Knowledge Graph Based Large Scale Network Security Threat Detection Techniques
This paper constructs a detection technique for large-scale network security threats based on a knowledge graph, extracts the attack features of network security threats using feature template FT, and combines the CNN layer, BiLSTM layer and CRF layer to establish FT-CNN-BiLSTM-CRF large-scale netwo...
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| Format: | Article |
| Language: | English |
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Sciendo
2024-01-01
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| Series: | Applied Mathematics and Nonlinear Sciences |
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| Online Access: | https://doi.org/10.2478/amns-2024-0046 |
| _version_ | 1797303160859000832 |
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| author | Hu Zhifeng |
| author_facet | Hu Zhifeng |
| author_sort | Hu Zhifeng |
| collection | DOAJ |
| description | This paper constructs a detection technique for large-scale network security threats based on a knowledge graph, extracts the attack features of network security threats using feature template FT, and combines the CNN layer, BiLSTM layer and CRF layer to establish FT-CNN-BiLSTM-CRF large-scale network security threat detection technique. Network security threat performance evaluation experiments and multi-step attack experiments have verified the detection capability of this paper's method. The recall rate of the method built in this paper in detecting malicious data is about 62.39%, the average F1-Score for normal and malicious traffic detection is 0.7482, and the anomaly score for normal traffic detection is almost 0. The detection performance of this paper's method for multi-step network attacks is superior to that of other methods, and it is capable of detecting malicious attacks quickly. Experiments have proved that the method constructed in this paper can meet the requirements of detection capability and efficiency in large-scale network security threats and has high feasibility and application value. |
| first_indexed | 2024-03-07T23:48:57Z |
| format | Article |
| id | doaj.art-39565c9841ee4675b2e8937223cc5a64 |
| institution | Directory Open Access Journal |
| issn | 2444-8656 |
| language | English |
| last_indexed | 2024-03-07T23:48:57Z |
| publishDate | 2024-01-01 |
| publisher | Sciendo |
| record_format | Article |
| series | Applied Mathematics and Nonlinear Sciences |
| spelling | doaj.art-39565c9841ee4675b2e8937223cc5a642024-02-19T09:03:34ZengSciendoApplied Mathematics and Nonlinear Sciences2444-86562024-01-019110.2478/amns-2024-0046Knowledge Graph Based Large Scale Network Security Threat Detection TechniquesHu Zhifeng01Modern Education Technology Center, Wuhan Business University, Wuhan, Hubei, 430056, China.This paper constructs a detection technique for large-scale network security threats based on a knowledge graph, extracts the attack features of network security threats using feature template FT, and combines the CNN layer, BiLSTM layer and CRF layer to establish FT-CNN-BiLSTM-CRF large-scale network security threat detection technique. Network security threat performance evaluation experiments and multi-step attack experiments have verified the detection capability of this paper's method. The recall rate of the method built in this paper in detecting malicious data is about 62.39%, the average F1-Score for normal and malicious traffic detection is 0.7482, and the anomaly score for normal traffic detection is almost 0. The detection performance of this paper's method for multi-step network attacks is superior to that of other methods, and it is capable of detecting malicious attacks quickly. Experiments have proved that the method constructed in this paper can meet the requirements of detection capability and efficiency in large-scale network security threats and has high feasibility and application value.https://doi.org/10.2478/amns-2024-0046knowledge graphnetwork security threatmulti-step attackanomaly score distributionft-cnnbilstm-crf05c82 |
| spellingShingle | Hu Zhifeng Knowledge Graph Based Large Scale Network Security Threat Detection Techniques Applied Mathematics and Nonlinear Sciences knowledge graph network security threat multi-step attack anomaly score distribution ft-cnnbilstm-crf 05c82 |
| title | Knowledge Graph Based Large Scale Network Security Threat Detection Techniques |
| title_full | Knowledge Graph Based Large Scale Network Security Threat Detection Techniques |
| title_fullStr | Knowledge Graph Based Large Scale Network Security Threat Detection Techniques |
| title_full_unstemmed | Knowledge Graph Based Large Scale Network Security Threat Detection Techniques |
| title_short | Knowledge Graph Based Large Scale Network Security Threat Detection Techniques |
| title_sort | knowledge graph based large scale network security threat detection techniques |
| topic | knowledge graph network security threat multi-step attack anomaly score distribution ft-cnnbilstm-crf 05c82 |
| url | https://doi.org/10.2478/amns-2024-0046 |
| work_keys_str_mv | AT huzhifeng knowledgegraphbasedlargescalenetworksecuritythreatdetectiontechniques |