A Machine Learning based Spam Detection Mechanism

Govil, Nikhil; Agarwal, Kavita; Bansal, Ashi; Varshney, Astha

doi:10.1109/iccmc48092.2020.iccmc-000177

Cited by 23 publications

(5 citation statements)

References 4 publications

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“…Numerous machine learning and deep learning-based spam email detection and classification applications have been carried out over the past few decades by many researchers. In such studies [9][10][11][12][13][14], authors have proposed, reviewed, and evaluated spam filtering models where the classification models are based on traditional machine learning algorithms, i.e., Naïve Bayes, Random Forest, SMV mostly. Govil et al [9] have created a dictionary, named "stopwards" for removing the helping verbs from email.…”

Section: Machine Learning and Deep Learning Based Methodsmentioning

confidence: 99%

“…In such studies [9][10][11][12][13][14], authors have proposed, reviewed, and evaluated spam filtering models where the classification models are based on traditional machine learning algorithms, i.e., Naïve Bayes, Random Forest, SMV mostly. Govil et al [9] have created a dictionary, named "stopwards" for removing the helping verbs from email. Then, the algorithm is executed for checking the possibility of being spam or not.…”

Section: Machine Learning and Deep Learning Based Methodsmentioning

confidence: 99%

“…Then, the algorithm is executed for checking the possibility of being spam or not. A machine learning classifier, Naïve Bayes has been applied for the identification purpose where non-spam emails were classified as spam, 1 and non-spam, 0 [9]. Similarly, Chen et al [10] have evaluated machine learning algorithms for detecting spam tweets.…”

Section: Machine Learning and Deep Learning Based Methodsmentioning

confidence: 99%

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An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach

Jamal,

Wimmer,

Sarker

2023

Preprint

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Phishing and spam detection is a long standing challenge that has been the subject of much academic research. Large Language Models (LLM) have vast potential to transform society and provide new and innovative approaches to solve well-established challenges. Phishing and spam have caused financial hardships and lost time and resources to email users all over the world and frequently serve as an entry point for ransomware threat actors. While detection approaches exist, especially heuristic-based approaches, LLMs offer the potential to venture into a new unexplored area for understanding and solving this challenge. LLMs have rapidly altered the landscape from business, consumers, and throughout academia and demonstrate transformational potential for the potential of society. Based on this, applying these new and innovative approaches to email detection is a rational next step in academic research. In this work, we present IPSDM, an improved phishing spam detection model based on fine-tuning the BERT family of models to specifically detect phishing and spam email. We demonstrate our fine-tuned version, IPSDM, is able to better classify emails in both unbalanced and balanced datasets.

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Section: Machine Learning and Deep Learning Based Methodsmentioning

confidence: 99%

Section: Machine Learning and Deep Learning Based Methodsmentioning

confidence: 99%

Section: Machine Learning and Deep Learning Based Methodsmentioning

confidence: 99%

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An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach

Jamal,

Wimmer,

Sarker

2023

Preprint

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“…The NB algorithm calculates the probability rate of the emails and classi es them as spam or ham. Compared to other ML algorithms, the NB gives low performance and works well for email-based spam detection 9 . Mehul Gupta et al study spam detection in SMS by using ML algorithms.…”

Section: Literature Reviewmentioning

confidence: 99%

Machine learning algorithm-based spam detection in social networks

Sumathi,

Raja

2023

Soc. Netw. Anal. Min.

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Many social media platforms have emerged as a result of the online social network's (OSN) rapid expansion. They have become important in day-to-day life, and spammers have turned their attention to them. Spam detection is done in two different ways, such as machine learning (ML) and expert-based detection. The expert-based detection technique's accuracy depends on expert knowledge, and the manual process is a time consuming task. Thus, ML-based spam detection is preferred in OSN. Spam identi cation on social networks is a di cult operation involving a variety of factors, and spam and ham have resulted in an imbalanced data distribution, which gives an advantage to spammers for corrupting our devices. Spam detection based on ML algorithms like Logistic Regression (LR), K-Nearest Neighbor (KNN), Decision Trees (DT), Random Forest (RF), Support Vector Machine (SVM), and XGB, Voting Classi er (VC), and many other algorithms are used to design the address balance and to attain high assessment accuracy. There is a non-balance issue. Text is vectorized by vectorizers and all the relative results are stored. The experimental result shows that, as compared to KN, NB, ETC, RF, SVC, LR, XGB, and DT, the proposed VC provides a higher classi cation accuracy rate of 97.96%. The proposed methods are effective in identifying balanced and imbalanced datasets, as evidenced by the validation results. The website was created to detect messages as spam or not.

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“…Also, public emails used in this research are collected from different advertisements and newsletters. Govil et al [30] proposed an algorithm to generate a dictionary and features and train them through a machine learning mechanism. Authors create a library named "stopwords" to remove all helping verbs from the content of the emails.…”

Section: Related Studiesmentioning

confidence: 99%

Email Filtering Using Hybrid Feature Selection Model

Mohammad¹,

Smadi²,

Alwada’n³

2022

Computer Modeling in Engineering &Amp; Sciences

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Undoubtedly, spam is a serious problem, and the number of spam emails is increased rapidly. Besides, the massive number of spam emails prompts the need for spam detection techniques. Several methods and algorithms are used for spam filtering. Also, some emergent spam detection techniques use machine learning methods and feature extraction. Some methods and algorithms have been introduced for spam detecting and filtering. This research proposes two models for spam detection and feature selection. The first model is evaluated with the email spam classification dataset, which is based on reducing the number of keywords to its minimum. The results of this model are promising and highly acceptable. The second proposed model is based on creating features for spam detection as a first stage. Then, the number of features is reduced using three well-known metaheuristic algorithms at the second stage. The algorithms used in the second model are Artificial Bee Colony (ABC), Ant Colony Optimization (ACO), and Particle Swarm Optimization (PSO), and these three algorithms are adapted to fit the proposed model. Also, the authors give it the names AABC, AACO, and APSO, respectively. The dataset used for the evaluation of this model is Enron. Finally, well-known criteria are used for the evaluation purposes of this model, such as true positive, false positive, false negative, precision, recall, and F-Measure. The outcomes of the second proposed model are highly significant compared to the first one.

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A Machine Learning based Spam Detection Mechanism

Cited by 23 publications

References 4 publications

An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach

An Improved Transformer-based Model for Detecting Phishing, Spam, and Ham: A Large Language Model Approach

Machine learning algorithm-based spam detection in social networks

Email Filtering Using Hybrid Feature Selection Model

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