Deep Learning Meets IoT: Hybrid Approaches for Botnet Detection

Year : 2025 | Volume : 12 | Issue : 01 | Page : 18 27
    By

    Sumit Kumar Soni,

  • Sreeja Nair,

  1. M Tech Scholar, Department of Computer Science, Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India
  2. HOD, Department of Computer Science, Oriental Institute of Science and Technology, Bhopal, Madhya Pradesh, India

Abstract

Rapid advancement in the Internet of Things (IoT) changed everything, making it possible for seamless interconnectivity of devices and altering data-driven decision processes. This study delves into the intersection of IoT with deep learning approaches and hybrid approaches for managing botnet in IoT systems, especially security, efficiency, and performance optimization. Leveraging deep learning models, for example, CNNs and RNNs, will help the network achieve more intrusion detection and data analysis. Additionally, this paper has further discussed how edge and cloud computing can be used for increasing latency and reducing the operational cost and improving data privacy. It also compares different approaches of hybrid security techniques to show how these effectively reduce vulnerabilities in an IoT device. The findings emphasize the potential of integrating deep learning with IoT to develop robust, efficient, and secure systems.

Keywords: IoT, deep learning, edge computing, hybrid techniques, intrusion detection and data privacy

[This article belongs to Recent Trends in Electronics Communication Systems ]

How to cite this article:
Sumit Kumar Soni, Sreeja Nair. Deep Learning Meets IoT: Hybrid Approaches for Botnet Detection. Recent Trends in Electronics Communication Systems. 2024; 12(01):18-27.
How to cite this URL:
Sumit Kumar Soni, Sreeja Nair. Deep Learning Meets IoT: Hybrid Approaches for Botnet Detection. Recent Trends in Electronics Communication Systems. 2024; 12(01):18-27. Available from: https://journals.stmjournals.com/rtecs/article=2024/view=190743


References

1. Mohammed Sadeeq M, Abdulkareem NM, Zeebaree SRM, Mikaeel Ahmed D, Saifullah Sami A, Zebari RR. IoT and cloud computing issues, challenges and opportunities: A review. Qubahan Academic Journal. 2021;1:1–7. DOI: 10.48161/qaj.v1n2a36.
2. Schiller E, Aidoo A, Fuhrer J, Stahl J, Ziörjen M, Stiller B. Landscape of IoT security. Computer Science Review. 2022;44:100467. DOI: 10.1016/j.cosrev.2022.100467.
3. Laghari AA, Wu K, Laghari RA, Ali M, Khan AA. A review and state of art of Internet of Things (IoT). Arch Comput Methods Eng. 2021;1–19.
4. Li C, Wang J, Wang S, Zhang Y. A review of IoT applications in healthcare. Neurocomputing.
2024;565:127017.
5. Sattari F, Farooqi AH, Qadir Z, Raza B, Nazari H, Almutiry M. A hybrid deep learning approach for bottleneck detection in IoT. IEEE Access. 2022;10:77039–53. DOI: 10.1109/ACCESS.2022. 3188635.
6. Schöffel M, Lauer F, Rheinländer CC, Wehn N. Secure IoT in the era of quantum computers: Where are the bottlenecks? Sensors. 2022;22:2484. DOI: 10.3390/s22072484. PubMed: 35408099.
7. Alashhab A, Zahid MS, Muneer A, et al. Low-rate DDoS attack detection using deep learning for SDN-enabled IoT networks. [Preprint]. TechRxiv. 2024 Jan 8. doi: 10.36227/techrxiv.170472893. 31491821/v1.
8. Tsimenidis S, Lagkas T, Rantos K. Deep learning in IoT intrusion detection. J Netw Syst Manag. 2022;30:8. doi: 10.1007/s10922-021-09621-9.
9. Jasim AD. A survey of intrusion detection using deep learning in internet of things. Iraqi J Comput Sci Math. 2022;3:83–93.
10. Lakshmanna K, Kaluri R, Gundluru N, Alzamil ZS, Rajput DS, Khan AA, et al. A review on deep learning techniques for IoT data. Electronics. 2022;11:1604.
11. Tran MQ, Elsisi M, Liu MK, Vu VQ, Mahmoud K, Darwish MMF, et al. Reliable deep learning and IoT-based monitoring system for secure computer numerical control machines against cyber attacks with experimental verification. IEEE Access. 2022;10:23186–97. DOI: 10.1109/ACCESS. 2022.3153471.
12. Samara MA, Bennis I, Abouaissa A, Lorenz P. A survey of outlier detection techniques in IoT: Review and classification. J Sens Actuator Netw. 2022;11:4. DOI: 10.3390/jsan11010004.
13. Abualsauod EH. A hybrid blockchain method in internet of things for privacy and security in unmanned aerial vehicles network. Comput Electr Eng. 2022;99:107847. DOI: 10.1016/j. compeleceng.2022.107847.
14. Jayalaxmi PLS, Saha R, Kumar G, Conti M, Kim TH. Machine and deep learning solutions for intrusion detection and prevention in IoTs: A survey. IEEE Access. 2022;10:121173–92. DOI: 10.1109/ACCESS.2022.3220622.
15. Ometov A, Molua OL, Komarov M, Nurmi J. A survey of security in cloud, edge, and fog computing. Sensors. 2022;22:927. DOI: 10.3390/s22030927. PubMed: 35161675.
16. Rupanetti D, Kaabouch N. Combining edge computing-assisted Internet of things security with artificial intelligence: Applications, challenges, and opportunities. Appl Sci. 2024;14:7104. DOI:10.3390/app14167104.

17. Laroui M, Nour B, Moungla H, Cherif MA, Afifi H, Guizani M. Edge and fog computing for IoT: A survey on current research activities & future directions. Comput Commun. 2021;180:210–31.DOI: 10.1016/j.comcom.2021.09.003.
18. Rehan H. Revolutionizing America’s cloud computing the pivotal role of AI in driving innovation and security. J Artif Intell Gen Sci. 2024;2:239–40. DOI: 10.60087/jaigs.v2i1.110.
19. Fazeldehkordi E, Grønli TM. A survey of security architectures for edge computing-based IoT. IoT. 2022;3:332–65. DOI: 10.3390/iot3030019.

Regular Issue Subscription Review Article
Volume 12
Issue 01
Received 02/12/2024
Accepted 09/12/2024
Published 16/12/2024
Publication Time 14 Days
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