Journal ArticleDOI
A stacking model using URL and HTML features for phishing webpage detection
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TLDR
A stacking model by combining GBDT, XGBoost and LightGBM in multiple layers is devised, which enables different models to be complementary, thus improving the performance on phishing webpage detection.About:
This article is published in Future Generation Computer Systems.The article was published on 2019-05-01. It has received 139 citations till now. The article focuses on the topics: Phishing & Character encodings in HTML.read more
Citations
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Journal ArticleDOI
A comprehensive survey of AI-enabled phishing attacks detection techniques.
TL;DR: A literature review of Artificial Intelligence techniques: Machine Learning, Deep Learning, Hybrid Learning, and Scenario-based techniques for phishing attack detection for each AI technique is presented and the qualities and shortcomings of these methodologies are examined.
Journal ArticleDOI
Phishing web site detection using diverse machine learning algorithms
Ammara Zamir,Hikmat Ullah Khan,Tassawar Iqbal,Nazish Yousaf,Farah Aslam,Almas Anjum,Maryam Hamdani +6 more
TL;DR: This research is novel in this regard that no previous research focusses on using feed forward NN and ensemble learners for detecting phishing websites.
Proceedings ArticleDOI
VisualPhishNet: Zero-Day Phishing Website Detection by Visual Similarity
TL;DR: In this paper, a similarity-based phishing detection framework based on a triplet convolutional neural network (CNN) was proposed to detect phishing websites by a similarity metric that generalizes to pages with new visual appearances.
Journal ArticleDOI
AI Meta-Learners and Extra-Trees Algorithm for the Detection of Phishing Websites
TL;DR: Four (4) meta-learner models developed using the extra-tree base classifier outperform existing ML-based models in phishing attack detection and are recommended for the adoption of meta-learners when building phishingattack detection models.
Journal ArticleDOI
Efficient deep learning techniques for the detection of phishing websites
TL;DR: Novel phishing URL detection models using Deep Neural Network, Long Short-Term Memory, and Convolution Neural Network are proposed using only 10 features of earlier work, which achieves an accuracy of 99.52% for DNN, 99.57% for LSTM and 99.43% for CNN.
References
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Deep Residual Learning for Image Recognition
TL;DR: In this article, the authors proposed a residual learning framework to ease the training of networks that are substantially deeper than those used previously, which won the 1st place on the ILSVRC 2015 classification task.
Proceedings Article
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: This paper presents a simple method for finding phrases in text, and shows that learning good vector representations for millions of phrases is possible and describes a simple alternative to the hierarchical softmax called negative sampling.
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Greedy function approximation: A gradient boosting machine.
TL;DR: A general gradient descent boosting paradigm is developed for additive expansions based on any fitting criterion, and specific algorithms are presented for least-squares, least absolute deviation, and Huber-M loss functions for regression, and multiclass logistic likelihood for classification.
Proceedings ArticleDOI
XGBoost: A Scalable Tree Boosting System
Tianqi Chen,Carlos Guestrin +1 more
TL;DR: XGBoost as discussed by the authors proposes a sparsity-aware algorithm for sparse data and weighted quantile sketch for approximate tree learning to achieve state-of-the-art results on many machine learning challenges.
Posted Content
Distributed Representations of Words and Phrases and their Compositionality
TL;DR: In this paper, the Skip-gram model is used to learn high-quality distributed vector representations that capture a large number of precise syntactic and semantic word relationships and improve both the quality of the vectors and the training speed.