Killing Two Birds with One Stone: Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior Sequence Article Swipe
J. S. Zhang
,
Kaifeng Huang
,
Yiheng Huang
,
Bihuan Chen
,
Ruisi Wang
,
Chong Wang
,
Xin Peng
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3705304
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.1145/3705304
Open source software (OSS) supply chain enlarges the attack surface of a software system, which makes package registries attractive targets for attacks. Recently, multiple package registries have received intensified attacks with malicious packages. Of those package registries, NPM and PyPI are two of the most severe victims. Existing malicious package detectors are developed with features from a list of packages of the same ecosystem and deployed within the same ecosystem exclusively, which is infeasible to utilize the knowledge of a new malicious NPM package detected recently to detect the new malicious package in PyPI. Moreover, existing detectors lack support to model malicious behavior of OSS packages in a sequential way. To address the two limitations, we propose a single detection model using malicious behavior sequence, named Cerebro , to detect malicious packages in NPM and PyPI. We curate a feature set based on a high-level abstraction of malicious behavior to enable multi-lingual knowledge fusing. We organize extracted features into a behavior sequence to model sequential malicious behavior. We fine-tune the pre-trained language model to understand the semantics of malicious behavior. Extensive evaluation has demonstrated the effectiveness of Cerebro over the state-of-the-art as well as the practically acceptable efficiency. Cerebro has detected 683 and 799 new malicious packages in PyPI and NPM, and received 707 thank letters from the official PyPI and NPM teams.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1145/3705304
- https://dl.acm.org/doi/pdf/10.1145/3705304
- OA Status
- bronze
- Cited By
- 7
- References
- 60
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4404711543
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4404711543Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1145/3705304Digital Object Identifier
- Title
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Killing Two Birds with One Stone: Malicious Package Detection in NPM and PyPI using a Single Model of Malicious Behavior SequenceWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-26Full publication date if available
- Authors
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J. S. Zhang, Kaifeng Huang, Yiheng Huang, Bihuan Chen, Ruisi Wang, Chong Wang, Xin PengList of authors in order
- Landing page
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https://doi.org/10.1145/3705304Publisher landing page
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https://dl.acm.org/doi/pdf/10.1145/3705304Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://dl.acm.org/doi/pdf/10.1145/3705304Direct OA link when available
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Computer science, Sequence (biology), Computer security, Chemistry, BiochemistryTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 6, 2024: 1Per-year citation counts (last 5 years)
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60Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.for | 20 |
| abstract_inverted_index.has | 182, 199 |
| abstract_inverted_index.new | 80, 89, 204 |
| abstract_inverted_index.set | 140 |
| abstract_inverted_index.the | 7, 43, 61, 67, 76, 88, 112, 169, 175, 184, 189, 194, 217 |
| abstract_inverted_index.two | 41, 113 |
| abstract_inverted_index.NPM, | 210 |
| abstract_inverted_index.Open | 0 |
| abstract_inverted_index.PyPI | 39, 208, 219 |
| abstract_inverted_index.from | 55, 216 |
| abstract_inverted_index.have | 26 |
| abstract_inverted_index.into | 158 |
| abstract_inverted_index.lack | 97 |
| abstract_inverted_index.list | 57 |
| abstract_inverted_index.most | 44 |
| abstract_inverted_index.over | 188 |
| abstract_inverted_index.same | 62, 68 |
| abstract_inverted_index.way. | 109 |
| abstract_inverted_index.well | 192 |
| abstract_inverted_index.with | 30, 53 |
| abstract_inverted_index.(OSS) | 3 |
| abstract_inverted_index.PyPI. | 93, 135 |
| abstract_inverted_index.based | 141 |
| abstract_inverted_index.chain | 5 |
| abstract_inverted_index.makes | 15 |
| abstract_inverted_index.model | 100, 120, 163, 172 |
| abstract_inverted_index.named | 125 |
| abstract_inverted_index.thank | 214 |
| abstract_inverted_index.those | 34 |
| abstract_inverted_index.using | 121 |
| abstract_inverted_index.which | 14, 71 |
| abstract_inverted_index.attack | 8 |
| abstract_inverted_index.curate | 137 |
| abstract_inverted_index.detect | 87, 129 |
| abstract_inverted_index.enable | 150 |
| abstract_inverted_index.severe | 45 |
| abstract_inverted_index.single | 118 |
| abstract_inverted_index.source | 1 |
| abstract_inverted_index.supply | 4 |
| abstract_inverted_index.teams. | 222 |
| abstract_inverted_index.within | 66 |
| abstract_inverted_index.Cerebro | 126, 187, 198 |
| abstract_inverted_index.address | 111 |
| abstract_inverted_index.attacks | 29 |
| abstract_inverted_index.feature | 139 |
| abstract_inverted_index.fusing. | 153 |
| abstract_inverted_index.letters | 215 |
| abstract_inverted_index.package | 16, 24, 35, 49, 83, 91 |
| abstract_inverted_index.propose | 116 |
| abstract_inverted_index.support | 98 |
| abstract_inverted_index.surface | 9 |
| abstract_inverted_index.system, | 13 |
| abstract_inverted_index.targets | 19 |
| abstract_inverted_index.utilize | 75 |
| abstract_inverted_index.Existing | 47 |
| abstract_inverted_index.attacks. | 21 |
| abstract_inverted_index.behavior | 102, 123, 148, 160 |
| abstract_inverted_index.deployed | 65 |
| abstract_inverted_index.detected | 84, 200 |
| abstract_inverted_index.enlarges | 6 |
| abstract_inverted_index.existing | 95 |
| abstract_inverted_index.features | 54, 157 |
| abstract_inverted_index.language | 171 |
| abstract_inverted_index.multiple | 23 |
| abstract_inverted_index.official | 218 |
| abstract_inverted_index.organize | 155 |
| abstract_inverted_index.packages | 59, 105, 131, 206 |
| abstract_inverted_index.received | 27, 212 |
| abstract_inverted_index.recently | 85 |
| abstract_inverted_index.sequence | 161 |
| abstract_inverted_index.software | 2, 12 |
| abstract_inverted_index.victims. | 46 |
| abstract_inverted_index.Extensive | 180 |
| abstract_inverted_index.Moreover, | 94 |
| abstract_inverted_index.Recently, | 22 |
| abstract_inverted_index.behavior. | 166, 179 |
| abstract_inverted_index.detection | 119 |
| abstract_inverted_index.detectors | 50, 96 |
| abstract_inverted_index.developed | 52 |
| abstract_inverted_index.ecosystem | 63, 69 |
| abstract_inverted_index.extracted | 156 |
| abstract_inverted_index.fine-tune | 168 |
| abstract_inverted_index.knowledge | 77, 152 |
| abstract_inverted_index.malicious | 31, 48, 81, 90, 101, 122, 130, 147, 165, 178, 205 |
| abstract_inverted_index.packages. | 32 |
| abstract_inverted_index.semantics | 176 |
| abstract_inverted_index.sequence, | 124 |
| abstract_inverted_index.acceptable | 196 |
| abstract_inverted_index.attractive | 18 |
| abstract_inverted_index.evaluation | 181 |
| abstract_inverted_index.high-level | 144 |
| abstract_inverted_index.infeasible | 73 |
| abstract_inverted_index.registries | 17, 25 |
| abstract_inverted_index.sequential | 108, 164 |
| abstract_inverted_index.understand | 174 |
| abstract_inverted_index.abstraction | 145 |
| abstract_inverted_index.efficiency. | 197 |
| abstract_inverted_index.intensified | 28 |
| abstract_inverted_index.practically | 195 |
| abstract_inverted_index.pre-trained | 170 |
| abstract_inverted_index.registries, | 36 |
| abstract_inverted_index.demonstrated | 183 |
| abstract_inverted_index.exclusively, | 70 |
| abstract_inverted_index.limitations, | 114 |
| abstract_inverted_index.effectiveness | 185 |
| abstract_inverted_index.multi-lingual | 151 |
| abstract_inverted_index.state-of-the-art | 190 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.93893934 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |