A Threat Flow Classification Method Based on Feature Fusion and Weighted Attention Article Swipe
Yanli Tu
,
Yu Wu
,
Feng Shi
,
Jiaxin Ren
,
Han Shen
,
Yuzhen Zhang
,
Qianwen Liu
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/dac.70092
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1002/dac.70092
With the rapid development of the Internet, the increasing network traffic has brought a greater burden to network management. At the same time, abnormal traffic attacks on network equipment pose significant security risks. Classifying network traffic on network devices is an important way to protect information security. However, due to the vast amount and high‐dimensional attributes of traffic data, existing traffic classification models are mostly complex in structure, with a large number of parameters, making them difficult to apply to network devices with limited computing resources. Hence, this paper proposes a threat flow classification method based on feature fusion and weighted attention (FFWCA) to save storage and computing costs while ensuring model accuracy. Firstly, it constructs a lightweight multiscale feature extraction module by dilated convolutions and convolutions to fuse features of different receptive field sizes. Then, it constructs an inverted residual structure embedded with a weighted coordinate attention mechanism, to extract accurate features for traffic classification and mitigate the gradient vanishing phenomenon brought by the lightweight model, a chapter. Finally, it constructs a fully convolutional structure classifier to reduce the computational overhead brought by the fully connected layer classifier while ensuring the model's nonlinear complexity. FFWCA significantly reduces the model's computational overhead and the number of parameters by incorporating a lightweight multiscale feature extraction module and a weighted coordinate attention mechanism, so that it can be efficiently deployed on network devices with constrain computing resources. Experiments on two public network traffic datasets, Bot‐IoT and USTC‐TFC2016, demonstrate that FFWCA achieves a balance between performance and lightweight and is suitable for edge computing and IoT devices.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1002/dac.70092
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/dac.70092
- OA Status
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- References
- 30
- Related Works
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- OpenAlex ID
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Raw OpenAlex JSON
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https://openalex.org/W4410214540Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1002/dac.70092Digital Object Identifier
- Title
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A Threat Flow Classification Method Based on Feature Fusion and Weighted AttentionWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-05-08Full publication date if available
- Authors
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Yanli Tu, Yu Wu, Feng Shi, Jiaxin Ren, Han Shen, Yuzhen Zhang, Qianwen LiuList of authors in order
- Landing page
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https://doi.org/10.1002/dac.70092Publisher landing page
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/dac.70092Direct 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://onlinelibrary.wiley.com/doi/pdfdirect/10.1002/dac.70092Direct OA link when available
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Computer science, Feature (linguistics), Artificial intelligence, Pattern recognition (psychology), Fusion, Data mining, Linguistics, PhilosophyTop concepts (fields/topics) attached by OpenAlex
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0Total citation count in OpenAlex
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.to | 17, 44, 50, 78, 80, 104, 128, 150, 178 |
| abstract_inverted_index.IoT | 263 |
| abstract_inverted_index.and | 54, 100, 107, 126, 157, 203, 216, 244, 254, 256, 262 |
| abstract_inverted_index.are | 64 |
| abstract_inverted_index.can | 225 |
| abstract_inverted_index.due | 49 |
| abstract_inverted_index.for | 154, 259 |
| abstract_inverted_index.has | 12 |
| abstract_inverted_index.the | 2, 6, 8, 21, 51, 159, 165, 180, 185, 192, 199, 204 |
| abstract_inverted_index.two | 238 |
| abstract_inverted_index.way | 43 |
| abstract_inverted_index.With | 1 |
| abstract_inverted_index.edge | 260 |
| abstract_inverted_index.flow | 93 |
| abstract_inverted_index.fuse | 129 |
| abstract_inverted_index.pose | 30 |
| abstract_inverted_index.same | 22 |
| abstract_inverted_index.save | 105 |
| abstract_inverted_index.that | 223, 247 |
| abstract_inverted_index.them | 76 |
| abstract_inverted_index.this | 88 |
| abstract_inverted_index.vast | 52 |
| abstract_inverted_index.with | 69, 83, 144, 232 |
| abstract_inverted_index.FFWCA | 196, 248 |
| abstract_inverted_index.Then, | 136 |
| abstract_inverted_index.apply | 79 |
| abstract_inverted_index.based | 96 |
| abstract_inverted_index.costs | 109 |
| abstract_inverted_index.data, | 59 |
| abstract_inverted_index.field | 134 |
| abstract_inverted_index.fully | 174, 186 |
| abstract_inverted_index.large | 71 |
| abstract_inverted_index.layer | 188 |
| abstract_inverted_index.model | 112 |
| abstract_inverted_index.paper | 89 |
| abstract_inverted_index.rapid | 3 |
| abstract_inverted_index.time, | 23 |
| abstract_inverted_index.while | 110, 190 |
| abstract_inverted_index.Hence, | 87 |
| abstract_inverted_index.amount | 53 |
| abstract_inverted_index.burden | 16 |
| abstract_inverted_index.fusion | 99 |
| abstract_inverted_index.making | 75 |
| abstract_inverted_index.method | 95 |
| abstract_inverted_index.model, | 167 |
| abstract_inverted_index.models | 63 |
| abstract_inverted_index.module | 122, 215 |
| abstract_inverted_index.mostly | 65 |
| abstract_inverted_index.number | 72, 205 |
| abstract_inverted_index.public | 239 |
| abstract_inverted_index.reduce | 179 |
| abstract_inverted_index.risks. | 33 |
| abstract_inverted_index.sizes. | 135 |
| abstract_inverted_index.threat | 92 |
| abstract_inverted_index.(FFWCA) | 103 |
| abstract_inverted_index.attacks | 26 |
| abstract_inverted_index.balance | 251 |
| abstract_inverted_index.between | 252 |
| abstract_inverted_index.brought | 13, 163, 183 |
| abstract_inverted_index.complex | 66 |
| abstract_inverted_index.devices | 39, 82, 231 |
| abstract_inverted_index.dilated | 124 |
| abstract_inverted_index.extract | 151 |
| abstract_inverted_index.feature | 98, 120, 213 |
| abstract_inverted_index.greater | 15 |
| abstract_inverted_index.limited | 84 |
| abstract_inverted_index.model's | 193, 200 |
| abstract_inverted_index.network | 10, 18, 28, 35, 38, 81, 230, 240 |
| abstract_inverted_index.protect | 45 |
| abstract_inverted_index.reduces | 198 |
| abstract_inverted_index.storage | 106 |
| abstract_inverted_index.traffic | 11, 25, 36, 58, 61, 155, 241 |
| abstract_inverted_index.ABSTRACT | 0 |
| abstract_inverted_index.Finally, | 170 |
| abstract_inverted_index.Firstly, | 114 |
| abstract_inverted_index.However, | 48 |
| abstract_inverted_index.abnormal | 24 |
| abstract_inverted_index.accurate | 152 |
| abstract_inverted_index.achieves | 249 |
| abstract_inverted_index.chapter. | 169 |
| abstract_inverted_index.deployed | 228 |
| abstract_inverted_index.devices. | 264 |
| abstract_inverted_index.embedded | 143 |
| abstract_inverted_index.ensuring | 111, 191 |
| abstract_inverted_index.existing | 60 |
| abstract_inverted_index.features | 130, 153 |
| abstract_inverted_index.gradient | 160 |
| abstract_inverted_index.inverted | 140 |
| abstract_inverted_index.mitigate | 158 |
| abstract_inverted_index.overhead | 182, 202 |
| abstract_inverted_index.proposes | 90 |
| abstract_inverted_index.residual | 141 |
| abstract_inverted_index.security | 32 |
| abstract_inverted_index.suitable | 258 |
| abstract_inverted_index.weighted | 101, 146, 218 |
| abstract_inverted_index.Bot‐IoT | 243 |
| abstract_inverted_index.Internet, | 7 |
| abstract_inverted_index.accuracy. | 113 |
| abstract_inverted_index.attention | 102, 148, 220 |
| abstract_inverted_index.computing | 85, 108, 234, 261 |
| abstract_inverted_index.connected | 187 |
| abstract_inverted_index.constrain | 233 |
| abstract_inverted_index.datasets, | 242 |
| abstract_inverted_index.different | 132 |
| abstract_inverted_index.difficult | 77 |
| abstract_inverted_index.equipment | 29 |
| abstract_inverted_index.important | 42 |
| abstract_inverted_index.nonlinear | 194 |
| abstract_inverted_index.receptive | 133 |
| abstract_inverted_index.security. | 47 |
| abstract_inverted_index.structure | 142, 176 |
| abstract_inverted_index.vanishing | 161 |
| abstract_inverted_index.attributes | 56 |
| abstract_inverted_index.classifier | 177, 189 |
| abstract_inverted_index.constructs | 116, 138, 172 |
| abstract_inverted_index.coordinate | 147, 219 |
| abstract_inverted_index.extraction | 121, 214 |
| abstract_inverted_index.increasing | 9 |
| abstract_inverted_index.mechanism, | 149, 221 |
| abstract_inverted_index.multiscale | 119, 212 |
| abstract_inverted_index.parameters | 207 |
| abstract_inverted_index.phenomenon | 162 |
| abstract_inverted_index.resources. | 86, 235 |
| abstract_inverted_index.structure, | 68 |
| abstract_inverted_index.Classifying | 34 |
| abstract_inverted_index.Experiments | 236 |
| abstract_inverted_index.complexity. | 195 |
| abstract_inverted_index.demonstrate | 246 |
| abstract_inverted_index.development | 4 |
| abstract_inverted_index.efficiently | 227 |
| abstract_inverted_index.information | 46 |
| abstract_inverted_index.lightweight | 118, 166, 211, 255 |
| abstract_inverted_index.management. | 19 |
| abstract_inverted_index.parameters, | 74 |
| abstract_inverted_index.performance | 253 |
| abstract_inverted_index.significant | 31 |
| abstract_inverted_index.convolutions | 125, 127 |
| abstract_inverted_index.computational | 181, 201 |
| abstract_inverted_index.convolutional | 175 |
| abstract_inverted_index.incorporating | 209 |
| abstract_inverted_index.significantly | 197 |
| abstract_inverted_index.classification | 62, 94, 156 |
| abstract_inverted_index.USTC‐TFC2016, | 245 |
| abstract_inverted_index.high‐dimensional | 55 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 7 |
| citation_normalized_percentile.value | 0.17193076 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |