Phishing Detection Using Machine Learning Algorithms

Mohammed, Moulana; Prasanth, K.; Subhash, Sujit

doi:10.1109/icssit53264.2022.9716269

Cited by 4 publications

(2 citation statements)

References 6 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this study, the random forest provided 90% accuracy, which was the highest. Based on phishing detection, for instance, in this paper [37], they only trained the decision tree model and got approximately 90% accuracy, SVM with 95.66% accuracy in [38]. However, Ojewumi et al used three ML algorithms while they got k-NN at 93.39%, SVM at 91.74%, and Random Forest at 98.35% accuracy [39].…”

Section: ) Tradition Machine Learning (Ml) Methodsmentioning

confidence: 99%

EnLem: An Ensemble Learning-based Model for Detecting Phishing Websites

Yeasmin,

Refat,

Singh

et al. 2023

Preprint

View full text Add to dashboard Cite

<p>In this paper, there is a novel model based on ensemble learning for predicting phishing websites. The overall design is a combination of three individual machine-learning models with the help of the uni-variate feature selection model for detecting phishing and non-phishing URLs. The proposed model can minimize the error rate and provide improved accuracy with a short execution time. As compared with other traditional models, this model has higher performance metrics for detecting phishing websites compared with traditional machine learning and deep learning models. </p>

show abstract

Section: ) Tradition Machine Learning (Ml) Methodsmentioning

confidence: 99%

EnLem: An Ensemble Learning-based Model for Detecting Phishing Websites

Yeasmin,

Refat,

Singh

et al. 2023

Preprint

View full text Add to dashboard Cite

show abstract

“…Support Vector Machines were implemented in both (7) and (8) , with the latter storing identified phishing URLs in a text file. (9) introduced a distinctive method by calculating the proportionate distance between input and database URLs, incorporating a Favicon Images Recognition Algorithm alongside established approaches like False Positive and False Negative. (10) Adopted a simple yet effective machine learning approach for phishing URL detection.…”

Section: Introductionmentioning

confidence: 99%

MaliceSpotter: Revolutionizing Cyber Security with Machine Learning for Phishing Resilience

Borade,

Doshi,

Patel

2024

IJST

View full text Add to dashboard Cite

Objectives: To enhance cyber security by implementing advanced algorithms to swiftly identify and neutralize phishing threats. Also, to bolster user protection, fortify data integrity, and ensure a resilient defense against evolving cyber threats. Methods: MaliceSpotter aims in classifying user-entered URLs by analysing 28 features, using algorithms like Logistic Regression, Random Forest, and KNN, combined via a Voting Classifier. Dataset on Kaggle provides diverse samples for evaluation. This methodology's unique aspects include multiple algorithm integration and the utilization of Kaggle as a data source. Findings: MaliceSpotter demonstrates a commendable accuracy of 95%, effectively classifying input URLs as phishing or legitimate. The system's uniqueness lies in its provision of a detailed report on URL behavior, facilitating informed decision-making. The implementation of ensemble learning is notable, particularly the introduction of the Voting Classifier. This approach leverages various algorithms, successfully incorporating bagging and voting concepts. Through the Voting Classifier, MaliceSpotter gains insights into the working of machine learning algorithms, enhancing the scrutiny of URL behavior. This innovative feature sets MaliceSpotter apart, offering a nuanced perspective on the reliability of URLs through the collective input of diverse algorithms. Novelty: MaliceSpotter uniquely combines diverse algorithms, leveraging a voting classifier for robust results. Continuously updating in real time, it meticulously dissects URLs into 28 parts, ensuring thorough scrutiny and effective detection. Keywords: Phishing, Machine Learning, Web Security, Voting Classifier, Bagging

show abstract

BIMI specification as another technical approach in the fight against e-mail phishing

Dolnak¹,

Kampova²

2022

2022 20th International Conference on Emerging eLearning Technologies and Applications (ICETA)

View full text Add to dashboard Cite

Phishing Detection Using Machine Learning Algorithms

Cited by 4 publications

References 6 publications

EnLem: An Ensemble Learning-based Model for Detecting Phishing Websites

EnLem: An Ensemble Learning-based Model for Detecting Phishing Websites

MaliceSpotter: Revolutionizing Cyber Security with Machine Learning for Phishing Resilience

BIMI specification as another technical approach in the fight against e-mail phishing

Contact Info

Product

Resources

About