Behavioral Based Insider Threat Detection Using Deep Learning Article Swipe
Rida Nasir
,
Mehreen Afzal
,
Rabia Latif
,
Waseem Iqbal
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3118297
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1109/access.2021.3118297
The most detrimental cyber attacks are usually not originated by malicious outsiders or malware but from trusted insiders. The main advantage insider attackers have over external elements is their ability to bypass security checks and remain undiscovered, this may cause serious damage to the organizational assets. This paper focuses on insider threat detection through behavioral analysis of users. User behavior is categorized as normal or malicious based on user activity. A series of events and activities are analyzed for feature selection to efficiently detect adversarial behavior. Selected feature vectors are used for model training during the implementation phase. A deep learning based approach is proposed that detects insiders with greater accuracy and low false positive rate. A rich event / user role based feature set containing Logon/Logoff events, User_role, Functional_unit etc are used for detection. The dataset used is the CMU CERT synthetic insider threat dataset r4.2. Performance of our proposed algorithm has been compared to other well-known techniques i.e. long short term Memory- convolutional neural network, random forest, long short term memory- recurrent neural network, one class support vector machine, Markov chain model, multi state long short term memory & convolutional neural network, gated recurrent unit & skipgram. The comparison proved that our novel approach produces relatively good accuracy(90.60%), precision(97%) and F1 Score (94%).
Related Topics
Concepts
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/access.2021.3118297
- https://ieeexplore.ieee.org/ielx7/6287639/6514899/09559986.pdf
- OA Status
- gold
- Cited By
- 91
- References
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- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3202733154
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3202733154Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/access.2021.3118297Digital Object Identifier
- Title
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Behavioral Based Insider Threat Detection Using Deep LearningWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2021Year of publication
- Publication date
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2021-01-01Full publication date if available
- Authors
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Rida Nasir, Mehreen Afzal, Rabia Latif, Waseem IqbalList of authors in order
- Landing page
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https://doi.org/10.1109/access.2021.3118297Publisher landing page
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/09559986.pdfDirect link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://ieeexplore.ieee.org/ielx7/6287639/6514899/09559986.pdfDirect OA link when available
- Concepts
-
Insider threat, Computer science, Insider, Artificial intelligence, Computer security, Deep learning, Law, Political scienceTop concepts (fields/topics) attached by OpenAlex
- Cited by
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91Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 32, 2024: 34, 2023: 20, 2022: 5Per-year citation counts (last 5 years)
- References (count)
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44Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.deep | 99 |
| abstract_inverted_index.from | 15 |
| abstract_inverted_index.good | 208 |
| abstract_inverted_index.have | 23 |
| abstract_inverted_index.i.e. | 159 |
| abstract_inverted_index.long | 160, 169, 186 |
| abstract_inverted_index.main | 19 |
| abstract_inverted_index.most | 1 |
| abstract_inverted_index.over | 24 |
| abstract_inverted_index.rich | 117 |
| abstract_inverted_index.role | 121 |
| abstract_inverted_index.term | 162, 171, 188 |
| abstract_inverted_index.that | 105, 202 |
| abstract_inverted_index.this | 37 |
| abstract_inverted_index.unit | 196 |
| abstract_inverted_index.used | 90, 132, 137 |
| abstract_inverted_index.user | 68, 120 |
| abstract_inverted_index.with | 108 |
| abstract_inverted_index.Score | 213 |
| abstract_inverted_index.based | 66, 101, 122 |
| abstract_inverted_index.cause | 39 |
| abstract_inverted_index.chain | 182 |
| abstract_inverted_index.class | 177 |
| abstract_inverted_index.cyber | 3 |
| abstract_inverted_index.event | 118 |
| abstract_inverted_index.false | 113 |
| abstract_inverted_index.gated | 194 |
| abstract_inverted_index.model | 92 |
| abstract_inverted_index.multi | 184 |
| abstract_inverted_index.novel | 204 |
| abstract_inverted_index.other | 156 |
| abstract_inverted_index.paper | 47 |
| abstract_inverted_index.r4.2. | 146 |
| abstract_inverted_index.rate. | 115 |
| abstract_inverted_index.short | 161, 170, 187 |
| abstract_inverted_index.state | 185 |
| abstract_inverted_index.their | 28 |
| abstract_inverted_index.Markov | 181 |
| abstract_inverted_index.bypass | 31 |
| abstract_inverted_index.checks | 33 |
| abstract_inverted_index.damage | 41 |
| abstract_inverted_index.detect | 83 |
| abstract_inverted_index.during | 94 |
| abstract_inverted_index.events | 73 |
| abstract_inverted_index.memory | 189 |
| abstract_inverted_index.model, | 183 |
| abstract_inverted_index.neural | 165, 174, 192 |
| abstract_inverted_index.normal | 63 |
| abstract_inverted_index.phase. | 97 |
| abstract_inverted_index.proved | 201 |
| abstract_inverted_index.random | 167 |
| abstract_inverted_index.remain | 35 |
| abstract_inverted_index.series | 71 |
| abstract_inverted_index.threat | 51, 144 |
| abstract_inverted_index.users. | 57 |
| abstract_inverted_index.vector | 179 |
| abstract_inverted_index.Memory- | 163 |
| abstract_inverted_index.ability | 29 |
| abstract_inverted_index.assets. | 45 |
| abstract_inverted_index.attacks | 4 |
| abstract_inverted_index.dataset | 136, 145 |
| abstract_inverted_index.detects | 106 |
| abstract_inverted_index.events, | 127 |
| abstract_inverted_index.feature | 79, 87, 123 |
| abstract_inverted_index.focuses | 48 |
| abstract_inverted_index.forest, | 168 |
| abstract_inverted_index.greater | 109 |
| abstract_inverted_index.insider | 21, 50, 143 |
| abstract_inverted_index.malware | 13 |
| abstract_inverted_index.memory- | 172 |
| abstract_inverted_index.serious | 40 |
| abstract_inverted_index.support | 178 |
| abstract_inverted_index.through | 53 |
| abstract_inverted_index.trusted | 16 |
| abstract_inverted_index.usually | 6 |
| abstract_inverted_index.vectors | 88 |
| abstract_inverted_index.& | 190, 197 |
| abstract_inverted_index.Selected | 86 |
| abstract_inverted_index.accuracy | 110 |
| abstract_inverted_index.analysis | 55 |
| abstract_inverted_index.analyzed | 77 |
| abstract_inverted_index.approach | 102, 205 |
| abstract_inverted_index.behavior | 59 |
| abstract_inverted_index.compared | 154 |
| abstract_inverted_index.elements | 26 |
| abstract_inverted_index.external | 25 |
| abstract_inverted_index.insiders | 107 |
| abstract_inverted_index.learning | 100 |
| abstract_inverted_index.machine, | 180 |
| abstract_inverted_index.network, | 166, 175, 193 |
| abstract_inverted_index.positive | 114 |
| abstract_inverted_index.produces | 206 |
| abstract_inverted_index.proposed | 104, 150 |
| abstract_inverted_index.security | 32 |
| abstract_inverted_index.training | 93 |
| abstract_inverted_index.activity. | 69 |
| abstract_inverted_index.advantage | 20 |
| abstract_inverted_index.algorithm | 151 |
| abstract_inverted_index.attackers | 22 |
| abstract_inverted_index.behavior. | 85 |
| abstract_inverted_index.detection | 52 |
| abstract_inverted_index.insiders. | 17 |
| abstract_inverted_index.malicious | 10, 65 |
| abstract_inverted_index.outsiders | 11 |
| abstract_inverted_index.recurrent | 173, 195 |
| abstract_inverted_index.selection | 80 |
| abstract_inverted_index.skipgram. | 198 |
| abstract_inverted_index.synthetic | 142 |
| abstract_inverted_index.activities | 75 |
| abstract_inverted_index.behavioral | 54 |
| abstract_inverted_index.comparison | 200 |
| abstract_inverted_index.containing | 125 |
| abstract_inverted_index.detection. | 134 |
| abstract_inverted_index.originated | 8 |
| abstract_inverted_index.relatively | 207 |
| abstract_inverted_index.techniques | 158 |
| abstract_inverted_index.well-known | 157 |
| abstract_inverted_index.Performance | 147 |
| abstract_inverted_index.adversarial | 84 |
| abstract_inverted_index.categorized | 61 |
| abstract_inverted_index.detrimental | 2 |
| abstract_inverted_index.efficiently | 82 |
| abstract_inverted_index.Logon/Logoff | 126 |
| abstract_inverted_index.(94%). | 214 |
| abstract_inverted_index.convolutional | 164, 191 |
| abstract_inverted_index.undiscovered, | 36 |
| abstract_inverted_index.implementation | 96 |
| abstract_inverted_index.organizational | 44 |
| abstract_inverted_index.User_role, | 128 |
| abstract_inverted_index.precision(97%) | 210 |
| abstract_inverted_index.Functional_unit | 129 |
| abstract_inverted_index.accuracy(90.60%), | 209 |
| cited_by_percentile_year.max | 100 |
| cited_by_percentile_year.min | 97 |
| corresponding_author_ids | https://openalex.org/A5032009749 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I929597975 |
| sustainable_development_goals[0].id | https://metadata.un.org/sdg/16 |
| sustainable_development_goals[0].score | 0.5099999904632568 |
| sustainable_development_goals[0].display_name | Peace, Justice and strong institutions |
| citation_normalized_percentile.value | 0.98650205 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |