RFFE – Random Forest Fuzzy Entropy for the classification of Diabetes Mellitus

Ruby, A. Usha; Chandran, Jaideep; Jain, Trapti; N, Chaithanya B; Patil, Renuka R

doi:10.3934/publichealth.2023030

Cited by 1 publication

(2 citation statements)

References 22 publications

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“…Figure 6 offers an overview of the raw dataset and its classification. Additionally, we conduct a comparative analysis between our approach and prior studies [1,9,10,36,40] utilizing the FSRE-K-means-CNN technique.…”

Section: Resultsmentioning

confidence: 99%

“…This approach not only enhances URL detection accuracy but also shifts the responsibility for implementation and training costs from end-user devices to the networking infrastructure, specifically the SDN controller and switches. Within this architecture, the Convolutional Neural Network (CNN) [40] operates within the SD-Controller, facilitating the crucial training process. The outcomes of this process are then transmitted to the SD-Switches through the OpenFlow protocol, where they are applied to incoming packets originating from user devices.…”

Section: Proposed Fsre-k-means-cnn-sdn Methodsmentioning

confidence: 99%

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Enhancing Phishing URL Detection Accuracy in Software-Defined Networks (SDNs) through Feature Selection and Machine Learning Techniques

RUBY,

2024

Preprint

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Phishing attacks remain an enduring and ever-evolving menace to both networked systems and their users' privacy. In response to this formidable challenge, our research delves into an innovative approach designed to enhance the precision of phishing Uniform Resource Locator (URL) detection within the dynamic and programmable realm of Software-Defined Networks (SDNs). By harnessing feature selection capabilities and adaptive machine learning techniques, our proposed framework aims to fortify security measures in SDNs against these malicious campaigns. Our methodology's core is the deliberate selection of discriminative features from the extensive network data attributes. This feature selection process is meticulously designed to identify the most relevant characteristics associated with phishing URLs, thereby enabling the extraction of invaluable insights for more precise detection. These carefully chosen features then serve as inputs for a dynamic machine learning model, trained to adapt and evolve alongside the constantly changing landscape of phishing attacks. Within the SDN environment, our framework optimizes utilizing network resources and controller processing power. It achieves this by reducing the dimensionality of input data, resulting in improved detection accuracy and a decrease in false positives. The adaptive nature of our machine learning model ensures rapid recognition of emerging phishing tactics, thereby reducing the risk of succumbing to novel and sophisticated attacks. To validate the effectiveness of our approach, we conducted extensive experiments and evaluations within an SDN testbed, utilizing real-world phishing URL datasets. The results consistently demonstrate that our framework surpasses conventional methods, achieving higher detection accuracy and adaptability to evolving threats. In summary, our research represents a significant stride in the ongoing battle against phishing attacks by leveraging the dynamic capabilities of SDNs. The synergy between feature selection and adaptive machine learning techniques empowers SDNs to sustain accurate and effective phishing URL detection, ultimately reinforcing network security and safeguarding user privacy in an ever-evolving threat landscape.

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