Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks
We suggest a method of effectively detecting and classifying network traffic attacks by visualizing their IP (Internet protocol) addresses and ports and clustering the visualized ports based on their variance. The proposed approach first visualizes the IP addresses and ports of the senders and recei...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Hindawi - SAGE Publishing
2014-04-01
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| Series: | International Journal of Distributed Sensor Networks |
| Online Access: | https://doi.org/10.1155/2014/438468 |
| _version_ | 1797709016082677760 |
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| author | Seok-Woo Jang Gye-Young Kim Siwoo Byun |
| author_facet | Seok-Woo Jang Gye-Young Kim Siwoo Byun |
| author_sort | Seok-Woo Jang |
| collection | DOAJ |
| description | We suggest a method of effectively detecting and classifying network traffic attacks by visualizing their IP (Internet protocol) addresses and ports and clustering the visualized ports based on their variance. The proposed approach first visualizes the IP addresses and ports of the senders and receivers into two-dimensional images. The method then analyzes the visualized images and extracts linear and/or high brightness patterns, which represent traffic attacks. Subsequently, it groups the ports using an improved clustering algorithm, allowing an artificial neural network to learn the extracted features and to automatically detect and classify normal traffic data, DDoS attacks, DoS attacks, or Internet Worms. The experiments conducted in this work prove that our suggested clustering-based algorithm effectively detects various traffic attacks. |
| first_indexed | 2024-03-12T06:30:12Z |
| format | Article |
| id | doaj.art-8b8ade75294a44f68061fdaf5bab13cd |
| institution | Directory Open Access Journal |
| issn | 1550-1477 |
| language | English |
| last_indexed | 2024-03-12T06:30:12Z |
| publishDate | 2014-04-01 |
| publisher | Hindawi - SAGE Publishing |
| record_format | Article |
| series | International Journal of Distributed Sensor Networks |
| spelling | doaj.art-8b8ade75294a44f68061fdaf5bab13cd2023-09-03T01:39:59ZengHindawi - SAGE PublishingInternational Journal of Distributed Sensor Networks1550-14772014-04-011010.1155/2014/438468438468Clustering-Based Pattern Abnormality Detection in Distributed Sensor NetworksSeok-Woo Jang0Gye-Young Kim1Siwoo Byun2 Department of Digital Media, Anyang University, 708-113 Anyang 5-dong, Manan-gu, Anyang 430-714, Republic of Korea School of Computing, Soongsil University, 369 Sangdo-ro, Dongjak-gu, Seoul 156-743, Republic of Korea Department of Digital Media, Anyang University, 708-113 Anyang 5-dong, Manan-gu, Anyang 430-714, Republic of KoreaWe suggest a method of effectively detecting and classifying network traffic attacks by visualizing their IP (Internet protocol) addresses and ports and clustering the visualized ports based on their variance. The proposed approach first visualizes the IP addresses and ports of the senders and receivers into two-dimensional images. The method then analyzes the visualized images and extracts linear and/or high brightness patterns, which represent traffic attacks. Subsequently, it groups the ports using an improved clustering algorithm, allowing an artificial neural network to learn the extracted features and to automatically detect and classify normal traffic data, DDoS attacks, DoS attacks, or Internet Worms. The experiments conducted in this work prove that our suggested clustering-based algorithm effectively detects various traffic attacks.https://doi.org/10.1155/2014/438468 |
| spellingShingle | Seok-Woo Jang Gye-Young Kim Siwoo Byun Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks International Journal of Distributed Sensor Networks |
| title | Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks |
| title_full | Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks |
| title_fullStr | Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks |
| title_full_unstemmed | Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks |
| title_short | Clustering-Based Pattern Abnormality Detection in Distributed Sensor Networks |
| title_sort | clustering based pattern abnormality detection in distributed sensor networks |
| url | https://doi.org/10.1155/2014/438468 |
| work_keys_str_mv | AT seokwoojang clusteringbasedpatternabnormalitydetectionindistributedsensornetworks AT gyeyoungkim clusteringbasedpatternabnormalitydetectionindistributedsensornetworks AT siwoobyun clusteringbasedpatternabnormalitydetectionindistributedsensornetworks |