Detection Method of Blog Spam Based on Categorization and Time Series Information

Teraguchi, Toshio; Nakamura, Kenji; Tanaka, Shigenori; Kitano, Koichi

doi:10.1109/waina.2012.217

Cited by 2 publications

(2 citation statements)

References 3 publications

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“…Bayesian methods, such as Naive Bayes, are regarded as the effective and important machine learning algorithms in information retrieval. Teraguchi et al [17] proposed a Bayesian algorithm to defeat spammers. To enhance its accuracy, Bayesian methods are often hybrid with other algorithms.…”

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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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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“…In the paper Teraguchi et al (2012), Bayesian algorithm was proposed to defect the spammers. Since spam keywords are constantly changed, this paper is evaluated by a time-series algorithm.…”

Section: Related Workmentioning

confidence: 99%

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

EBADATI

Ahmadzadeh

2019

J. Info. Know. Mgmt.

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Email spam is a security problem that involves different techniques in machine learning to solve this problem. The rise of this security issue makes organisation email service unreliable and has a direct relation with vulnerability of clients through unexpected spam mails, like ransomware. There are several methods to identifying spam emails. Most of these methods focused on feature selection; however, these models decreased the accuracy of the detection. This paper proposed a novel spam detection method that is not only to decrease the accuracy, but eliminates unsuitable features with less processing. The features are in the terms of contents, and the number of features is very big, so it can decrease the memory complexity. We use Hewlett-Packet (HP) laboratory samples text emails. First, GA algorithm is employed to select features without limited number of feature selection with the aid of Bayesian theory as a fitness function and checked with a different number of repetitions. The result improved with GA by increasing number of repetitions, and tested with distinctive selection method, Random selection and Tournament selection. In the second stage, the dataset classifies emails as Spam or Ham by Naive Bayes. The results show that Naive Bayes and hybrid GA-Naive Bayes are almost identical, but GA-Naive Bayes has a better performance.

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Detection Method of Blog Spam Based on Categorization and Time Series Information

Cited by 2 publications

References 3 publications

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

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

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

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