An Intelligent Intrusion Detection and Mitigation using Optimized Neuro Fuzzy Inference System in Wireless Communication Networks

dc.contributor.guideSangita Chaudhari
dc.coverage.spatialNetwork Security
dc.creator.researcherChitte, P H
dc.date.accessioned2026-01-16T09:30:28Z
dc.date.available2026-01-16T09:30:28Z
dc.date.awarded2025
dc.date.completed2025
dc.date.registered2020
dc.description.abstractThe rapid growth of wireless communication technologies and their widespread deployment in modern infrastructure have significantly increased the vulnerability of wireless networks to cyber threats, particularly Distributed Denial of Service (DDoS) attacks. The continuously evolving nature of DDoS attack strategies necessitates intelligent, adaptive and resource-efficient intrusion detection and mitigation mechanisms for wireless communication networks. newline Conventional Intrusion Detection Systems (IDS) deployed in wireless environments suffer from several limitations including high false positive rates, poor adaptability to emerging attack patterns, scalability issues and excessive computational overhead. While machine learning-based techniques improve detection accuracy, they often lack interpretability and demand significant computational resources. Fuzzy logic-based approaches offer transparent decision-making but face challenges in handling complex and dynamic attack behaviors. Bio-inspired techniques provide adaptability; however, their integration into real-time wireless intrusion detection frameworks remains limited. newline To address these challenges, this research proposes an intelligent intrusion detection and mitigation framework based on an optimized Neuro-Fuzzy Inference System for wireless communication networks. The proposed framework integrates adaptive neural network learning with interpretable fuzzy logic reasoning to accurately detect and classify both known and unknown intrusion patterns. Metaheuristic optimization algorithms are employed to autonomously optimize system parameters and detection thresholds, thereby enhancing detection accuracy while reducing computational complexity. An intelligent autonomous mitigation mechanism is incorporated to enable timely response actions such as malicious node isolation, intelligent traffic redirection and dynamic network reconfiguration, particularly during DDoS attack scenarios.
dc.description.note
dc.format.accompanyingmaterialNone
dc.format.dimensions5.67 MB
dc.format.extent270
dc.identifier.researcherid0000-0002-8458-9670
dc.identifier.urihttp://hdl.handle.net/10603/688290
dc.languageEnglish
dc.publisher.institutionSchool of Engineering
dc.publisher.placeNavi Mumbai
dc.publisher.universityPadmashree Dr. D.Y. Patil Vidyapeeth, Navi Mumbai
dc.relationhttps://doi.org/10.1007/s13042-025-02617-w, https://doi.org/10.1016/j.dajour.2025.100608
dc.rightsuniversity
dc.source.universityUniversity
dc.subject.keywordComputer Science
dc.subject.keywordComputer Science Artificial Intelligence
dc.subject.keywordEngineering and Technology
dc.titleAn Intelligent Intrusion Detection and Mitigation using Optimized Neuro Fuzzy Inference System in Wireless Communication Networks
dc.title.alternativeAn Intelligent Neuro-Fuzzy Based Intrusion Detection and Mitigation Model for Wireless Communication Environments
dc.type.degreePh.D.

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