A distributed architecture for phishing detection using Bayesian Additive Regression Trees

Abu-Nimeh, Saeed; Nappa, Dario; Wang, Xinlei; Nair, Suku

doi:10.1109/ecrime.2008.4696965

Cited by 10 publications

(5 citation statements)

References 12 publications

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“…They found BACT to outperform the logit model, CART and support vector machines. Abu-Nimeh et al (2008) also independently discovered the probit extension of BART, which they call CBART, and applied it for the automatic detection of phishing emails. They found CBART to outperform logistic regression, random forests, support vector machines, CART, neural networks and the original BART.…”

Section: Execution Time Considerationsmentioning

confidence: 99%

BART: Bayesian additive regression trees

Chipman¹,

George²,

McCulloch³

2010

Ann. Appl. Stat.

1,448

1,961

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Bayesian Additive Regression Trees (BART) is a statistical sum of trees model. It can be considered a Bayesian version of machine learning tree ensemble methods where the individual trees are the base learners. However for datasets where the number of variables p is large (e.g. p > 5, 000) the algorithm can become prohibitively expensive, computationally. Another method which is popular for high dimensional data is random forests, a machine learning algorithm which grows trees using a greedy search for the best split points. However, as it is not a statistical model, it cannot produce probabilistic estimates or predictions. We propose an alternative algorithm for BART called BART-BMA, which uses Bayesian Model Averaging and a greedy search algorithm to produce a model which is much more efficient than BART for datasets with large p. BART-BMA incorporates elements of both BART and random forests to offer a model-based algorithm which can deal with high-dimensional data.

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Section: Execution Time Considerationsmentioning

confidence: 99%

BART: Bayesian additive regression trees

Chipman¹,

George²,

McCulloch³

2010

Ann. Appl. Stat.

1,448

1,961

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“…[11] Accuracy= True Positive/ True Positive + True Negative We reach the error rate by using false positive and false negative values outside the diagonals. [12] Error Rate= False Positive+ False Negative/ All of Samples Presicion; It shows how many of the values we estimated as positive are actually positive. [13] Presicion= True Positive/ True Positive + False Positive Sensitivity (Recall) is a metric that shows how much of the operations we need to estimate as Positive, we estimate as Positive.…”

Section: Iterative Neural Networkmentioning

confidence: 99%

Classification of Plant Diseases with Machine Learning

Akarsu¹,

Karacalı²,

Özbek³

2023

IJANSER

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Deep learning applications are also of great importance in the field of agriculture. It is an issue that should be examined in terms of what the diagnosis of the disease is and its treatment in the disease of plants. A study on the detection of plant diseases was desired. This study is intended to be done using CNN and Alexnet methods. It was aimed to observe the success rates of two different methods. It is aimed to examine the effect of classification with and without feature extraction. In other words, it is aimed to examine the effect of deep layers that alexnet has. In this study, the solution of a 38-class problem was dealt with. 38-class test and 38-class train data are available. Each of the 38 classes contains approximately 1500 to 2000 pictures. And as a result of the study, 68500 of approximately 70000 pictures consist of healthy leaves and 1500 of them are pictures of sick leaves. this whole data set is separated and classified as 80% and 20%. While the accuracy obtained with CNN was 98.27%, the accuracy obtained with Alexnet was 98.49%. In conclusion it was seen that the use of Alexnet increased the accuracy rate.

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“…The structure of the NN is formed by a set of interconnected identical units (neurons). Through these interconnections, signals are sent from one neuron to another [129]. Furthermore, weights are attached to the interconnections so that delivery between the neurons is enhanced [130].…”

Section: ) Neural Network (Nn)mentioning

confidence: 99%

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

et al. 2022

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Every year, phishing results in losses of billions of dollars and is a major threat to the Internet economy. Phishing attacks are now most often carried out by email. To better comprehend the existing research trend of phishing email detection, several review studies have been performed. However, it is important to assess this is sue from different perspectives . None of the surveys have ever comprehensively studied the use of Natural Language Processing (NLP) techniques for detection of phishing except one that shed light on the use of NLP techniques for classification and training purposes , while exploring a few alternatives. To bridge the gap, this study aims to systematically review and synthesise research on the use of NLP for detecting phishing emails. Based on specific predefined criteria, a total of 100 research articles published between 2006 and 2022 were identified and analysed. We study the key research areas in phishing email detection using NLP, machine learning algorithms used in phishing detection email, text features in phishing emails, datasets and resources that have been used in phishing emails, and the evaluation criteria. The findings include that the main research area in phishing detection studies is feature extraction and selection, followed by methods for classifying and optimizing the detection of phishing emails. Amongst the range of classification algorithms, support vector machines (SVMs) are heavily utilised for detecting phishing emails . The most frequently used NLP techniques are found to be TF-IDF and word embeddings. Furthermore, the most commonly used datasets for benchmarking phishing email detection methods is the Nazario phishing corpus. Also, Python is the most commonly used one for phishing email detection. It is expected that the findings of this paper can be helpful for the scientific community, especially in the field of NLP application in cybersecurity problems. This survey also is unique in the sense that it relates works to their openly available tools and resources. The analysis of the presented works revealed that not much work had been performed on Arabic language phishing emails using NLP techniques. Therefore, many open issues are associated with Arabic phishing email detection.INDEX TERMS Phishing email detection; systematic literature review; natural language processing; machine learning.

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A distributed architecture for phishing detection using Bayesian Additive Regression Trees

Cited by 10 publications

References 12 publications

BART: Bayesian additive regression trees

BART: Bayesian additive regression trees

Classification of Plant Diseases with Machine Learning

A Systematic Literature Review on Phishing Email Detection Using Natural Language Processing Techniques

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