Email User Classification and Topic Modeling

Shah, Krupal; Shah, Nirav R.; Shah, Shaival; Patel, Dip

doi:10.1007/978-3-030-63128-4_27

Cited by 2 publications

(2 citation statements)

References 2 publications

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“…Another research uses histogram matching for face recognition which can handle variations in illumination and expression [8]. Further features like histogram are explored by [9] [10] where KNN algorithm is used for classification. An image classification technique based on gabor filters is proposed in [11] where local binary patterns are used to represent the histograms.…”

Section: Introductionmentioning

confidence: 99%

Image Classifier based on Histogram Matching and Outlier Detection using Hellinger distance

Gupta

2023

J. Appl. Data Sci.

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Thoracic diseases, including tuberculosis, pneumonia, lung cancer etc., affect millions of people worldwide every year. Interpretation of chest radiograph for the detection of disease is a time-consuming task and typically requires expert radiologists to interpret the images. In this paper, an effort has been made to develop a prediction model based on histogram matching of Chest X-ray images which can be used as a replacement of human-level interpretation task. Hellinger distance metric is used to match two histograms. The chest x-ray images are pre-processed and converted to histograms. A benchmark histogram is obtained by finding the average of all pixel intensity values. Then outlier images are detected by comparing the histogram of an image with the benchmark histogram using the hellinger metric. Finally, a prediction method is proposed which matches the histogram of unseen images to histograms of nearest neighbor images. Hypertuning of input parameters to the proposed prediction method is performed to get the best set of parameters. The proposed model gives an accuracy of 92.3 % and F1 score of 94.6 % on the training set, accuracy of 86.2% and F1 score of 89.6% on the test set. The performance of the proposed model is better than the existing techniques in this domain.

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Section: Introductionmentioning

confidence: 99%

Image Classifier based on Histogram Matching and Outlier Detection using Hellinger distance

Gupta

2023

J. Appl. Data Sci.

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“…Word2Vec [13] is a popular method for the creation of word embeddings, namely vector representations of a word, which has seen a few applications in the phishing email detection for the identification of word associations between different emails of an email corpus [14] [15]. Furthermore, recent advances in ML, such as the emergence of a new language model known as Bidirectional Encoder Representations from Transformers (BERT) [16], have revealed promising results in an wide range of classification problems, like malware detection [17] [18] [19] and email user classification [20]. BERT's key innovation is that it can learn the context of a particular word considering both the previous and next words.…”

Section: Introductionmentioning

confidence: 99%

A Comparison of Natural Language Processing and Machine Learning Methods for Phishing Email Detection

Bountakas

Koutroumpouchos

Xenakis

2021

Proceedings of the 16th International Conference on Availability, Reliability and Security

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Phishing is the most-used malicious attempt in which attackers, commonly via emails, impersonate trusted persons or entities to obtain private information from a victim. Even though phishing email attacks are a known cybercriminal strategy for decades, their usage has been expanded over last couple of years due to the COVID-19 pandemic, where attackers exploit people's consternation to lure victims. Therefore, further research is needed in the phishing email detection field. Recent phishing email detection solutions that extract representational text-based features from the email's body have proved to be an appropriate strategy to tackle these threats. This paper proposes a comparison approach for the combined usage of Natural Language Processing (TF-IDF, Word2Vec, and BERT) and Machine Learning (Random Forest, Decision Tree, Logistic Regression, Gradient Boosting Trees, and Naive Bayes) methods for phishing email detection. The evaluation was performed on two datasets, one balanced and one imbalanced, both of which were comprised of emails from the well-known Enron corpus and the most recent emails from the Nazario phishing corpus. The best combination in the balanced dataset proved to be the Word2Vec with the Random Forest algorithm, while in the imbalanced dataset the Word2Vec with the Logistic Regression algorithm.

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Email User Classification and Topic Modeling

Cited by 2 publications

References 2 publications

Image Classifier based on Histogram Matching and Outlier Detection using Hellinger distance

Image Classifier based on Histogram Matching and Outlier Detection using Hellinger distance

A Comparison of Natural Language Processing and Machine Learning Methods for Phishing Email Detection

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