Light gradient boosting machine-based phishing webpage detection model using phisher website features of mimic URLs

Oram, Etuari; Dash, Pandit Byomakesha; Naik, Bighnaraj; Nayak, Janmenjoy; Vimal, S.; Nataraj, Sathees Kumar

doi:10.1016/j.patrec.2021.09.018

Cited by 16 publications

(5 citation statements)

References 20 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The EFB algorithm can combine many exclusive characteristics to a much less characteristic density that can dramatically avoid unnecessary calculations for zero feature value [88]. The LightGBM algorithm is a histogram-based algorithm that inserts continuous feature (attribute) into discrete values [89], giving rise to faster training speed with higher efficiency and reduced memory usage [90], [91].…”

Section: Light Gradient Boosting Machine (Lightgbm)mentioning

confidence: 99%

Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

et al. 2023

View full text Add to dashboard Cite

show abstract

Section: Light Gradient Boosting Machine (Lightgbm)mentioning

confidence: 99%

Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

et al. 2023

View full text Add to dashboard Cite

show abstract

“…The DL approach has been utilized frequently in various text-related problems, including text categorization and machine translation [22,29], treating the URL string as a series of text for detecting phishing sites. [23] LGBM Phishing dataset for ML DL techniques are used to understand the representation of URL functionality at the dictionary and semantic levels.…”

Section: Existing Studies On Phishing Detectionmentioning

confidence: 99%

HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction

Zonyfar,

Lee,

Kim

2023

JWE

View full text Add to dashboard Cite

Phishing techniques are the most frequently used threat by attackers to deceive Internet users and obtain sensitive victim information, such as login credentials and credit card numbers. So, it is important for users to know the legitimate website to avoid the traps of fake websites. However, it is difficult for lay users to distinguish legitimate websites, considering that phishing techniques are always developing from time to time. Therefore, a legitimate website detection system is an easy way for users to avoid phishing websites. To address this problem, we present a hybrid deep learning model by combining a convolution neural network and long short-term memory (HCNN-LSTM). A one-dimensional CNN with a LSTM network shared estimation of all sublayers, then implements the proposed model in the benchmark dataset for phishing prediction, which consists of 11430 URLs with 87 attributes extracted of which 56 parameters are selected from URL structure and syntax. The HCNN-LSTM model was successful in binary classification with accuracy, precision, recall, and F1-score of 95.19%, 95.00%, 95.00%, 95.00%, successively outperforming the CNN and LSTM. Thus, the results show that our proposed model is a competitive new model for the legitimate web prediction tasks.

show abstract

“…It was created by Microsoft, is quicker, and uses less memory. As light GBM develops vertically (leaf-wise), more loss is reduced [29].…”

Section: Light Gradient Boostingmentioning

confidence: 99%

A Machine Learning Approach to Identifying Phishing Websites: A Comparative Study of Classification Models and Ensemble Learning Techniques

Uppalapati,

Gontla,

Gundu

et al. 2023

EAI Endorsed Scal Inf Syst

View full text Add to dashboard Cite

Phishing assaults are one of the more prevalent types of cybercrime in the world today. To steal information, users are sent emails and messages. Moreover, websites are used for it. Phishing primarily targets corporate web-sites, such as those for e-commerce, finance, and governmental organizations. In order to obtain sensitive user information, attackers impersonate websites, a phenomenon known as phishing. In addition to exploring the use of machine learning algorithms to identify and stop web phishing assaults, this research suggests utilizing machine learning techniques to detect phish-ing URLs by analysing various aspects of the URLs. The study includes classification models like Logistic Regression, Random Forest, Decision trees, KNN, Naive bayes, SVM and other ensemble learning techniques like Gradient Boosting, XGBoost, Histogram Gradient Boosting, Light Gradient Boosting and AdaBoost were used to detect phishing websites.

show abstract

Light gradient boosting machine-based phishing webpage detection model using phisher website features of mimic URLs

Cited by 16 publications

References 20 publications

Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction

A Machine Learning Approach to Identifying Phishing Websites: A Comparative Study of Classification Models and Ensemble Learning Techniques

Contact Info

Product

Resources

About

Light gradient boosting machine-based phishing webpage detection model using phisher website features of mimic URLs

Cited by 16 publications

References 20 publications

Boosting Algorithm to Handle Unbalanced Classification of PM2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

Boosting Algorithm to Handle Unbalanced Classification of PM2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

HCNN-LSTM: Hybrid Convolutional Neural Network with Long Short-Term Memory Integrated for Legitimate Web Prediction

A Machine Learning Approach to Identifying Phishing Websites: A Comparative Study of Classification Models and Ensemble Learning Techniques

Contact Info

Product

Resources

About

Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM

Boosting Algorithm to Handle Unbalanced Classification of PM_2.5 Concentration Levels by Observing Meteorological Parameters in Jakarta-Indonesia Using AdaBoost, XGBoost, CatBoost, and LightGBM