Deep Learning for Proactive Network Monitoring and Security Protection
The work presented in this paper deals with a proactive network monitoring for security and protection of computing infrastructures. We provide an exploitation of an intelligent module, in the form of a as a machine learning application using deep learning modeling, in order to enhance functionality...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
IEEE
2020-01-01
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| Series: | IEEE Access |
| Subjects: | |
| Online Access: | https://ieeexplore.ieee.org/document/8966259/ |
| _version_ | 1818921240419631104 |
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| author | Giang Nguyen Stefan Dlugolinsky Viet Tran Alvaro Lopez Garcia |
| author_facet | Giang Nguyen Stefan Dlugolinsky Viet Tran Alvaro Lopez Garcia |
| author_sort | Giang Nguyen |
| collection | DOAJ |
| description | The work presented in this paper deals with a proactive network monitoring for security and protection of computing infrastructures. We provide an exploitation of an intelligent module, in the form of a as a machine learning application using deep learning modeling, in order to enhance functionality of intrusion detection system supervising network traffic flows. Currently, intrusion detection systems work well for network monitoring in near real-time and they effectively deal with threats in a reactive way. Deep learning is the emerging generation of artificial intelligence techniques and one of the most promising candidates for intelligence integration into traditional solutions leading to quality improvement of the original solutions. The work presented in this paper faces the challenge of cooperation between deep learning techniques and large-scale data processing. The outcomes obtained from extensive and careful experiments show the applicability and feasibility of simultaneously modelled multiple monitoring channels using deep learning techniques. The proper joining of deep learning modelling with scalable data preprocessing ensures high quality and stability of model performance in dynamic and fast-changing environments such as network traffic flow monitoring. |
| first_indexed | 2024-12-20T01:34:30Z |
| format | Article |
| id | doaj.art-e4fbe6e91ec84cf18078e1249eeebf8f |
| institution | Directory Open Access Journal |
| issn | 2169-3536 |
| language | English |
| last_indexed | 2024-12-20T01:34:30Z |
| publishDate | 2020-01-01 |
| publisher | IEEE |
| record_format | Article |
| series | IEEE Access |
| spelling | doaj.art-e4fbe6e91ec84cf18078e1249eeebf8f2022-12-21T19:58:03ZengIEEEIEEE Access2169-35362020-01-018196961971610.1109/ACCESS.2020.29687188966259Deep Learning for Proactive Network Monitoring and Security ProtectionGiang Nguyen0https://orcid.org/0000-0002-6769-0195Stefan Dlugolinsky1https://orcid.org/0000-0002-4424-4221Viet Tran2https://orcid.org/0000-0002-4852-1601Alvaro Lopez Garcia3https://orcid.org/0000-0002-0013-4602Institute of Informatics, Slovak Academy of Sciences (IISAS), Bratislava, SlovakiaInstitute of Informatics, Slovak Academy of Sciences (IISAS), Bratislava, SlovakiaInstitute of Informatics, Slovak Academy of Sciences (IISAS), Bratislava, SlovakiaInstituto de Fisica de Cantabria (IFCA, CSIC-UC), Santander, SpainThe work presented in this paper deals with a proactive network monitoring for security and protection of computing infrastructures. We provide an exploitation of an intelligent module, in the form of a as a machine learning application using deep learning modeling, in order to enhance functionality of intrusion detection system supervising network traffic flows. Currently, intrusion detection systems work well for network monitoring in near real-time and they effectively deal with threats in a reactive way. Deep learning is the emerging generation of artificial intelligence techniques and one of the most promising candidates for intelligence integration into traditional solutions leading to quality improvement of the original solutions. The work presented in this paper faces the challenge of cooperation between deep learning techniques and large-scale data processing. The outcomes obtained from extensive and careful experiments show the applicability and feasibility of simultaneously modelled multiple monitoring channels using deep learning techniques. The proper joining of deep learning modelling with scalable data preprocessing ensures high quality and stability of model performance in dynamic and fast-changing environments such as network traffic flow monitoring.https://ieeexplore.ieee.org/document/8966259/Deep learningproactive forecastingnetwork monitoringcyber securityanomaly detectionneural machine translation |
| spellingShingle | Giang Nguyen Stefan Dlugolinsky Viet Tran Alvaro Lopez Garcia Deep Learning for Proactive Network Monitoring and Security Protection IEEE Access Deep learning proactive forecasting network monitoring cyber security anomaly detection neural machine translation |
| title | Deep Learning for Proactive Network Monitoring and Security Protection |
| title_full | Deep Learning for Proactive Network Monitoring and Security Protection |
| title_fullStr | Deep Learning for Proactive Network Monitoring and Security Protection |
| title_full_unstemmed | Deep Learning for Proactive Network Monitoring and Security Protection |
| title_short | Deep Learning for Proactive Network Monitoring and Security Protection |
| title_sort | deep learning for proactive network monitoring and security protection |
| topic | Deep learning proactive forecasting network monitoring cyber security anomaly detection neural machine translation |
| url | https://ieeexplore.ieee.org/document/8966259/ |
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