Distributed Fuzzy Logic Algorithm for Cyberattack Detection and Energy Efficiency in Wireless Sensor Networks

Leena Arya; Gunjan Varshney; MPJ Santosh; Venkata Rajani Katuri; Monika Bhatnagar; Ravi Rastogi

doi:10.3991/ijim.v19i15.54871

Authors

Leena Arya Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India https://orcid.org/0000-0002-9249-732X
Gunjan Varshney JSS University, Noida, Uttar Pradesh, India https://orcid.org/0000-0002-3717-9660
MPJ Santosh Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India https://orcid.org/0000-0003-1002-3604
Venkata Rajani Katuri GITAM University, Hyderabad, Telangana, India https://orcid.org/0009-0008-1790-7027
Monika Bhatnagar Galgotias College of Engineering & Technology, Greater Noida, Uttar Pradesh, India https://orcid.org/0000-0003-0005-882X
Ravi Rastogi Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India https://orcid.org/0000-0002-1118-3020

DOI:

https://doi.org/10.3991/ijim.v19i15.54871

Keywords:

Adaptive Security Mechanisms, Cluster Head Selection, Energy consumption optimization, Residual Energy, Resource-Constrained Networks

Abstract

Wireless sensor networks (WSNs) are critical for applications like environmental monitoring and industrial automation but face challenges balancing cybersecurity and energy efficiency. Existing approaches, such as centralized intrusion detection systems (IDS) and machine learning (ML) models, suffer from high computational overhead, scalability issues, and an inability to adapt to dynamic threats. This paper proposes a distributed fuzzy logic algorithm (DFLA) that integrates cyberattack detection and energy optimization through a decentralized architecture. By employing fuzzy logic to handle uncertainty, Dempster-Shafer theory for decision fusion, and the Reptile Search Algorithm for parameter adjustment, DFLA uses dual-objective rules to dynamically evaluate metrics such as packet drop rate, residual energy, and signal strength deviation. Nodes autonomously compute an attack risk level (ARL) and adjust transmission using localized fuzzy inference systems (FIS), minimizing reliance on cluster heads. Validated on real-world datasets (WSN-DS, CIC-IDS2017) and testbeds (TinyOS), DFLA achieves 99.87% detection accuracy for Blackhole and Flooding attacks, outperforming E-LEACH and RSA-IT2FLS while reducing energy consumption by 48%. The distributed design ensures scalability with lower communication overhead than centralized systems.

Author Biographies

Leena Arya, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India

Dr. Leena Arya is working as a Professor at the Department of Computer Science and Engineering at Koneru Lakshmaiah Education Foundation Deemed to be University, Vaddeswaram, 22nd NIRF Ranking University, Guntur, AP, India. She completed her Ph.D. from IIT Roorkee in 2012. She has published over 55 research papers in Scopus and SCI-indexed international journals. She handled a research project for the Department of Science and Technology, DST, New Delhi, as Principal Investigator under the Women Scientists Scheme (WOS-A). She has been granted a UK design and Australian Government Innovation patents. She has been a reviewer of many prestigious international journals and conferences. Her research area includes wireless communication, the Internet of Things (IoT), network security, and artificial intelligence.

Gunjan Varshney, JSS University, Noida, Uttar Pradesh, India

Dr. Gunjan Varshney is currently working as an Associate Professor and Head, Department of Robotics and Artificial Intelligence JSS University, Noida, U.P.
She has spent more than seventeen years working for JSS Academy of Technical Education, Noida. More than 30 of her research papers have been published in journals and conferences at both the national and international levels. She is Senior member of IEEE and leading IEEE JSSATEN Student branch with the current role in it as branch counsellor. She is also faculty advisor, Women in Engineering Affinity group, IEEE JSSATE. She is also the member of, Industry academia young professional committee -2024. She contributes in the MDSM committee of the IEEE UP Section as well. She has been an exceptionally active IEEE volunteer, contributing significantly to numerous activities across various roles. She is the recipient of Outstanding Branch Counselor Award from the IEEE UP Section in 2022 and also Outstanding Branch Counselor Award from IEEE MGA in 2023.

Ravi Rastogi, Koneru Lakshmaiah Education Foundation, Guntur, Andhra Pradesh, India

Dr. Ravi Rastogi received his Ph.D. in Computer Science and Engineering in December 2011 from Uttarakhand Technical University, Dehradun, Uttarakhand, India. MS in Computer Science degree in September 2005 from Fairleigh Dickinson University, Teaneck, New Jersey, USA. He is working as a Professor in the Department of Computer Science and Engineering (Honors) at Koneru Lakshmaiah Education Foundation Deemed to be a university. He was a database developer for Microsoft, Seattle, USA, and ING, Philadelphia, USA. His research interests are interconnection networks, stable matching problems, data mining and intelligence in systems, the Internet of Things, and cloud computing. Refereed International Journal publications are 22 and Refereed International Conference Publications are 8.