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Efficientnetv2-RegNet: an effective deep learning framework for secure SDN based IOT network

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Abstract

Traditional network administration required manual programming of routing policies and related parameters on specific routers and switches, which was expensive. Therefore, software-defined networking (SDN) technology has been introduced, which has boosted flexibility and decreased hardware development costs by centralizing network management. Since intrusion detection is vital in the SDN environment, this centralized architecture makes information security vulnerable to network threats. To evaluate and recognize these attacks, many researchers have recently adopted cutting-edge approaches like machine learning. However, most of these methods are not very accurate and scalable. To address this issue, this paper proposes an EfficientNetV2-RegNet-based effective deep learning technique. It effectively extracted the network features and classified the intrusions in SDN-based IoT (Internet of Things). Afterwards, an effective mitigation process was performed by a remote SDN controller to mitigate the assaults and reconfigure the network resources for trusted network hosts. Furthermore, the Conditional Generative Adversarial Network (CGAN) based data augmentation approach efficiently tackles the data imbalance issue. The most recent realistic datasets, named InSDN and IoT-23, were utilized to train and assess the presented framework to validate its efficiency. The results of the experiments demonstrated that the suggested system surpassed competitors in identifying various attack types and achieved 99.53 and 99.56% accuracy for IoT-23 and InSDN datasets, correspondingly.

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Acknowledgements

We declare that this manuscript is original, has not been published before, and is not currently being considered for publication elsewhere.

Author information

Authors and Affiliations

  1. Department of Computer Science and Engineering (Data Science), New Horizon College of Engineering, Bengaluru, Karnataka, 560103, India

    Baswaraju Swathi

  2. Department of Computer Science and Engineering, University College of Engineering Narasaraopet, JNTU, Kakinada, Andhra Pradesh, 522616, India

    Soma Sekhar Kolisetty

  3. Department of Computer Science and Engineering, GST, Gitam (Deemed to Be University), Visakhapatnam, Andhra Pradesh, 530045, India

    G Venkata Sivanarayana

  4. School of Computer and Information Sciences (SCIS), University of Hyderabad, Gachibowly, Hyderabad, 500046, India

    Srinivasa Rao Battula

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  1. Baswaraju Swathi
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  3. G Venkata Sivanarayana
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  4. Srinivasa Rao Battula
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The author confirms sole responsibility for the following: study conception and design, data collection, analysis and interpretation of results, and manuscript preparation.

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Correspondence to Srinivasa Rao Battula.

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Appendices

Appendix

Appendix-I

InSDN dataset

IoT-23 dataset

Attack types

Original Samples

After removing duplicate record

Newly generated samples

After augmentation

Attack types

Original Samples

After removing duplicate records

Newly generated samples

After augmentation

Normal

68,424

–

0

68,424

Benign

30,858,735

2,113,860

To maintain dataset balance, certain samples are eliminated

68,424

DDoS

121,942

–

To maintain dataset balance, certain samples are eliminated

68,424

DDoS

19,538,713

3,643,225

To maintain dataset balance, certain samples are eliminated

68,424

DoS

 

–

14,808

68,424

Mirai

9400

9400

59,024

68,424

Probe

 

–

To maintain dataset balance, certain samples are eliminated

68,424

Okiru

60,990,711

234,942

To maintain dataset balance, certain samples are eliminated

68,424

Botnet

164

–

68,260

68,424

Torii

30

30

68,394

68,424

Password

1405

–

67,019

68,424

PartofHorizontalPortScan

213,853,817

369,525

To maintain dataset balance, certain samples are eliminated

68,424

Web-attack

192

–

 

68,424

Heart beat

34,518

22,982

45,442

68,424

U2R

17

–

68,407

68,424

FileDownload

71

71

68,353

68,424

     

Command and control

21,995

18,939

49,485

68,424

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Swathi, B., Kolisetty, S.S., Sivanarayana, G.V. et al. Efficientnetv2-RegNet: an effective deep learning framework for secure SDN based IOT network. Cluster Comput 27, 10653–10670 (2024). https://doi.org/10.1007/s10586-024-04498-0

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