Phishing Detection in E-mails using Machine Learning

Saraswat, Pankaj; Solanki, Madhav Singh

doi:10.1109/ictacs56270.2022.9987839

Cited by 1 publication

(1 citation statement)

References 12 publications

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“…Many machine-learning (ML) techniques exist to classify emails into predefined categories, such as supervised ML, semi-supervised ML, unsupervised ML, content-based learning, and statistical learning, [2]. Some of the algorithms used supervised learning concept are support vector machine (SVM), genetic algorithms (GA) [3], decision trees (DT) [4], random forest (RF) [5], Naïve bayes (NB) [6], k-nearest neighbor (KNN) [7], and artificial neural network (ANN) [8]. The unstructured, noisy, and highly dimensional data in each email makes it difficult to develop an email classifier for a real-time dataset.…”

Section: Introductionmentioning

confidence: 99%

Email Net: Efficient Email Classification Approach Based on Graph Similarity Measure

2023

IJIES

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In this Internet Era, Email is a vital form of communication for many academic, personal, and professional users. Despite the availability of alternative means of communication, such as social networks, electronic messages, and mobile apps, email remains an integral part of communication. Email auto-management techniques are necessary for a variety of reasons, such as saving users valuable time, dealing with high-dimensional data, and making email communication easier and more accessible. In this work, a novel email net (improved elephant herd optimization and Graph similarity with the Jaccard index) technique has been proposed for efficient email classification based on graph similarity measure. Initially, the dataset is pre-processed using NLP techniques such as removing email signatures, removing punctuations, removing stop-words, lowercase conversion, tokenization, and stemming for removing irrelevant data. After pre-processing the feature are extracted using bag of words and term frequencyinverse document frequency (TF-IDF). These extracted features are given as input to improved Elephant herding optimization (EHO) for selecting the most relevant features to build a graph-based similarity index for classifying each category of e-mail. The proposed email net was tested on a benchmark dataset and a real-time dataset. Also, the proposed method's performance is compared with other classifiers. According to the experimental results, the proposed approach outperforms all other classifiers with a 98.82% of accuracy.

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Section: Introductionmentioning

confidence: 99%

Email Net: Efficient Email Classification Approach Based on Graph Similarity Measure

2023

IJIES

View full text Add to dashboard Cite

show abstract

Phishing Detection in E-mails using Machine Learning

Cited by 1 publication

References 12 publications

Email Net: Efficient Email Classification Approach Based on Graph Similarity Measure

Email Net: Efficient Email Classification Approach Based on Graph Similarity Measure

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