Classification Spam Email with Elimination of Unsuitable Features with Hybrid of GA-Naive Bayes

EBADATI, Omid Mahdi; Ahmadzadeh, Farzaneh

doi:10.1142/s0219649219500084

Cited by 13 publications

(6 citation statements)

References 28 publications

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“…Some more complex algorithms or systems [18][19][20] are proposed attempting to enhance the performance of supervised email classification. However, previous study 21 figured out that there is a lack of empirical study to investigate the practical performance of supervised learning, as most existing studies usually adopt datasets for performance evaluation.…”

Section: Motivationmentioning

confidence: 99%

An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors

Meng

et al. 2021

Int J Intell Syst

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Internet of Things (IoT) is gradually adopted by many organizations to facilitate the information collection and sharing. In an organization, an IoT node usually can receive and send an email for event notification and reminder. However, unwanted and malicious emails are a big security challenge to IoT systems. For example, attackers may intrude a network by sending emails with phishing links. To mitigate this issue, email classification is an important solution with the aim of distinguishing legitimate and spam emails. Artificial intelligence especially machine learning is a major tool for helping detect malicious emails, but the performance might be fluctuant according to specific datasets. The previous research figured out that supervised learning could be acceptable in practice, and that practical evaluation and users' feedback are important. Motivated by these observations, we conduct an empirical study to validate the performance of common learning algorithms under three different environments for email classification. With over 900 users, our study results validate prior observations and indicate that LibSVM and SMO-SVM can achieve better performance than other selected algorithms.

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Section: Motivationmentioning

confidence: 99%

An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors

Meng

et al. 2021

Int J Intell Syst

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“…Sable et al [7] introduced a hybrid system of SMS classification based on a naïve Bayes classifier and Apriori Algorithm. Ebadati and Ahmadzadeh [22] proposed a genetic algorithm (GA)-naïve Bayes for spam email detection with a genetic algorithm (GA) for feature extraction. Arifin at al.…”

Section: Content-filtering Technologiesmentioning

confidence: 99%

A Discrete Hidden Markov Model for SMS Spam Detection

Xia

Chen

2020

Applied Sciences

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Many machine learning methods have been applied for short messaging service (SMS) spam detection, including traditional methods such as naïve Bayes (NB), vector space model (VSM), and support vector machine (SVM), and novel methods such as long short-term memory (LSTM) and the convolutional neural network (CNN). These methods are based on the well-known bag of words (BoW) model, which assumes documents are unordered collection of words. This assumption overlooks an important piece of information, i.e., word order. Moreover, the term frequency, which counts the number of occurrences of each word in SMS, is unable to distinguish the importance of words, due to the length limitation of SMS. This paper proposes a new method based on the discrete hidden Markov model (HMM) to use the word order information and to solve the low term frequency issue in SMS spam detection. The popularly adopted SMS spam dataset from the UCI machine learning repository is used for performance analysis of the proposed HMM method. The overall performance is compatible with deep learning by employing CNN and LSTM models. A Chinese SMS spam dataset with 2000 messages is used for further performance evaluation. Experiments show that the proposed HMM method is not language-sensitive and can identify spam with high accuracy on both datasets.

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“…To enhance its accuracy, Bayesian methods are often hybrid with other algorithms. Ebadati and Ahmadzadeh [18] proposed a GA-Naive Bayes for spam email detection with GA algorithm for feature section. Arifin et al [19] focused on spam detection for SMS by Naive Bayes Classifier and FP-Growth since FP-Growth is utilized for mining frequent patterns.…”

Section: A the Content Filtering Technologiesmentioning

confidence: 99%

A Constant Time Complexity Spam Detection Algorithm for Boosting Throughput on Rule-Based Filtering Systems

Xia

2020

IEEE Access

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Along with the barbarous growth of spams, anti-spam technologies including rule-based approaches and machine-learning thrive rapidly as well. In antispam industry, the rule-based systems (RBS) becomes the most prominent methods for fighting spam due to its capability to enrich and update rules remotely. However, the antispam filtering throughput is always a great challenge of RBS. Especially, the explosively spreading of obfuscated words leads to frequent rule update and extensive rule vocabulary expansion. These incremental obfuscated words make the filtering speed slow down and the throughput decrease. This paper addresses the challenging throughput issue and proposes a constant time complexity rule-based spam detection algorithm. The algorithm has a constant processing speed, which is independent of rule and its vocabulary size. A new special data structure, namely, Hash Forest, and a rule encoding method are developed to make constant time complexity possible. Instead of traversing each spam term in rules, the proposed algorithm manages to detect spam terms by checking a very small portion of all terms. The experiment results show effectiveness of proposed algorithm. INDEX TERMS Constant time complexity, hash forest, rule-based filtering, spam detection, throughput.

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Classification Spam Email with Elimination of Unsuitable Features with Hybrid of GA-Naive Bayes

Cited by 13 publications

References 28 publications

An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors

An empirical study of supervised email classification in Internet of Things: Practical performance and key influencing factors

A Discrete Hidden Markov Model for SMS Spam Detection

A Constant Time Complexity Spam Detection Algorithm for Boosting Throughput on Rule-Based Filtering Systems

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