Scholarly open access journals, Peer-reviewed, and Refereed Journals, Impact factor 8.14 (Calculate by google scholar and Semantic Scholar | AI-Powered Research Tool) , Multidisciplinary, Monthly, Indexing in all major database & Metadata, Citation Generator, Digital Object Identifier(DOI)
Wireless Sensor Networks (WSNs) are increasingly deployed in mission-critical applications such as military defence, healthcare monitoring, and industrial automation. However, their constrained resources and decentralized architecture make them highly susceptible to a wide range of cyber threats. This survey explores recent advancements in WSN intrusion detection, with a particular focus on bio-inspired and neuromorphic approaches that promise high detection accuracy with minimal energy overhead. Special attention is given to spiking neural networks (SNNs), federated learning, and hybrid quantum-classical models. We critically examine the strengths, limitations, datasets, and deployment feasibility of state-of-the-art techniques, aiming to guide future research toward scalable, energy-efficient, and adaptive WSN security solutions.
Keywords:
WSN, Intrusion Detection, Energy Efficiency, Machine Learning, Security, Bio-Inspired Methods
Cite Article:
"A Comprehensive Survey and Neuromorphic Intrusion Detection Framework", International Journal of Science & Engineering Development Research (www.ijrti.org), ISSN:2455-2631, Vol.10, Issue 8, page no.b316-b321, August-2025, Available :http://www.ijrti.org/papers/IJRTI2508142.pdf
Downloads:
000497
ISSN:
2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016
An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator