International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

A Strong Network Security Framework Utilizing a Dual-Layered and Hybrid Model Integrated with Machine Learning

K P ManikandanAssistant Professor, Department of CSE (Cyber Security), Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh 517325, IndiaTiruthani GovardhanDepartment of CSE (Cyber Security), Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh 517325, IndiaPuram NarasimhuluDepartment of CSE (Cyber Security), Madanapalle Institute of Technology & Science, Madanapalle, Andhra Pradesh 517325, India

Vol 9 No 25 (2025): Volume 9, Special Issue of INSPIRE’25 April 2025 | Pages: 233-237

International Research Journal of Innovations in Engineering and Technology

OPEN ACCESS | Research Article | Published Date: 24-04-2025

doi Logo doi.org/10.47001/IRJIET/2025.INSPIRE37

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Abstract

In an era of escalating cyber threats, having effective network security is vital for preserving sensitive data and digital assets. In order to improve threat identification and mitigation, this study suggests a robust network security framework that uses a dual- layered, hybrid model combined with machine learning. The framework uses machine learning algorithms to continuously adjust to changing attack patterns by combining signature-based and anomaly- based intrusion detection techniques. By combining deep packet inspection and perimeter defense mechanisms, the dual-layered strategy improves security and provides complete defense against known and undiscovered threats. Additionally, the hybrid approach reduces false positives and increases threat classification accuracy by combining sophisticated AI-driven analytics with rule-based heuristics. Results from experiments show how well this framework works to identify cyberthreats with high accuracy Through the presentation of an intelligent, flexible, and robust security architecture, this study advances network security.

Keywords

Random Forest, Linear SVM, KNeighbors Classifier, Gradient Boosting, Multi Layer Perceptron, Logistic Regression


Citation of this Article

K P Manikandan, Tiruthani Govardhan, & Puram Narasimhulu. (2025). A Strong Network Security Framework Utilizing a Dual-Layered and Hybrid Model Integrated with Machine Learning. In proceeding of International Conference on Sustainable Practices and Innovations in Research and Engineering (INSPIRE'25), published by IRJIET, Volume 9, Special Issue of INSPIRE’25, pp 233-237. Article DOI https://doi.org/10.47001/IRJIET/2025.INSPIRE37

Licence Copyright (c) 2026 International Research Journal of Innovations in Engineering and Technology creative common licence This work is licensed under Creative common Attribution Non Commercial 4.0 Internation Licence
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