Evading obscure communication from spam emails

Rafat, Khan Farhan; Xin, Qin; Javed, Abdul Rehman; Jalil, Zunera; Ahmad, Rana Zeeshan

doi:10.3934/mbe.2022091

Cited by 10 publications

(3 citation statements)

References 37 publications

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“…The study proposed a DL-based approach that consistently outperforms standard ML models in detecting malicious content. Although the results showed the power of DL algorithms over the standard ML in filtering spam, the effects were unsatisfactory for detecting encrypted communication for both forms of algorithms [14]. The study need to be linked with the evaluation of machine learning algorithms in detecting malware-based phishing attacks.…”

Section: Rafatet Al (2021)mentioning

confidence: 99%

Evaluation of Machines Learning Algorithms in Detection of Malware-based Phishing Attacks for Securing E-Mail Communication

Kisambu¹,

Mjahidi²

2022

Artificial Intelligence and Machine Learning

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Malicious software, commonly known as Malware is one of the most significant threats facing Internet users today. Malware-based phishing attacks are among the major threats to Internet users that are difficult to defend against because they do not appear to be malicious in nature. There were several initiatives in combating phishing attacks but there are many difficulties and obstacles encountered. This study deals with evaluation of machine learning algorithms in detection of malware-based phishing attacks for securing email communication. It deeply evaluate the efficacy of the algorithms when integrated with major open-source security mail filters with different mitigation techniques. The main classifiers used such as SVM, KNN, Logistic Regression and Naïve Bayes were evaluated using performance metrics namely accuracy, precision, recall and f-score. Based on the findings, the study proposed improvement for securing e-mail communication against malware-based phishing using the best performing machine-learning algorithm to keep pace with malware evolution.

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Section: Rafatet Al (2021)mentioning

confidence: 99%

Evaluation of Machines Learning Algorithms in Detection of Malware-based Phishing Attacks for Securing E-Mail Communication

Kisambu¹,

Mjahidi²

2022

Artificial Intelligence and Machine Learning

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“…Rafat et al [69] discussed the text pre-processing impact on email classification using ML and DL algorithms. They used the Spamassassin corpus and compared the results of ML and DL algorithms, with and without text pre-processing.…”

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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“…With text analysis tools, businesses can structure vast amounts of information, such as emails, chats, social media, support tickets, documents, and so on, in seconds instead of days. Therefore, we can dedicate more resources to critical tasks (Hina et al, 2021b ; Rafat et al, 2022 ).…”

Section: Introductionmentioning

confidence: 99%

An efficient approach for textual data classification using deep learning

Alqahtani

Khan

Alsubai

et al. 2022

Front. Comput. Neurosci.

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Text categorization is an effective activity that can be accomplished using a variety of classification algorithms. In machine learning, the classifier is built by learning the features of categories from a set of preset training data. Similarly, deep learning offers enormous benefits for text classification since they execute highly accurately with lower-level engineering and processing. This paper employs machine and deep learning techniques to classify textual data. Textual data contains much useless information that must be pre-processed. We clean the data, impute missing values, and eliminate the repeated columns. Next, we employ machine learning algorithms: logistic regression, random forest, K-nearest neighbors (KNN), and deep learning algorithms: long short-term memory (LSTM), artificial neural network (ANN), and gated recurrent unit (GRU) for classification. Results reveal that LSTM achieves 92% accuracy outperforming all other model and baseline studies.

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Evading obscure communication from spam emails

Cited by 10 publications

References 37 publications

Evaluation of Machines Learning Algorithms in Detection of Malware-based Phishing Attacks for Securing E-Mail Communication

Evaluation of Machines Learning Algorithms in Detection of Malware-based Phishing Attacks for Securing E-Mail Communication

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

An efficient approach for textual data classification using deep learning

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