MOE/RF: A Novel Phishing Detection Model Based on Revised Multiobjective Evolution Optimization Algorithm and Random Forest

Zhu, En; Chen, Zhile; Cui, Jie; Zhong, Hong

doi:10.1109/tnsm.2022.3162885

Cited by 16 publications

(3 citation statements)

References 55 publications

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“…Compared with the traditional grid search and random search methods, genetic algorithms can search for the optimal solution more comprehensively and quickly when optimising the parameters of the Random Forest algorithm, and can handle more combinations of parameters. Therefore, genetic algorithms play an important role in parameter optimisation of the Random Forest algorithm to improve the accuracy and efficiency of the model [6].…”

Section: Genetic Algorithmmentioning

confidence: 99%

Research and Application of Random Forest Model Based on Genetic Algorithm Optimisation

Yu,

2024

TCSISR

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In this study, an in-depth analysis of the relationship between infant behavioural characteristics and mothers' physical and psychological indicators was conducted by integrating a random forest model optimised by a genetic algorithm. A mathematical model of treatment cost and health improvement rate was also established, which provides a scientific basis and technical support for infant behaviour analysis and individualized intervention strategies.

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Section: Genetic Algorithmmentioning

confidence: 99%

Research and Application of Random Forest Model Based on Genetic Algorithm Optimisation

Yu,

2024

TCSISR

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“…This algorithm includes multiple decision trees for different subsets of the provided dataset [25]. It takes an average value to improve the accuracy of the model for efficient prediction using each tree and on the basis of frequent results from these trees [26]. In the proposed model, the random forest learned the dataset and provided remarkable accuracy.…”

Section: Random Forestmentioning

confidence: 99%

A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning

Shaukat,

Amin,

Muslam

et al. 2023

Sensors

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Phishing attacks are evolving with more sophisticated techniques, posing significant threats. Considering the potential of machine-learning-based approaches, our research presents a similar modern approach for web phishing detection by applying powerful machine learning algorithms. An efficient layered classification model is proposed to detect websites based on their URL structure, text, and image features. Previously, similar studies have used machine learning techniques for URL features with a limited dataset. In our research, we have used a large dataset of 20,000 website URLs, and 22 salient features from each URL are extracted to prepare a comprehensive dataset. Along with this, another dataset containing website text is also prepared for NLP-based text evaluation. It is seen that many phishing websites contain text as images, and to handle this, the text from images is extracted to classify it as spam or legitimate. The experimental evaluation demonstrated efficient and accurate phishing detection. Our layered classification model uses support vector machine (SVM), XGBoost, random forest, multilayer perceptron, linear regression, decision tree, naïve Bayes, and SVC algorithms. The performance evaluation revealed that the XGBoost algorithm outperformed other applied models with maximum accuracy and precision of 94% in the training phase and 91% in the testing phase. Multilayer perceptron also worked well with an accuracy of 91% in the testing phase. The accuracy results for random forest and decision tree were 91% and 90%, respectively. Logistic regression and SVM algorithms were used in the text-based classification, and the accuracy was found to be 87% and 88%, respectively. With these precision values, the models classified phishing and legitimate websites very well, based on URL, text, and image features. This research contributes to early detection of sophisticated phishing attacks, enhancing internet user security.

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“…Phishing has become one of the most common cyberattacks due to the exponential growth of the Internet [13]. The Anti-Phishing Working Group (APWG) reported a huge increase in the second quarter of 2022 [14], finding 1, 097, 811 phishing attacks, a record number, making that quarter the worst ever observed.…”

Section: Introductionmentioning

confidence: 99%

PhiKitA: Phishing Kit Attacks Dataset for Phishing Websites Identification

Castano¹,

Fidalgo²,

Aláiz-Rodríguez³

et al. 2023

IEEE Access

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Recent studies have shown that phishers are using phishing kits to deploy phishing attacks faster, easier and more massive. Detecting phishing kits in deployed websites might help to detect phishing campaigns earlier. To the best of our knowledge, there are no datasets providing a set of phishing kits that are used in websites that were attacked by phishing. In this work, we propose PhiKitA, a novel dataset that contains phishing kits and also phishing websites generated using these kits. We have applied MD5 hashes, fingerprints, and graph representation DOM algorithms to obtain baseline results in PhiKitA in three experiments: familiarity analysis of phishing kit samples, phishing website detection and identifying the source of a phishing website. In the familiarity analysis, we find evidence of different types of phishing kits and a small phishing campaign. In the binary classification problem for phishing detection, the graph representation algorithm achieved an accuracy of 92.50%, showing that the phishing kit data contain useful information to classify phishing. Finally, the MD5 hash representation obtained a 39.54% F1 score, which means that this algorithm does not extract enough information to distinguish phishing websites and their phishing kit sources properly.

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MOE/RF: A Novel Phishing Detection Model Based on Revised Multiobjective Evolution Optimization Algorithm and Random Forest

Cited by 16 publications

References 55 publications

Research and Application of Random Forest Model Based on Genetic Algorithm Optimisation

Research and Application of Random Forest Model Based on Genetic Algorithm Optimisation

A Hybrid Approach for Alluring Ads Phishing Attack Detection Using Machine Learning

PhiKitA: Phishing Kit Attacks Dataset for Phishing Websites Identification

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