Lightweight Hyperparameter Optimization Model for Enhancing Phishing Detection in IoT

Hunaıda Avvad

Article Details

Lightweight Hyperparameter Optimization Model for Enhancing Phishing Detection in IoT

Authors:

Hunaıda Avvad

Publisher:

Erzincan Binali Yildirim University

Main Topics:

Bilgi Sistemleri (Diğer)

Journal:

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.18 Issue 1, pp.189-203

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001343696

pISSN:

2149-4584

Keywords:

Abstract:

 This study presents an enhanced machine learning approach that emphasizes the optimization of hyperparameters to improve phishing detection, particularly in resource-constrained environments like Internet of Things IoT devices. Phishing is considered one of the dangerous forms of cyberattacks where attackers can reveal sensitive information about users identity, password, privacy and even properties. Machine learning techniques and tools are playing important role in detecting phishing and have shown to be effective and advantageous methods for detection and classification, especially for the unified resource locator URL. The proposed model presupposes a systematic approach for feature selection as well as finding the optimized hyperparameter values for the sake of increasing the detection quality while maintaining low computational complexity of the process. This study examines how feature set selection from a training dataset and how hyperparameters tuning can significantly improves the performance of phishing attack classification in IoT devices. Logistic regression, random forest, gradient boosting, support vector machine, and k-nearest neighbors are used in this study. According to the experimental, we found the best hyperparameter values for each classifier and comparative results of the implemented classification algorithms showed that support vector machine achieved the best performance with an accuracy of 96.2.

 This study presents an enhanced machine learning approach that emphasizes the optimization of hyperparameters to improve phishing detection, particularly in resource-constrained environments like Internet of Things IoT devices. Phishing is considered one of the dangerous forms of cyberattacks where attackers can reveal sensitive information about users identity, password, privacy and even properties. Machine learning techniques and tools are playing important role in detecting phishing and have shown to be effective and advantageous methods for detection and classification, especially for the unified resource locator URL. The proposed model presupposes a systematic approach for feature selection as well as finding the optimized hyperparameter values for the sake of increasing the detection quality while maintaining low computational complexity of the process. This study examines how feature set selection from a training dataset and how hyperparameters tuning can significantly improves the performance of phishing attack classification in IoT devices. Logistic regression, random forest, gradient boosting, support vector machine, and k-nearest neighbors are used in this study. According to the experimental, we found the best hyperparameter values for each classifier and comparative results of the implemented classification algorithms showed that support vector machine achieved the best performance with an accuracy of 96.2.

 This study presents an enhanced machine learning approach that emphasizes the optimization of hyperparameters to improve phishing detection, particularly in resource-constrained environments like Internet of Things IoT devices. Phishing is considered one of the dangerous forms of cyberattacks where attackers can reveal sensitive information about users identity, password, privacy and even properties. Machine learning techniques and tools are playing important role in detecting phishing and have shown to be effective and advantageous methods for detection and classification, especially for the unified resource locator URL. The proposed model presupposes a systematic approach for feature selection as well as finding the optimized hyperparameter values for the sake of increasing the detection quality while maintaining low computational complexity of the process. This study examines how feature set selection from a training dataset and how hyperparameters tuning can significantly improves the performance of phishing attack classification in IoT devices. Logistic regression, random forest, gradient boosting, support vector machine, and k-nearest neighbors are used in this study. According to the experimental, we found the best hyperparameter values for each classifier and comparative results of the implemented classification algorithms showed that support vector machine achieved the best performance with an accuracy of 96.2.

 This study presents an enhanced machine learning approach that emphasizes the optimization of hyperparameters to improve phishing detection, particularly in resource-constrained environments like Internet of Things IoT devices. Phishing is considered one of the dangerous forms of cyberattacks where attackers can reveal sensitive information about users identity, password, privacy and even properties. Machine learning techniques and tools are playing important role in detecting phishing and have shown to be effective and advantageous methods for detection and classification, especially for the unified resource locator URL. The proposed model presupposes a systematic approach for feature selection as well as finding the optimized hyperparameter values for the sake of increasing the detection quality while maintaining low computational complexity of the process. This study examines how feature set selection from a training dataset and how hyperparameters tuning can significantly improves the performance of phishing attack classification in IoT devices. Logistic regression, random forest, gradient boosting, support vector machine, and k-nearest neighbors are used in this study. According to the experimental, we found the best hyperparameter values for each classifier and comparative results of the implemented classification algorithms showed that support vector machine achieved the best performance with an accuracy of 96.2.

Article Details

Lightweight Hyperparameter Optimization Model for Enhancing Phishing Detection in IoT

Authors:

Hunaıda Avvad

Publisher:

Erzincan Binali Yildirim University

Main Topics:

Bilgi Sistemleri (Diğer)

Journal:

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.18 Issue 1, pp.189-203

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001343696

pISSN:

2149-4584

classification

cyber security

hyperparameter tuning

industrial engineering

phishing attack

Issues

Article Details

Lightweight Hyperparameter Optimization Model for Enhancing Phishing Detection in IoT

Authors: Hunaıda Avvad

Publisher: Erzincan Binali Yildirim University

Main Topics: Bilgi Sistemleri (Diğer)

Journal: Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.18 Issue 1, pp.189-203

Document Type: Article

Publication Date: 3/2025

Document Language: English

Document ID (Org No.): 2001343696

pISSN: 2149-4584

classification

cyber security

hyperparameter tuning

industrial engineering

phishing attack

Issues

Authors:

Hunaıda Avvad

Publisher:

Erzincan Binali Yildirim University

Main Topics:

Bilgi Sistemleri (Diğer)

Journal:

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi Vol.18 Issue 1, pp.189-203

Document Type:

Article

Publication Date:

3/2025

Document Language:

English

Document ID (Org No.):

2001343696

pISSN:

2149-4584