Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection System Article Swipe
Azriel Henry
,
Sunil Gautam
,
Samrat Khanna
,
Khaled M. Rabie
,
Thokozani Shongwe
,
Pronaya Bhattacharya
,
Bhisham Sharma
,
S. Chowdhury
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23020890
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.3390/s23020890
Recently, with the massive growth of IoT devices, the attack surfaces have also intensified. Thus, cybersecurity has become a critical component to protect organizational boundaries. In networks, Intrusion Detection Systems (IDSs) are employed to raise critical flags during network management. One aspect is malicious traffic identification, where zero-day attack detection is a critical problem of study. Current approaches are aligned towards deep learning (DL) methods for IDSs, but the success of the DL mechanism depends on the feature learning process, which is an open challenge. Thus, in this paper, the authors propose a technique which combines both CNN, and GRU, where different CNN–GRU combination sequences are presented to optimize the network parameters. In the simulation, the authors used the CICIDS-2017 benchmark dataset and used metrics such as precision, recall, False Positive Rate (FPR), True Positive Rate (TRP), and other aligned metrics. The results suggest a significant improvement, where many network attacks are detected with an accuracy of 98.73%, and an FPR rate of 0.075. We also performed a comparative analysis with other existing techniques, and the obtained results indicate the efficacy of the proposed IDS scheme in real cybersecurity setups.
Related Topics
Concepts
Intrusion detection system
Benchmark (surveying)
Computer science
False positive rate
Deep learning
Artificial intelligence
Data mining
Feature (linguistics)
Process (computing)
Scheme (mathematics)
Machine learning
Identification (biology)
Constant false alarm rate
Pattern recognition (psychology)
Botany
Philosophy
Mathematical analysis
Linguistics
Geodesy
Geography
Mathematics
Operating system
Biology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/s23020890
- https://www.mdpi.com/1424-8220/23/2/890/pdf?version=1673515238
- OA Status
- gold
- Cited By
- 96
- References
- 46
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4315781229
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4315781229Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/s23020890Digital Object Identifier
- Title
-
Composition of Hybrid Deep Learning Model and Feature Optimization for Intrusion Detection SystemWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2023Year of publication
- Publication date
-
2023-01-12Full publication date if available
- Authors
-
Azriel Henry, Sunil Gautam, Samrat Khanna, Khaled M. Rabie, Thokozani Shongwe, Pronaya Bhattacharya, Bhisham Sharma, S. ChowdhuryList of authors in order
- Landing page
-
https://doi.org/10.3390/s23020890Publisher landing page
- PDF URL
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https://www.mdpi.com/1424-8220/23/2/890/pdf?version=1673515238Direct link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/1424-8220/23/2/890/pdf?version=1673515238Direct OA link when available
- Concepts
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Intrusion detection system, Benchmark (surveying), Computer science, False positive rate, Deep learning, Artificial intelligence, Data mining, Feature (linguistics), Process (computing), Scheme (mathematics), Machine learning, Identification (biology), Constant false alarm rate, Pattern recognition (psychology), Botany, Philosophy, Mathematical analysis, Linguistics, Geodesy, Geography, Mathematics, Operating system, BiologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
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96Total citation count in OpenAlex
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2025: 37, 2024: 37, 2023: 21, 2022: 1Per-year citation counts (last 5 years)
- References (count)
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46Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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