Hybrid simple artificial immune system (SAIS) and particle swarm optimization (PSO) for spam detection

Salehi, Saber; Selamat, Ali

doi:10.1109/mysec.2011.6140655

Cited by 14 publications

(10 citation statements)

References 15 publications

(17 reference statements)

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“…It can reduce the errors of classification and increase its accuracy against spam mail. Table III compares the performance and FP of proposed GA-ANN with SAIS-PSO (Hybrid of Simple Artificial Immune System and Particle Swarm Optimization) [14] for specific spambase dataset. The comparison shows that the proposed GA-ANN is better than SAIS-PSO in terms of Accuracy and FP.…”

Section: Resultsmentioning

confidence: 99%

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Spam detection using hybrid Artificial Neural Network and Genetic algorithm

Arram

Mousa

Zainal

2013

2013 13th International Conference on Intellient Systems Design and Applications

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Spam detection is one of the major problems which considered by many researchers by different developed strategies. Artificial Neural Network (ANN) is one of many others being proposed. However designing an ANN is a difficult task as it requires setting of ANN structure and tuning of some complex parameters. In this study, ANN was hybridized with Genetic algorithm (GA) in order to optimize the performance of ANN for spam detection. GA was used to determine some ANN parameters and suggest optimum weights to efficiently enhance the ANN learning. Experimental results show that the hybrid ANN and GA has superior performance when compared to conventional ANN.

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Section: Resultsmentioning

confidence: 99%

“…In spam detection, many evaluation methods or criteria have been designed for measuring performance of different filters such as: spam recall, FB measure, accuracy, and spam precision [14,15]. Accuracy (A) can reflect the overall performance of filters.…”

Section: B Evaluation Criteriamentioning

confidence: 99%

Spam detection using hybrid Artificial Neural Network and Genetic algorithm

Arram

Mousa

Zainal

2013

2013 13th International Conference on Intellient Systems Design and Applications

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“…The filter takes the whitelist and compares it with the message header and if spam is detected it sends it to the spam folder. In [2]- [4], to provide accurate classification result in email the author combines rough set theory and TF-IDF (Term Frequency-Inverse Document Frequency). In [5]- [8], the technique used is K-means Filter based on local concentration.…”

Section: Introductionmentioning

confidence: 99%

Detection of Spam Messages in Social Networks based on SVM

Pathan¹,

Goudar²

2016

IJCA

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Social networks are platforms through which people communicate and share information. Some users commonly known as spammers are misusing these platforms for spreading unsolicited messages commonly known as spam messages. Due to the advancement of internet, it is very difficult to detect spam messages and fake profiles. This research article presents the use of a machine learning algorithm such SVM (Support Vector Machine), which is based on statistical learning methods to detect spam in social networks. This paper also evaluates the classification efficiency of Non Linear SVM using RBS (Radial Basis Function) Kernel.

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“…In this regard, classification of email is an important and growing task that concluded from the spam email's menace [1,3]. Several spam filtering techniques have been introduced by using machine learning approaches including SVM [4], GA [5], AIS [1,6,7], case based technique [8] and ANN [9] to combat spam messages.…”

Section: Introductionmentioning

confidence: 99%

Fuzzy Granular Classifier Approach for Spam Detection

Salehi

Selamat

Krejcar

et al. 2015

Computational Collective Intelligence

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Abstract.Spam email problem is a major shortcoming of email technology for computer security. In this research, a granular classifier model is proposed to discover hyperboxes in the geometry of information granules for spam detection in three steps. In the first step, the k-means clustering algorithm is applied to find the seed_points to build the granular structure of the spam and non-spam patterns. Moreover, applying the interval analysis through the high homogeneity of the patterns captures the key part of the spam and non-spam classifiers' structure. In the second step, PSO algorithm is hybridized with the k-means to optimize the formalized information granules' performance. The proposed model is evaluated based on the accuracy, misclassification and coverage criteria. Experimental results reveal that the performance of our proposed model is increased through applying Particle Swarm Optimization and fuzzy set.

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Hybrid simple artificial immune system (SAIS) and particle swarm optimization (PSO) for spam detection

Cited by 14 publications

References 15 publications

Spam detection using hybrid Artificial Neural Network and Genetic algorithm

Spam detection using hybrid Artificial Neural Network and Genetic algorithm

Detection of Spam Messages in Social Networks based on SVM

Fuzzy Granular Classifier Approach for Spam Detection

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