Stepping out of Flatland: Discovering Behavior Patterns as Topological Structures in Cyber Hypergraphs Article Swipe
Helen Jenne
,
Sinan G. Aksoy
,
Daniel M. Best
,
Alyson Bittner
,
Gregory Henselman‐Petrusek
,
Cliff Joslyn
,
Bill Kay
,
Audun Myers
,
Garret Seppala
,
Jackson T. Warley
,
Stephen J. Young
,
Emilie Purvine
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.16154
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.48550/arxiv.2311.16154
Data breaches and ransomware attacks occur so often that they have become part of our daily news cycle. This is due to a myriad of factors, including the increasing number of internet-of-things devices, shift to remote work during the pandemic, and advancement in adversarial techniques, which all contribute to the increase in both the complexity of data captured and the challenge of protecting our networks. At the same time, cyber research has made strides, leveraging advances in machine learning and natural language processing to focus on identifying sophisticated attacks that are known to evade conventional measures. While successful, the shortcomings of these methods, particularly the lack of interpretability, are inherent and difficult to overcome. Consequently, there is an ever-increasing need to develop new tools for analyzing cyber data to enable more effective attack detection. In this paper, we present a novel framework based in the theory of hypergraphs and topology to understand data from cyber networks through topological signatures, which are both flexible and can be traced back to the log data. While our approach's mathematical grounding requires some technical development, this pays off in interpretability, which we will demonstrate with concrete examples in a large-scale cyber network dataset. These examples are an introduction to the broader possibilities that lie ahead; our goal is to demonstrate the value of applying methods from the burgeoning fields of hypernetwork science and applied topology to understand relationships among behaviors in cyber data.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- http://arxiv.org/abs/2311.16154
- https://arxiv.org/pdf/2311.16154
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4389157301
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4389157301Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.48550/arxiv.2311.16154Digital Object Identifier
- Title
-
Stepping out of Flatland: Discovering Behavior Patterns as Topological Structures in Cyber HypergraphsWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-08Full publication date if available
- Authors
-
Helen Jenne, Sinan G. Aksoy, Daniel M. Best, Alyson Bittner, Gregory Henselman‐Petrusek, Cliff Joslyn, Bill Kay, Audun Myers, Garret Seppala, Jackson T. Warley, Stephen J. Young, Emilie PurvineList of authors in order
- Landing page
-
https://arxiv.org/abs/2311.16154Publisher landing page
- PDF URL
-
https://arxiv.org/pdf/2311.16154Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://arxiv.org/pdf/2311.16154Direct OA link when available
- Concepts
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Interpretability, Computer science, Data science, Topological data analysis, Network topology, Adversarial system, Cyber-physical system, Focus (optics), Computer security, Topology (electrical circuits), Artificial intelligence, Engineering, Operating system, Electrical engineering, Optics, Algorithm, PhysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.back | 167 |
| abstract_inverted_index.both | 52, 161 |
| abstract_inverted_index.data | 56, 127, 152 |
| abstract_inverted_index.from | 153, 221 |
| abstract_inverted_index.goal | 212 |
| abstract_inverted_index.have | 10 |
| abstract_inverted_index.lack | 105 |
| abstract_inverted_index.made | 72 |
| abstract_inverted_index.more | 130 |
| abstract_inverted_index.need | 119 |
| abstract_inverted_index.news | 16 |
| abstract_inverted_index.part | 12 |
| abstract_inverted_index.pays | 182 |
| abstract_inverted_index.same | 67 |
| abstract_inverted_index.some | 178 |
| abstract_inverted_index.that | 8, 89, 208 |
| abstract_inverted_index.they | 9 |
| abstract_inverted_index.this | 135, 181 |
| abstract_inverted_index.will | 188 |
| abstract_inverted_index.with | 190 |
| abstract_inverted_index.work | 36 |
| abstract_inverted_index.These | 199 |
| abstract_inverted_index.While | 96, 172 |
| abstract_inverted_index.among | 234 |
| abstract_inverted_index.based | 142 |
| abstract_inverted_index.cyber | 69, 126, 154, 196, 237 |
| abstract_inverted_index.daily | 15 |
| abstract_inverted_index.data. | 171, 238 |
| abstract_inverted_index.evade | 93 |
| abstract_inverted_index.focus | 84 |
| abstract_inverted_index.known | 91 |
| abstract_inverted_index.novel | 140 |
| abstract_inverted_index.occur | 5 |
| abstract_inverted_index.often | 7 |
| abstract_inverted_index.shift | 33 |
| abstract_inverted_index.there | 115 |
| abstract_inverted_index.these | 101 |
| abstract_inverted_index.time, | 68 |
| abstract_inverted_index.tools | 123 |
| abstract_inverted_index.value | 217 |
| abstract_inverted_index.which | 45, 159, 186 |
| abstract_inverted_index.ahead; | 210 |
| abstract_inverted_index.attack | 132 |
| abstract_inverted_index.become | 11 |
| abstract_inverted_index.cycle. | 17 |
| abstract_inverted_index.during | 37 |
| abstract_inverted_index.enable | 129 |
| abstract_inverted_index.fields | 224 |
| abstract_inverted_index.myriad | 23 |
| abstract_inverted_index.number | 29 |
| abstract_inverted_index.paper, | 136 |
| abstract_inverted_index.remote | 35 |
| abstract_inverted_index.theory | 145 |
| abstract_inverted_index.traced | 166 |
| abstract_inverted_index.applied | 229 |
| abstract_inverted_index.attacks | 4, 88 |
| abstract_inverted_index.broader | 206 |
| abstract_inverted_index.develop | 121 |
| abstract_inverted_index.machine | 77 |
| abstract_inverted_index.methods | 220 |
| abstract_inverted_index.natural | 80 |
| abstract_inverted_index.network | 197 |
| abstract_inverted_index.present | 138 |
| abstract_inverted_index.science | 227 |
| abstract_inverted_index.through | 156 |
| abstract_inverted_index.advances | 75 |
| abstract_inverted_index.applying | 219 |
| abstract_inverted_index.breaches | 1 |
| abstract_inverted_index.captured | 57 |
| abstract_inverted_index.concrete | 191 |
| abstract_inverted_index.dataset. | 198 |
| abstract_inverted_index.devices, | 32 |
| abstract_inverted_index.examples | 192, 200 |
| abstract_inverted_index.factors, | 25 |
| abstract_inverted_index.flexible | 162 |
| abstract_inverted_index.increase | 50 |
| abstract_inverted_index.inherent | 109 |
| abstract_inverted_index.language | 81 |
| abstract_inverted_index.learning | 78 |
| abstract_inverted_index.methods, | 102 |
| abstract_inverted_index.networks | 155 |
| abstract_inverted_index.requires | 177 |
| abstract_inverted_index.research | 70 |
| abstract_inverted_index.strides, | 73 |
| abstract_inverted_index.topology | 149, 230 |
| abstract_inverted_index.analyzing | 125 |
| abstract_inverted_index.behaviors | 235 |
| abstract_inverted_index.challenge | 60 |
| abstract_inverted_index.difficult | 111 |
| abstract_inverted_index.effective | 131 |
| abstract_inverted_index.framework | 141 |
| abstract_inverted_index.grounding | 176 |
| abstract_inverted_index.including | 26 |
| abstract_inverted_index.measures. | 95 |
| abstract_inverted_index.networks. | 64 |
| abstract_inverted_index.overcome. | 113 |
| abstract_inverted_index.pandemic, | 39 |
| abstract_inverted_index.technical | 179 |
| abstract_inverted_index.approach's | 174 |
| abstract_inverted_index.burgeoning | 223 |
| abstract_inverted_index.complexity | 54 |
| abstract_inverted_index.contribute | 47 |
| abstract_inverted_index.detection. | 133 |
| abstract_inverted_index.increasing | 28 |
| abstract_inverted_index.leveraging | 74 |
| abstract_inverted_index.processing | 82 |
| abstract_inverted_index.protecting | 62 |
| abstract_inverted_index.ransomware | 3 |
| abstract_inverted_index.understand | 151, 232 |
| abstract_inverted_index.advancement | 41 |
| abstract_inverted_index.adversarial | 43 |
| abstract_inverted_index.demonstrate | 189, 215 |
| abstract_inverted_index.hypergraphs | 147 |
| abstract_inverted_index.identifying | 86 |
| abstract_inverted_index.large-scale | 195 |
| abstract_inverted_index.signatures, | 158 |
| abstract_inverted_index.successful, | 97 |
| abstract_inverted_index.techniques, | 44 |
| abstract_inverted_index.topological | 157 |
| abstract_inverted_index.conventional | 94 |
| abstract_inverted_index.development, | 180 |
| abstract_inverted_index.hypernetwork | 226 |
| abstract_inverted_index.introduction | 203 |
| abstract_inverted_index.mathematical | 175 |
| abstract_inverted_index.particularly | 103 |
| abstract_inverted_index.shortcomings | 99 |
| abstract_inverted_index.Consequently, | 114 |
| abstract_inverted_index.possibilities | 207 |
| abstract_inverted_index.relationships | 233 |
| abstract_inverted_index.sophisticated | 87 |
| abstract_inverted_index.ever-increasing | 118 |
| abstract_inverted_index.interpretability, | 107, 185 |
| abstract_inverted_index.internet-of-things | 31 |
| cited_by_percentile_year | |
| countries_distinct_count | 0 |
| institutions_distinct_count | 12 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/3 |
| sustainable_development_goals[0].score | 0.6000000238418579 |
| sustainable_development_goals[0].display_name | Good health and well-being |
| citation_normalized_percentile |