ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection
Network intrusion detection systems can identify intrusion behavior in a network by analyzing network traffic data. It is challenging to detect a very small proportion of intrusion data from massive network traffic and identify the attack class in intrusion detection tasks. Many existing intrusion d...
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MDPI AG
2025-02-01
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| author | Bin Li Jie Li Mingyu Jia |
| author_facet | Bin Li Jie Li Mingyu Jia |
| author_sort | Bin Li |
| collection | DOAJ |
| description | Network intrusion detection systems can identify intrusion behavior in a network by analyzing network traffic data. It is challenging to detect a very small proportion of intrusion data from massive network traffic and identify the attack class in intrusion detection tasks. Many existing intrusion detection studies often fail to fully extract the spatial features of network traffic and make reasonable use of temporal features. In this paper, we propose ADFCNN-BiLSTM, a novel deep neural network for network intrusion detection. ADFCNN-BiLSTM uses deformable convolution and an attention mechanism to adaptively extract the spatial features of network traffic data, and it pays attention to the important features from both channel and spatial perspectives. It uses BiLSTM to mine the temporal features from the traffic data and employs the multi-head attention mechanism to allow the network to focus on the time-series information related to suspicious traffic. In addition, ADFCNN-BiLSTM addresses the issue of class imbalance during the training process at both the data level and algorithm level. We evaluated the proposed ADFCNN-BiLSTM on three standard datasets, i.e., NSL-KDD, UNSW-NB15, and CICDDoS2019. The experimental results show that ADFCNN-BiLSTM outperforms the state-of-the-art model in terms of accuracy, detection rate, and false-positive rate. |
| format | Article |
| id | doaj-art-47f93ef6932c417a9b6d9f593c5385bd |
| institution | OA Journals |
| issn | 1424-8220 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | MDPI AG |
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| series | Sensors |
| spelling | doaj-art-47f93ef6932c417a9b6d9f593c5385bd2025-08-20T02:06:15ZengMDPI AGSensors1424-82202025-02-01255138210.3390/s25051382ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion DetectionBin Li0Jie Li1Mingyu Jia2School of Computer Science, Northeast Electric Power University, Jilin 132012, ChinaSchool of Computer Science, Northeast Electric Power University, Jilin 132012, ChinaSchool of Computer Science, Northeast Electric Power University, Jilin 132012, ChinaNetwork intrusion detection systems can identify intrusion behavior in a network by analyzing network traffic data. It is challenging to detect a very small proportion of intrusion data from massive network traffic and identify the attack class in intrusion detection tasks. Many existing intrusion detection studies often fail to fully extract the spatial features of network traffic and make reasonable use of temporal features. In this paper, we propose ADFCNN-BiLSTM, a novel deep neural network for network intrusion detection. ADFCNN-BiLSTM uses deformable convolution and an attention mechanism to adaptively extract the spatial features of network traffic data, and it pays attention to the important features from both channel and spatial perspectives. It uses BiLSTM to mine the temporal features from the traffic data and employs the multi-head attention mechanism to allow the network to focus on the time-series information related to suspicious traffic. In addition, ADFCNN-BiLSTM addresses the issue of class imbalance during the training process at both the data level and algorithm level. We evaluated the proposed ADFCNN-BiLSTM on three standard datasets, i.e., NSL-KDD, UNSW-NB15, and CICDDoS2019. The experimental results show that ADFCNN-BiLSTM outperforms the state-of-the-art model in terms of accuracy, detection rate, and false-positive rate.https://www.mdpi.com/1424-8220/25/5/1382network intrusion detectionbidirectional long short-term memorydeformable convolutionattention mechanism |
| spellingShingle | Bin Li Jie Li Mingyu Jia ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection Sensors network intrusion detection bidirectional long short-term memory deformable convolution attention mechanism |
| title | ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection |
| title_full | ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection |
| title_fullStr | ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection |
| title_full_unstemmed | ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection |
| title_short | ADFCNN-BiLSTM: A Deep Neural Network Based on Attention and Deformable Convolution for Network Intrusion Detection |
| title_sort | adfcnn bilstm a deep neural network based on attention and deformable convolution for network intrusion detection |
| topic | network intrusion detection bidirectional long short-term memory deformable convolution attention mechanism |
| url | https://www.mdpi.com/1424-8220/25/5/1382 |
| work_keys_str_mv | AT binli adfcnnbilstmadeepneuralnetworkbasedonattentionanddeformableconvolutionfornetworkintrusiondetection AT jieli adfcnnbilstmadeepneuralnetworkbasedonattentionanddeformableconvolutionfornetworkintrusiondetection AT mingyujia adfcnnbilstmadeepneuralnetworkbasedonattentionanddeformableconvolutionfornetworkintrusiondetection |