Verfasser aut Network anomaly detection a machine learning perspective Dhruba Kumar Bhattacharyya ; Jugal Kumar Kalita Boca Raton, Fla.[u.a.] CRC Press, Francis & Taylor Group 2014 XXV, 340 S. graph. Darst. txt rdacontent n rdamedia nc rdacarrier Includes bibliographical references and index Computer networks Security measures Intrusion detection systems (Computer security) Machine learning COMPUTERS / Machine Theory bisacsh COMPUTERS / Security / General bisacsh COMPUTERS / Security / Cryptography bisacsh Computersicherheit (DE-588)4274324-2 gnd rswk-swf Rechnernetz (DE-588)4070085-9 gnd rswk-swf Rechnernetz (DE-588)4070085-9 s Computersicherheit (DE-588)4274324-2 s DE-604 Kalita, Jugal Kumar Verfasser (DE-588)1038035732 aut Digitalisierung UB Bamberg - ADAM Catalogue Enrichment application/pdf http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026124922&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA Inhaltsverzeichnis |
| spellingShingle | Bhattacharyya, Dhruba K. Kalita, Jugal Kumar Network anomaly detection a machine learning perspective Computer networks Security measures Intrusion detection systems (Computer security) Machine learning COMPUTERS / Machine Theory bisacsh COMPUTERS / Security / General bisacsh COMPUTERS / Security / Cryptography bisacsh Computersicherheit (DE-588)4274324-2 gnd Rechnernetz (DE-588)4070085-9 gnd |
| subject_GND | (DE-588)4274324-2 (DE-588)4070085-9 |
| title | Network anomaly detection a machine learning perspective |
| title_auth | Network anomaly detection a machine learning perspective |
| title_exact_search | Network anomaly detection a machine learning perspective |
| title_full | Network anomaly detection a machine learning perspective Dhruba Kumar Bhattacharyya ; Jugal Kumar Kalita |
| title_fullStr | Network anomaly detection a machine learning perspective Dhruba Kumar Bhattacharyya ; Jugal Kumar Kalita |
| title_full_unstemmed | Network anomaly detection a machine learning perspective Dhruba Kumar Bhattacharyya ; Jugal Kumar Kalita |
| title_short | Network anomaly detection |
| title_sort | network anomaly detection a machine learning perspective |
| title_sub | a machine learning perspective |
| topic | Computer networks Security measures Intrusion detection systems (Computer security) Machine learning COMPUTERS / Machine Theory bisacsh COMPUTERS / Security / General bisacsh COMPUTERS / Security / Cryptography bisacsh Computersicherheit (DE-588)4274324-2 gnd Rechnernetz (DE-588)4070085-9 gnd |
| topic_facet | Computer networks Security measures Intrusion detection systems (Computer security) Machine learning COMPUTERS / Machine Theory COMPUTERS / Security / General COMPUTERS / Security / Cryptography Computersicherheit Rechnernetz |
| url | http://bvbr.bib-bvb.de:8991/F?func=service&doc_library=BVB01&local_base=BVB01&doc_number=026124922&sequence=000002&line_number=0001&func_code=DB_RECORDS&service_type=MEDIA |
| work_keys_str_mv | AT bhattacharyyadhrubak networkanomalydetectionamachinelearningperspective AT kalitajugalkumar networkanomalydetectionamachinelearningperspective |