Water Moth Search Algorithm-based Deep Training for Intrusion Detection in IoT

Authors

  • Rekha P. M. Department of Information Science and Engineering, JSS Academy of Technical Education, Dr. Vishnuvardhana Road, Bengalru-560060, Karnataka, India
  • Nagamani H. Shahapure Department of Information Science, JSS Academy of Technical Education, Bangalore, Karnataka, India
  • Punitha M. Department of Information Science, JSS Academy of Technical Education, Bangalore, Karnataka, India
  • Sudha P. R. Department of Information Science, JSS Academy of Technical Education, Bangalore, Karnataka, India

DOI:

https://doi.org/10.13052/jwe1540-9589.2064

Keywords:

Internet of Things, Intrusion detection, Water Wave optimization, Deep Recurrent Neural Network, Moth Search Optimization.

Abstract

The economic growth and information technology leads to the development of Internet of Things (IoT) industry and has become the emerging field of research. Several intrusion detection techniques are introduced but the detection of intrusion and malicious activities poses a challenging task. This paper devises a novel method, namely the Water Moth Search algorithm (WMSA) algorithm, for training Deep Recurrent Neural Network (Deep RNN) to detect malicious network activities. The WMSA algorithm is newly devised by combining Water Wave optimization (WWO) and the Moth Search Optimization (MSO). The pre-processing is employed for the removal of redundant data. Then, the feature selection is devised using the Wrapper approach, then using the selected features; the Deep RNN classifier effectively detects the intrusion using the selected features. The proposed WMSA-based Deep RNN showed improved results with maximal accuracy, specificity, and sensitivity of 0.96, 0.973 and 0.960.

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Author Biographies

Rekha P. M., Department of Information Science and Engineering, JSS Academy of Technical Education, Dr. Vishnuvardhana Road, Bengalru-560060, Karnataka, India

Rekha P. M. is currently working as an Associate Professor and Head of the Department Information Science and Engineering, JSS Academy of Technical Education, Bangalore and has twenty years of teaching experience. Completed PhD in Cloud Computing, VTU with BMS as research center. Published several 20 papers in national and international journals which includes Scopus indexed and web of science journals. Have carried out invited talks which includes technical talk on IOT and workshop on Cloud Simulators. She is a member of IRED, IAENG, ISTE. FIELD OF RESEARCH: Internet of Things, Cloud Computing, Microcontroller & Embedded systems.

Nagamani H. Shahapure, Department of Information Science, JSS Academy of Technical Education, Bangalore, Karnataka, India

Nagamani H Shahapure has been associated with JSSATE, Bangalore since 2001. Has over 23 years of experience, which includes 3 years in Infosys and 20 years in JSS. Completed BE in CSE from PDA College of Engg. Gulbarga University in 1995. MTech from BMS College of Engineering, VTU. Completed PhD in Cloud Computing, VTU with BMS as research center. Published several (13) papers in national and international journals which includes Scopus indexed journals.Have carried out invited talks which includes technical talk on chatbots and workshop on Cloud Simulators, FIELD OF RESEARCH: Cloud Computing, Edge Computing, Fog Computing, Serverless Computing, Open Source and Web Technologies.

Punitha M., Department of Information Science, JSS Academy of Technical Education, Bangalore, Karnataka, India

Punitha M. is currently working as an Assistant Professor in Information Science and Engineering Department, JSS Academy of Technical Education, Bangalore and has two years of teaching experience. Research interest in Internet of Things, Cloud computing and Python Programming. Attended several FDP’s and Workshop.

Sudha P. R., Department of Information Science, JSS Academy of Technical Education, Bangalore, Karnataka, India

Sudha P. R. is currently working as an Assistant Professor in Information Science and Engineering Department, JSS Academy of Technical Education, Bangalore and has fifteen years of teaching experience. Research interest in Cloud computing and Network Security. Attended several FDP’s and Workshop.

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Published

2021-10-13

How to Cite

M., R. P., Shahapure, N. H., M., P., & R., S. P. (2021). Water Moth Search Algorithm-based Deep Training for Intrusion Detection in IoT. Journal of Web Engineering, 20(6), 1781–1812. https://doi.org/10.13052/jwe1540-9589.2064

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Articles