Enhancement of spam detection mechanism based on hybrid $$\varvec{k}$$ k -mean clustering and support vector machine
| LEADER | caa a22 4500 | ||
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| 001 | 605470855 | ||
| 003 | CHVBK | ||
| 005 | 20210128100330.0 | ||
| 007 | cr unu---uuuuu | ||
| 008 | 210128e20151101xx s 000 0 eng | ||
| 024 | 7 | 0 | |a 10.1007/s00500-014-1479-2 |2 doi |
| 035 | |a (NATIONALLICENCE)springer-10.1007/s00500-014-1479-2 | ||
| 245 | 0 | 0 | |a Enhancement of spam detection mechanism based on hybrid $$\varvec{k}$$ k -mean clustering and support vector machine |h [Elektronische Daten] |c [Nadir Elssied, Othman Ibrahim, Ahmed Osman] |
| 520 | 3 | |a Spam e-mails are considered a serious violation of privacy. It has become costly and unwanted communication. Support vector machine (SVM) has been widely used in e-mail spam classification, yet the problem of dealing with huge amounts of data results in low accuracy and time consumption as many researches have demonstrated. This paper proposes a hybrid approach for e-mail spam classification based on the SVM and $$k$$ k -mean clustering. The experiment of the proposed approach was carried out using spambase standard dataset to evaluate the feasibility of the proposed method. The result of this combination led to improve SVM and accordingly increase the accuracy of spam classification. The accuracy based on SVM algorithm is 96.30% and the proposed hybrid SVM algorithm with $$k$$ k -mean clustering is 98.01%. In addition, experimental results on spambase datasets showed that improved SVM (ESVM) significantly outperforms SVM and many other recent spam classification methods. | |
| 540 | |a Springer-Verlag Berlin Heidelberg, 2014 | ||
| 690 | 7 | |a $$k$$ k -mean clustering |2 nationallicence | |
| 690 | 7 | |a Mechanism |2 nationallicence | |
| 690 | 7 | |a Non-spam |2 nationallicence | |
| 690 | 7 | |a Spam detection |2 nationallicence | |
| 690 | 7 | |a SVM |2 nationallicence | |
| 690 | 7 | |a Spam |2 nationallicence | |
| 690 | 7 | |a $$t$$ t test |2 nationallicence | |
| 690 | 7 | |a Coefficient correlation |2 nationallicence | |
| 700 | 1 | |a Elssied |D Nadir |u Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia |4 aut | |
| 700 | 1 | |a Ibrahim |D Othman |u Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia |4 aut | |
| 700 | 1 | |a Osman |D Ahmed |u Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia |4 aut | |
| 773 | 0 | |t Soft Computing |d Springer Berlin Heidelberg |g 19/11(2015-11-01), 3237-3248 |x 1432-7643 |q 19:11<3237 |1 2015 |2 19 |o 500 | |
| 856 | 4 | 0 | |u https://doi.org/10.1007/s00500-014-1479-2 |q text/html |z Onlinezugriff via DOI |
| 898 | |a BK010053 |b XK010053 |c XK010000 | ||
| 900 | 7 | |a Metadata rights reserved |b Springer special CC-BY-NC licence |2 nationallicence | |
| 908 | |D 1 |a research-article |2 jats | ||
| 949 | |B NATIONALLICENCE |F NATIONALLICENCE |b NL-springer | ||
| 950 | |B NATIONALLICENCE |P 856 |E 40 |u https://doi.org/10.1007/s00500-014-1479-2 |q text/html |z Onlinezugriff via DOI | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Elssied |D Nadir |u Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Ibrahim |D Othman |u Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 700 |E 1- |a Osman |D Ahmed |u Universiti Teknologi Malaysia (UTM), Johor Bahru, Malaysia |4 aut | ||
| 950 | |B NATIONALLICENCE |P 773 |E 0- |t Soft Computing |d Springer Berlin Heidelberg |g 19/11(2015-11-01), 3237-3248 |x 1432-7643 |q 19:11<3237 |1 2015 |2 19 |o 500 | ||