E-mail spam classification using S-cuckoo search and support vector machine

Kumaresan, T.; Palanisamy, C.

doi:10.1504/ijbic.2017.083677

Cited by 28 publications

(12 citation statements)

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“…The authors in [45], designed a spam categorization technique using a modified cuckoo search to enhance the spam classification. In their work, the step size-cuckoo search was utilized for feature extraction, and the SVM was used for classification.…”

Section: Phishing and Spam Detectionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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Artificial intelligence techniques have grown rapidly in recent years, and their applications in practice can be seen in many fields, ranging from facial recognition to image analysis. In the cybersecurity domain, AI-based techniques can provide better cyber defense tools and help adversaries improve methods of attack. However, malicious actors are aware of the new prospects too and will probably attempt to use them for nefarious purposes. This survey paper aims at providing an overview of how artificial intelligence can be used in the context of cybersecurity in both offense and defense.

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Section: Phishing and Spam Detectionmentioning

confidence: 99%

Artificial Intelligence in the Cyber Domain: Offense and Defense

2020

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“…Kumaresan and Palanisamy [5] adapted the algorithm of Cuckoo Search by adding the StepSize feature to it and introduced as Cuckoo Search with Stepsize (SCS) approach. In this work, authors have also used the classification approach of SVM.…”

Section: Related Workmentioning

confidence: 99%

Email Spam Detection using Ensemble Methods

Bhardwaj¹,

Sharma²

2019

IJRTE

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The swiftly growth of spam email has escalated the need to upgrade the existing spam detection and filtration methods. There is the existence of several machine learning methods for the classification and detection of email spam but these lacks in some cases. In this research work ensemble methods are adapted to detect the email spam. The machine learning methods of Multinomial Naïve Bayes and J48 Decision Tree algorithms are considered and ensembled. The considered ensemble methods are bagging and boosting. The experimentation is conducted on the dataset of CSDMC2010 Spam corpus. The results for the considered dataset are evaluated using individual classifiers, bagging, and boosting ensemble approaches. The system performance is accessed in terms of precision, recall, f-measure, and accuracy. The experimental outcomes indicates the distinguish results for the detection of email spam using ensemble methods.

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“…I and table II in terms of sensitivity and accuracy indicates that the boosting approach outperformed than the individual results of multinomial naive bayes and decision tree J48 approach. The evaluated results of bare and lemm category using boosting approach are further compared with existing algorithm of Stepsize Cuckoo Search (SCS) [7] for both the bare and lemm category. The comparison results for bare and lemm categories are illustrated by fig.…”

Section: (1) Bare Categorymentioning

confidence: 99%

“…In this boosting approach, the weakness of one classifiers is replaced with another and vice-versa to achieve the overall effective performance results. [7] adapted the algorithm of Cuckoo Search by adding the StepSize feature to it and introduced as Cuckoo Search with Stepsize (SCS) approach. In this work, authors have also used the classification approach of SVM.…”

Section: Introductionmentioning

confidence: 99%

Detection of Email Spam using an Ensemble based Boosting Technique

Bhardwaj¹,

Sharma²

2019

IJITEE

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Email is amongst the advanced socializing communication source that is mostly adapted by business and commercial users. Although there are various assets of email as it is quick, cheap, and efficient communication resource, the service is misused by various users for the personal or professional purposes by spreading the useless and extra emails which are termed as email spam. There is the existing research of authors who have used machine learning methods for the detection of email spam and achieved effective precision values, but the individual methods mostly noted with some shortcomings which leads to lack in the performance. In this research work, ensemble based boosting technique on machine learning classifiers of Multinomial Naïve Bayes and J48 classifiers is proposed for the detection of email spam. The system detects email spam by adding the strong features of one classifier into another with the help of Adaboost algorithm. The results of the proposed ensemble technique are evaluated with accuracy and sensitivity parameters. The evaluated results indicate the effectiveness of proposed concept to detect the email spam in comparison with individual methods and other considered existing concepts.

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E-mail spam classification using S-cuckoo search and support vector machine

Cited by 28 publications

References 0 publications

Artificial Intelligence in the Cyber Domain: Offense and Defense

Artificial Intelligence in the Cyber Domain: Offense and Defense

Email Spam Detection using Ensemble Methods

Detection of Email Spam using an Ensemble based Boosting Technique

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