Remediating Anomalous Traffic Behaviour in Future Networked Environments View Full Text


Ontology type: schema:Chapter      Open Access: True


Chapter Info

DATE

2011

AUTHORS

Angelos K. Marnerides , Matthew Jakeman , David Hutchison , Dimitrios P. Pezaros

ABSTRACT

The diverse characteristics of network anomalies, and the specific recovery approaches that can subsequently be employed to remediate their effects, have generally led to defence mechanisms tuned to respond to specific abnormalities; and they are often suboptimal for providing an overall resilience framework. Emerging future network environments are likely to require always-on, adaptive, and generic mechanisms that can integrate with the core networking infrastructure and provide for a range of self-* capabilities, ranging from self-protection to self-tuning. In this paper we present the design and implementation of an adaptive remediation component built on top of an autonomic network node architecture. A set of pluggable modules that employ diverse algorithms, together with explicit cross-layer interaction, has been engineered to mitigate different classes of anomalous traffic behaviour in response to both legitimate and malicious external stimuli. In collaboration with an always-on measurement-based anomaly detection component, our prototype facilitates the properties of self-optimisation and self-healing. More... »

PAGES

187-197

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/978-3-642-20931-4_15

DOI

http://dx.doi.org/10.1007/978-3-642-20931-4_15

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1053405072


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/08", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Information and Computing Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/0801", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Artificial Intelligence and Image Processing", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Infolab21, Computing Department, Lancaster University, Lancaster, UK", 
          "id": "http://www.grid.ac/institutes/grid.9835.7", 
          "name": [
            "Infolab21, Computing Department, Lancaster University, Lancaster, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Marnerides", 
        "givenName": "Angelos K.", 
        "id": "sg:person.016005611407.32", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016005611407.32"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Infolab21, Computing Department, Lancaster University, Lancaster, UK", 
          "id": "http://www.grid.ac/institutes/grid.9835.7", 
          "name": [
            "Infolab21, Computing Department, Lancaster University, Lancaster, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Jakeman", 
        "givenName": "Matthew", 
        "id": "sg:person.011046613402.16", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011046613402.16"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Infolab21, Computing Department, Lancaster University, Lancaster, UK", 
          "id": "http://www.grid.ac/institutes/grid.9835.7", 
          "name": [
            "Infolab21, Computing Department, Lancaster University, Lancaster, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Hutchison", 
        "givenName": "David", 
        "id": "sg:person.012636622347.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012636622347.55"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Department of Computing Science, University of Glasgow, Glasgow, UK", 
          "id": "http://www.grid.ac/institutes/grid.8756.c", 
          "name": [
            "Department of Computing Science, University of Glasgow, Glasgow, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Pezaros", 
        "givenName": "Dimitrios P.", 
        "id": "sg:person.016531721441.55", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016531721441.55"
        ], 
        "type": "Person"
      }
    ], 
    "datePublished": "2011", 
    "datePublishedReg": "2011-01-01", 
    "description": "The diverse characteristics of network anomalies, and the specific recovery approaches that can subsequently be employed to remediate their effects, have generally led to defence mechanisms tuned to respond to specific abnormalities; and they are often suboptimal for providing an overall resilience framework. Emerging future network environments are likely to require always-on, adaptive, and generic mechanisms that can integrate with the core networking infrastructure and provide for a range of self-* capabilities, ranging from self-protection to self-tuning. In this paper we present the design and implementation of an adaptive remediation component built on top of an autonomic network node architecture. A set of pluggable modules that employ diverse algorithms, together with explicit cross-layer interaction, has been engineered to mitigate different classes of anomalous traffic behaviour in response to both legitimate and malicious external stimuli. In collaboration with an always-on measurement-based anomaly detection component, our prototype facilitates the properties of self-optimisation and self-healing.", 
    "editor": [
      {
        "familyName": "Szab\u00f3", 
        "givenName": "R\u00f3bert", 
        "type": "Person"
      }, 
      {
        "familyName": "Zhu", 
        "givenName": "Hua", 
        "type": "Person"
      }, 
      {
        "familyName": "Imre", 
        "givenName": "S\u00e1ndor", 
        "type": "Person"
      }, 
      {
        "familyName": "Chaparadza", 
        "givenName": "Ranganai", 
        "type": "Person"
      }
    ], 
    "genre": "chapter", 
    "id": "sg:pub.10.1007/978-3-642-20931-4_15", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isPartOf": {
      "isbn": [
        "978-3-642-20930-7", 
        "978-3-642-20931-4"
      ], 
      "name": "Access Networks", 
      "type": "Book"
    }, 
    "keywords": [
      "anomalous traffic behavior", 
      "cross-layer interactions", 
      "future network environment", 
      "traffic behavior", 
      "anomaly detection component", 
      "network environment", 
      "network anomalies", 
      "networked environment", 
      "diverse algorithms", 
      "detection component", 
      "pluggable modules", 
      "node architecture", 
      "recovery approach", 
      "generic mechanism", 
      "diverse characteristics", 
      "range of self", 
      "remediation component", 
      "different classes", 
      "environment", 
      "algorithm", 
      "architecture", 
      "infrastructure", 
      "implementation", 
      "prototype", 
      "module", 
      "capability", 
      "framework", 
      "set", 
      "collaboration", 
      "behavior", 
      "design", 
      "external stimuli", 
      "properties", 
      "top", 
      "components", 
      "class", 
      "characteristics", 
      "resilience framework", 
      "range", 
      "core", 
      "mechanism", 
      "effect", 
      "approach", 
      "defense mechanisms", 
      "interaction", 
      "self", 
      "anomalies", 
      "response", 
      "specific abnormalities", 
      "stimuli", 
      "abnormalities", 
      "paper", 
      "specific recovery approaches", 
      "overall resilience framework", 
      "adaptive remediation component", 
      "autonomic network node architecture", 
      "network node architecture", 
      "explicit cross-layer interaction", 
      "malicious external stimuli", 
      "measurement-based anomaly detection component", 
      "Future Networked Environments"
    ], 
    "name": "Remediating Anomalous Traffic Behaviour in Future Networked Environments", 
    "pagination": "187-197", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1053405072"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/978-3-642-20931-4_15"
        ]
      }
    ], 
    "publisher": {
      "name": "Springer Nature", 
      "type": "Organisation"
    }, 
    "sameAs": [
      "https://doi.org/10.1007/978-3-642-20931-4_15", 
      "https://app.dimensions.ai/details/publication/pub.1053405072"
    ], 
    "sdDataset": "chapters", 
    "sdDatePublished": "2022-01-01T19:21", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/chapter/chapter_363.jsonl", 
    "type": "Chapter", 
    "url": "https://doi.org/10.1007/978-3-642-20931-4_15"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-20931-4_15'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-20931-4_15'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-20931-4_15'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/978-3-642-20931-4_15'


 

This table displays all metadata directly associated to this object as RDF triples.

160 TRIPLES      23 PREDICATES      87 URIs      80 LITERALS      7 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/978-3-642-20931-4_15 schema:about anzsrc-for:08
2 anzsrc-for:0801
3 schema:author Ne0d5effa3d7c4b59bf7b53d3d1f4f6f5
4 schema:datePublished 2011
5 schema:datePublishedReg 2011-01-01
6 schema:description The diverse characteristics of network anomalies, and the specific recovery approaches that can subsequently be employed to remediate their effects, have generally led to defence mechanisms tuned to respond to specific abnormalities; and they are often suboptimal for providing an overall resilience framework. Emerging future network environments are likely to require always-on, adaptive, and generic mechanisms that can integrate with the core networking infrastructure and provide for a range of self-* capabilities, ranging from self-protection to self-tuning. In this paper we present the design and implementation of an adaptive remediation component built on top of an autonomic network node architecture. A set of pluggable modules that employ diverse algorithms, together with explicit cross-layer interaction, has been engineered to mitigate different classes of anomalous traffic behaviour in response to both legitimate and malicious external stimuli. In collaboration with an always-on measurement-based anomaly detection component, our prototype facilitates the properties of self-optimisation and self-healing.
7 schema:editor N5cce0b34d59149efa9ff8eb94f06e974
8 schema:genre chapter
9 schema:inLanguage en
10 schema:isAccessibleForFree true
11 schema:isPartOf N2859ee79314f4518b207f6e87c189618
12 schema:keywords Future Networked Environments
13 abnormalities
14 adaptive remediation component
15 algorithm
16 anomalies
17 anomalous traffic behavior
18 anomaly detection component
19 approach
20 architecture
21 autonomic network node architecture
22 behavior
23 capability
24 characteristics
25 class
26 collaboration
27 components
28 core
29 cross-layer interactions
30 defense mechanisms
31 design
32 detection component
33 different classes
34 diverse algorithms
35 diverse characteristics
36 effect
37 environment
38 explicit cross-layer interaction
39 external stimuli
40 framework
41 future network environment
42 generic mechanism
43 implementation
44 infrastructure
45 interaction
46 malicious external stimuli
47 measurement-based anomaly detection component
48 mechanism
49 module
50 network anomalies
51 network environment
52 network node architecture
53 networked environment
54 node architecture
55 overall resilience framework
56 paper
57 pluggable modules
58 properties
59 prototype
60 range
61 range of self
62 recovery approach
63 remediation component
64 resilience framework
65 response
66 self
67 set
68 specific abnormalities
69 specific recovery approaches
70 stimuli
71 top
72 traffic behavior
73 schema:name Remediating Anomalous Traffic Behaviour in Future Networked Environments
74 schema:pagination 187-197
75 schema:productId N69f678db532144bc916a32d18651e8bf
76 Nd066c246054f49788ca548bf89adb1dd
77 schema:publisher Ncf6c9b32c53c4014a0cea6cbecd6db16
78 schema:sameAs https://app.dimensions.ai/details/publication/pub.1053405072
79 https://doi.org/10.1007/978-3-642-20931-4_15
80 schema:sdDatePublished 2022-01-01T19:21
81 schema:sdLicense https://scigraph.springernature.com/explorer/license/
82 schema:sdPublisher N90a79e20774c4161976170deead9b477
83 schema:url https://doi.org/10.1007/978-3-642-20931-4_15
84 sgo:license sg:explorer/license/
85 sgo:sdDataset chapters
86 rdf:type schema:Chapter
87 N15e66d5844ae4de98527d5b992df21a2 rdf:first sg:person.012636622347.55
88 rdf:rest Nefaa5876e982483885e62d1bafbaf5b2
89 N257b8ab4a38e4f078801e64bfd4d2d2a schema:familyName Szabó
90 schema:givenName Róbert
91 rdf:type schema:Person
92 N2859ee79314f4518b207f6e87c189618 schema:isbn 978-3-642-20930-7
93 978-3-642-20931-4
94 schema:name Access Networks
95 rdf:type schema:Book
96 N3b1dbd5aecdf432da170dd6e46d9d17a rdf:first Nb59c78a4bada4d56b513e34c10dce3a0
97 rdf:rest rdf:nil
98 N5cce0b34d59149efa9ff8eb94f06e974 rdf:first N257b8ab4a38e4f078801e64bfd4d2d2a
99 rdf:rest Nbab012fc42c042c1a11c681531775f3f
100 N69f678db532144bc916a32d18651e8bf schema:name dimensions_id
101 schema:value pub.1053405072
102 rdf:type schema:PropertyValue
103 N6cebbd24778f43418a069386dd8d625d rdf:first Na8facca323ea4edb87812b56a29efd9c
104 rdf:rest N3b1dbd5aecdf432da170dd6e46d9d17a
105 N90a79e20774c4161976170deead9b477 schema:name Springer Nature - SN SciGraph project
106 rdf:type schema:Organization
107 Na6f42b0e7dcd4607804ebc758aeb9b38 schema:familyName Zhu
108 schema:givenName Hua
109 rdf:type schema:Person
110 Na8facca323ea4edb87812b56a29efd9c schema:familyName Imre
111 schema:givenName Sándor
112 rdf:type schema:Person
113 Nb59c78a4bada4d56b513e34c10dce3a0 schema:familyName Chaparadza
114 schema:givenName Ranganai
115 rdf:type schema:Person
116 Nbab012fc42c042c1a11c681531775f3f rdf:first Na6f42b0e7dcd4607804ebc758aeb9b38
117 rdf:rest N6cebbd24778f43418a069386dd8d625d
118 Nbcabc5eb6e53432e9e4c854cb908163b rdf:first sg:person.011046613402.16
119 rdf:rest N15e66d5844ae4de98527d5b992df21a2
120 Ncf6c9b32c53c4014a0cea6cbecd6db16 schema:name Springer Nature
121 rdf:type schema:Organisation
122 Nd066c246054f49788ca548bf89adb1dd schema:name doi
123 schema:value 10.1007/978-3-642-20931-4_15
124 rdf:type schema:PropertyValue
125 Ne0d5effa3d7c4b59bf7b53d3d1f4f6f5 rdf:first sg:person.016005611407.32
126 rdf:rest Nbcabc5eb6e53432e9e4c854cb908163b
127 Nefaa5876e982483885e62d1bafbaf5b2 rdf:first sg:person.016531721441.55
128 rdf:rest rdf:nil
129 anzsrc-for:08 schema:inDefinedTermSet anzsrc-for:
130 schema:name Information and Computing Sciences
131 rdf:type schema:DefinedTerm
132 anzsrc-for:0801 schema:inDefinedTermSet anzsrc-for:
133 schema:name Artificial Intelligence and Image Processing
134 rdf:type schema:DefinedTerm
135 sg:person.011046613402.16 schema:affiliation grid-institutes:grid.9835.7
136 schema:familyName Jakeman
137 schema:givenName Matthew
138 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.011046613402.16
139 rdf:type schema:Person
140 sg:person.012636622347.55 schema:affiliation grid-institutes:grid.9835.7
141 schema:familyName Hutchison
142 schema:givenName David
143 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.012636622347.55
144 rdf:type schema:Person
145 sg:person.016005611407.32 schema:affiliation grid-institutes:grid.9835.7
146 schema:familyName Marnerides
147 schema:givenName Angelos K.
148 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016005611407.32
149 rdf:type schema:Person
150 sg:person.016531721441.55 schema:affiliation grid-institutes:grid.8756.c
151 schema:familyName Pezaros
152 schema:givenName Dimitrios P.
153 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.016531721441.55
154 rdf:type schema:Person
155 grid-institutes:grid.8756.c schema:alternateName Department of Computing Science, University of Glasgow, Glasgow, UK
156 schema:name Department of Computing Science, University of Glasgow, Glasgow, UK
157 rdf:type schema:Organization
158 grid-institutes:grid.9835.7 schema:alternateName Infolab21, Computing Department, Lancaster University, Lancaster, UK
159 schema:name Infolab21, Computing Department, Lancaster University, Lancaster, UK
160 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...