An Intelligent Intrusion Detection and Mitigation using Optimized Neuro Fuzzy Inference System in Wireless Communication Networks
| dc.contributor.guide | Sangita Chaudhari | |
| dc.coverage.spatial | Network Security | |
| dc.creator.researcher | Chitte, P H | |
| dc.date.accessioned | 2026-01-16T09:30:28Z | |
| dc.date.available | 2026-01-16T09:30:28Z | |
| dc.date.awarded | 2025 | |
| dc.date.completed | 2025 | |
| dc.date.registered | 2020 | |
| dc.description.abstract | The 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.accompanyingmaterial | None | |
| dc.format.dimensions | 5.67 MB | |
| dc.format.extent | 270 | |
| dc.identifier.researcherid | 0000-0002-8458-9670 | |
| dc.identifier.uri | http://hdl.handle.net/10603/688290 | |
| dc.language | English | |
| dc.publisher.institution | School of Engineering | |
| dc.publisher.place | Navi Mumbai | |
| dc.publisher.university | Padmashree Dr. D.Y. Patil Vidyapeeth, Navi Mumbai | |
| dc.relation | https://doi.org/10.1007/s13042-025-02617-w, https://doi.org/10.1016/j.dajour.2025.100608 | |
| dc.rights | university | |
| dc.source.university | University | |
| dc.subject.keyword | Computer Science | |
| dc.subject.keyword | Computer Science Artificial Intelligence | |
| dc.subject.keyword | Engineering and Technology | |
| dc.title | An Intelligent Intrusion Detection and Mitigation using Optimized Neuro Fuzzy Inference System in Wireless Communication Networks | |
| dc.title.alternative | An Intelligent Neuro-Fuzzy Based Intrusion Detection and Mitigation Model for Wireless Communication Environments | |
| dc.type.degree | Ph.D. |
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