Detection of Abnormal SIP Signaling Patterns: A Deep Learning Comparison Article Swipe
Diogo Pereira
,
Rodolfo Oliveira
·
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/computers11020027
YOU?
·
· 2022
· Open Access
·
· DOI: https://doi.org/10.3390/computers11020027
This paper investigates the detection of abnormal sequences of signaling packets purposely generated to perpetuate signaling-based attacks in computer networks. The problem is studied for the Session Initiation Protocol (SIP) using a dataset of signaling packets exchanged by multiple end-users. A sequence of SIP messages never observed before can indicate possible exploitation of a vulnerability and its detection or prediction is of high importance to avoid security attacks due to unknown abnormal SIP dialogs. The paper starts to briefly characterize the adopted dataset and introduces multiple definitions to detail how the deep learning-based approach is adopted to detect possible attacks. The proposed solution is based on a convolutional neural network capable of exploring the definition of an orthogonal space representing the SIP dialogs. The space is then used to train the neural network model to classify the type of SIP dialog according to a sequence of SIP packets prior observed. The classifier of unknown SIP dialogs relies on the statistical properties of the supervised learning of known SIP dialogs. Experimental results are presented to assess the solution in terms of SIP dialogs prediction, unknown SIP dialogs detection, and computational performance, demonstrating the usefulness of the proposed methodology to rapidly detect signaling-based attacks.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/computers11020027
- https://www.mdpi.com/2073-431X/11/2/27/pdf?version=1645093235
- OA Status
- gold
- Cited By
- 6
- References
- 21
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4213245394
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4213245394Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/computers11020027Digital Object Identifier
- Title
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Detection of Abnormal SIP Signaling Patterns: A Deep Learning ComparisonWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2022Year of publication
- Publication date
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2022-02-17Full publication date if available
- Authors
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Diogo Pereira, Rodolfo OliveiraList of authors in order
- Landing page
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https://doi.org/10.3390/computers11020027Publisher landing page
- PDF URL
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https://www.mdpi.com/2073-431X/11/2/27/pdf?version=1645093235Direct link to full text PDF
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2073-431X/11/2/27/pdf?version=1645093235Direct OA link when available
- Concepts
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Computer science, Session Initiation Protocol, Network packet, Classifier (UML), Convolutional neural network, Artificial intelligence, Deep learning, Machine learning, Computer network, ServerTop concepts (fields/topics) attached by OpenAlex
- Cited by
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6Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1, 2024: 3, 2023: 2Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| referenced_works | https://openalex.org/W4242138061, https://openalex.org/W2256260561, https://openalex.org/W2136927118, https://openalex.org/W2139942987, https://openalex.org/W2111505384, https://openalex.org/W2096549642, https://openalex.org/W4233730469, https://openalex.org/W3171096778, https://openalex.org/W3123788673, https://openalex.org/W3128856937, https://openalex.org/W3217486576, https://openalex.org/W3034976607, https://openalex.org/W2791640589, https://openalex.org/W3092855438, https://openalex.org/W2767470436, https://openalex.org/W3139082917, https://openalex.org/W2152063140, https://openalex.org/W2903745530, https://openalex.org/W1977616396, https://openalex.org/W2556408062, https://openalex.org/W1504192099 |
| referenced_works_count | 21 |
| abstract_inverted_index.A | 40 |
| abstract_inverted_index.a | 31, 53, 106, 143 |
| abstract_inverted_index.an | 116 |
| abstract_inverted_index.by | 37 |
| abstract_inverted_index.in | 17, 177 |
| abstract_inverted_index.is | 22, 60, 94, 103, 125 |
| abstract_inverted_index.of | 5, 8, 33, 42, 52, 61, 111, 115, 138, 145, 152, 161, 165, 179, 193 |
| abstract_inverted_index.on | 105, 157 |
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| abstract_inverted_index.SIP | 43, 72, 121, 139, 146, 154, 167, 180, 184 |
| abstract_inverted_index.The | 20, 74, 100, 123, 150 |
| abstract_inverted_index.and | 55, 83, 187 |
| abstract_inverted_index.are | 171 |
| abstract_inverted_index.can | 48 |
| abstract_inverted_index.due | 68 |
| abstract_inverted_index.for | 24 |
| abstract_inverted_index.how | 89 |
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| abstract_inverted_index.This | 0 |
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| abstract_inverted_index.model | 133 |
| abstract_inverted_index.never | 45 |
| abstract_inverted_index.paper | 1, 75 |
| abstract_inverted_index.prior | 148 |
| abstract_inverted_index.space | 118, 124 |
| abstract_inverted_index.terms | 178 |
| abstract_inverted_index.train | 129 |
| abstract_inverted_index.using | 30 |
| abstract_inverted_index.assess | 174 |
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| abstract_inverted_index.detail | 88 |
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| abstract_inverted_index.neural | 108, 131 |
| abstract_inverted_index.relies | 156 |
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| abstract_inverted_index.adopted | 81, 95 |
| abstract_inverted_index.attacks | 16, 67 |
| abstract_inverted_index.briefly | 78 |
| abstract_inverted_index.capable | 110 |
| abstract_inverted_index.dataset | 32, 82 |
| abstract_inverted_index.dialogs | 155, 181, 185 |
| abstract_inverted_index.network | 109, 132 |
| abstract_inverted_index.packets | 10, 35, 147 |
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| abstract_inverted_index.rapidly | 198 |
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| abstract_inverted_index.Protocol | 28 |
| abstract_inverted_index.abnormal | 6, 71 |
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| abstract_inverted_index.attacks. | 99, 201 |
| abstract_inverted_index.classify | 135 |
| abstract_inverted_index.computer | 18 |
| abstract_inverted_index.dialogs. | 73, 122, 168 |
| abstract_inverted_index.indicate | 49 |
| abstract_inverted_index.learning | 164 |
| abstract_inverted_index.messages | 44 |
| abstract_inverted_index.multiple | 38, 85 |
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| abstract_inverted_index.solution | 102, 176 |
| abstract_inverted_index.according | 141 |
| abstract_inverted_index.detection | 4, 57 |
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| abstract_inverted_index.exploring | 112 |
| abstract_inverted_index.generated | 12 |
| abstract_inverted_index.networks. | 19 |
| abstract_inverted_index.observed. | 149 |
| abstract_inverted_index.presented | 172 |
| abstract_inverted_index.purposely | 11 |
| abstract_inverted_index.sequences | 7 |
| abstract_inverted_index.signaling | 9, 34 |
| abstract_inverted_index.Initiation | 27 |
| abstract_inverted_index.classifier | 151 |
| abstract_inverted_index.definition | 114 |
| abstract_inverted_index.detection, | 186 |
| abstract_inverted_index.end-users. | 39 |
| abstract_inverted_index.importance | 63 |
| abstract_inverted_index.introduces | 84 |
| abstract_inverted_index.orthogonal | 117 |
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| abstract_inverted_index.prediction | 59 |
| abstract_inverted_index.properties | 160 |
| abstract_inverted_index.supervised | 163 |
| abstract_inverted_index.usefulness | 192 |
| abstract_inverted_index.definitions | 86 |
| abstract_inverted_index.methodology | 196 |
| abstract_inverted_index.prediction, | 182 |
| abstract_inverted_index.statistical | 159 |
| abstract_inverted_index.Experimental | 169 |
| abstract_inverted_index.characterize | 79 |
| abstract_inverted_index.exploitation | 51 |
| abstract_inverted_index.investigates | 2 |
| abstract_inverted_index.performance, | 189 |
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| abstract_inverted_index.vulnerability | 54 |
| abstract_inverted_index.learning-based | 92 |
| abstract_inverted_index.signaling-based | 15, 200 |
| cited_by_percentile_year.max | 97 |
| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5063353259 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 2 |
| corresponding_institution_ids | https://openalex.org/I4210120471, https://openalex.org/I83558840 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.7099999785423279 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.74372903 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |