Improved Spam Email Filtering Architecture Using Several Feature Extraction Techniques

Prosun, Priyo Ranjan Kundu; Alam, Kazi Saeed; Bhowmik, Shovan

doi:10.1007/978-981-16-6636-0_50

Cited by 3 publications

(5 citation statements)

References 15 publications

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“…Prosun, Alam, and Bhowmik [65] discussed two voting architectures based on ML models and ensemble classifiers. The authors investigated the performance of numerous ensemble approaches and individual classifiers utilizing various feature retrieval algorithms.…”

Section: Research Papers Published In 2022mentioning

confidence: 99%

A Systematic Review on Deep-Learning-Based Phishing Email Detection

Thakur,

Ali,

Obaidat

et al. 2023

Electronics

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Phishing attacks are a growing concern for individuals and organizations alike, with the potential to cause significant financial and reputational damage. Traditional methods for detecting phishing attacks, such as blacklists and signature-based techniques, have limitations that have led to developing more advanced techniques. In recent years, machine learning and deep learning techniques have gained attention for their potential to improve the accuracy of phishing detection. Deep learning algorithms, such as CNNs and LSTMs, are designed to learn from patterns and identify anomalies in data, making them more effective in detecting sophisticated phishing attempts. To develop a comprehensive understanding of the current state of research on the use of deep learning techniques for phishing detection, a systematic literature review is necessary. This review aims to identify the various deep learning techniques used for phishing detection, their effectiveness, and areas for future research. By synthesizing the findings of relevant studies, this review identifies the strengths and limitations of different approaches and provides insights into the challenges that need to be addressed to improve the accuracy and effectiveness of phishing detection. This review aims to contribute to developing a coherent and evidence-based understanding of the use of deep learning techniques for phishing detection. The review identifies gaps in the literature and informs the development of future research questions and areas of focus. With the increasing sophistication of phishing attacks, applying deep learning in this area is a critical and rapidly evolving field. This systematic literature review aims to provide insights into the current state of research and identify areas for future research to advance the field of phishing detection using deep learning.

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Section: Research Papers Published In 2022mentioning

confidence: 99%

A Systematic Review on Deep-Learning-Based Phishing Email Detection

Thakur,

Ali,

Obaidat

et al. 2023

Electronics

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“…The most prevalent phishing email detection algorithms are supervised approaches, such as support vector machines (SVM) [44]- [50], logistic regression (LR) [44], [45], [48], [51]- [56], Decision Tree (DT) [48]- [50], [57]- [62], and Naïve Bayes (NB) [44], [63]- [65]. Unsupervised approaches such as kmeans clustering [48], [66]- [70] and deep learning methods have also been adopted [45], [51], [52], [63], [71]- [82].…”

Section: B Machine Learning Techniquesmentioning

confidence: 99%

“…They are attained through consideration of the voted classes for each specific individual tree, and the class which attains the highest number of vote is considered the output. Similar issues within literature are resolved using the RF method [44], [46], [49], [54], [56]- [58], [61], [71], [73], [84]- [102]. RF details can further be attained using [103], [104].…”

Section: ) Random Forest (Rf)mentioning

confidence: 99%

“…Quick convergence and simplicity are the classifiers benefits, yet it is not possible to understand the associations and interactions amongst the features of each of the samples. The following papers [44], [50], [53]- [56], [58], [61], [63], [64], [66], [71], [73], [77], [80], [85], [87], [89], [91], [94], [97], [101], [102], [105]- [114] have reported the use of NB to enhance the textual features in phishing email detection.…”

Section: ) Naïve Bayes (Nb)mentioning

confidence: 99%

“…Yet, it is difficult to interpret the SVM, and it is quite memory sensitive. We noticed that numerous scientific papers, such as [44]- [50], [53]- [56], [58], [61], [63], [66], [70], [71], [73], [80], [87]- [89], [91], [94], [96], [97], [99], [101], [102], [106]- [110], [112], [114]- [128] have used SVM algorithm to detect phishing emails.…”

Section: ) Support Vector Machine (Svm)mentioning

confidence: 99%

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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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