Comparison of machine learning techniques for spam detection

Ghosh, Argha; Rajan, A. Senthil

doi:10.1007/s11042-023-14689-3

Cited by 12 publications

(7 citation statements)

References 108 publications

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“…Recent work by Ghosh and Senthilrajan [25] classifies spam emails using machine learning classifiers and evaluates the performance of these classifiers. The authors implemented thirteen machine learning classifiers: the adaptive booster, artificial neural network, bootstrap aggregating, decision table, decision tree, J48, K-nearest neighbor, linear regression, logistic regression, naïve Bayes, random forest (RF), sequential minimal optimization, and SVM methods.…”

Section: Machine Learning Approachesmentioning

confidence: 99%

Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models

Altwaijry,

Al-Turaiki,

Alotaibi

et al. 2024

Sensors

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Phishing is one of the most dangerous attacks targeting individuals, organizations, and nations. Although many traditional methods for email phishing detection exist, there is a need to improve accuracy and reduce false-positive rates. Our work investigates one-dimensional CNN-based models (1D-CNNPD) to detect phishing emails in order to address these challenges. Additionally, further improvement is achieved with the augmentation of the base 1D-CNNPD model with recurrent layers, namely, LSTM, Bi-LSTM, GRU, and Bi-GRU, and experimented with the four resulting models. Two benchmark datasets were used to evaluate the performance of our models: Phishing Corpus and Spam Assassin. Our results indicate that, in general, the augmentations improve the performance of the 1D-CNNPD base model. Specifically, the 1D-CNNPD with Bi-GRU yields the best results. Overall, the performance of our models is comparable to the state of the art of CNN-based phishing email detection. The Advanced 1D-CNNPD with Leaky ReLU and Bi-GRU achieved 100% precision, 99.68% accuracy, an F1 score of 99.66%, and a recall of 99.32%. We observe that increasing model depth typically leads to an initial performance improvement, succeeded by a decline. In conclusion, this study highlights the effectiveness of augmented 1D-CNNPD models in detecting phishing emails with improved accuracy. The reported performance measure values indicate the potential of these models in advancing the implementation of cybersecurity solutions to combat email phishing attacks.

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Section: Machine Learning Approachesmentioning

confidence: 99%

Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models

Altwaijry,

Al-Turaiki,

Alotaibi

et al. 2024

Sensors

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“…Ghosh and Senthilrajan ( 2023 ) used the Spambase data set (Mark Hopkins, 1999 ) to develop a framework for email evaluation. The research focuses on the comparison of the performance of 13 different classifiers, including SVMs, considering a set of only eight attributes.…”

Section: Related Workmentioning

confidence: 99%

A distance-based kernel for classification via Support Vector Machines

Amaya-Tejera,

Gamarra,

Vélez

et al. 2024

Front. Artif. Intell.

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Support Vector Machines (SVMs) are a type of supervised machine learning algorithm widely used for classification tasks. In contrast to traditional methods that split the data into separate training and testing sets, here we propose an innovative approach where subsets of the original data are randomly selected to train the model multiple times. This iterative training process aims to identify a representative data subset, leading to improved inferences about the population. Additionally, we introduce a novel distance-based kernel specifically designed for binary-type features based on a similarity matrix that efficiently handles both binary and multi-class classification problems. Computational experiments on publicly available datasets of varying sizes demonstrate that our proposed method significantly outperforms existing approaches in terms of classification accuracy. Furthermore, the distance-based kernel achieves superior performance compared to other well-known kernels from the literature and those used in previous studies on the same datasets. These findings validate the effectiveness of our proposed classification method and distance-based kernel for SVMs. By leveraging random subset selection and a unique kernel design, we achieve notable improvements in classification accuracy. These results have significant implications for diverse classification problems in Machine Learning and data analysis.

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“…Finally, a new instance is predicted by combining the prediction of these trees (i.e., majority vote) [11]. Te RF algorithm reduces the correlation among the trees because the RF algorithm randomly chooses variables at each node, which helps to achieve an efcient prediction by this classifer [12]. Te RF algorithm has many decision trees, which makes it a robust and efcient algorithm [13].…”

Section: Random Forestmentioning

confidence: 99%

Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable

Alharbi

2024

Discrete Dynamics in Nature and Society

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Class noise is a common issue that affects the performance of classification techniques on real-world data sets. Class noise appears when a class variable in data sets has incorrect class labels. In the case of noisy data, the robustness of classification techniques against noise could be more important than the performance results on noise-free data sets. The decision tree method is one of the most popular techniques for classification tasks. The C4.5, CART, and random forest (RF) algorithms are considered to be three of the most used algorithms in decision trees. The aim of this paper is to reach conclusions on which decision tree algorithm is better to use for building decision trees in terms of its performance and robustness against class noise. In order to achieve this aim, we study and compare the performance of the models when applied to class variables with noise. The results obtained indicate that the RF algorithm is more robust to data sets with noisy class variable than other algorithms.

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Comparison of machine learning techniques for spam detection

Cited by 12 publications

References 108 publications

Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models

Advancing Phishing Email Detection: A Comparative Study of Deep Learning Models

A distance-based kernel for classification via Support Vector Machines

Classification Performance Analysis of Decision Tree-Based Algorithms with Noisy Class Variable

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