Spam Classification Based on Supervised Learning Using Machine Learning Techniques

Renuka, D. Karthika; Hamsapriya, T.; Chakkaravarthi, M. Raja; Surya, P. Lakshmi

doi:10.1109/pacc.2011.5979035

Cited by 39 publications

(18 citation statements)

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“…The accuracy of Naï ve Bayes was enhanced using FBL feature selection and used filtered Bayesian Learning with Naï ve Bayes. The modified Naï ve Bayes showed the accuracy of 91% [4].…”

Section: Related Workmentioning

confidence: 99%

K-MLP Based Classifier for Discernment of Gratuitous Mails using N-Gram Filtration

Kaur¹,

Verma²

2017

IJCNIS

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Abstract-Electronic spam is a highly concerning phenomenon over the internet affecting various organisations like Google, Yahoo etc. Email spam causes several serious problems like high utilisation of memory space, financial loss, degradation of computation speed and power, and several threats to authenticated account holders. Email spam allows the spammers to deceit as a legitimate account holder of the organisations to fraud money and other useful information from the victims. It is necessary to control the spreading of spam and to develop an effective and efficient mechanism for defence. In this research, we proposed an efficient method for characterising spam emails using both supervised and unsupervised approaches by boosting the algorithm's performance. This study refined a supervised approach, MLP using a fast and efficient unsupervised approach, KMeans for the detection of spam emails by selecting best features using N-Gram technique. The proposed system shows high accuracy with a low error rate in contrast to the existing technique. The system also shows a reduction in vague information when MLP was combined with KMeans algorithm for selecting initial clusters. N-Gram produces 100 best features from the group of data. Finally, the results are demonstrated and the output of the proposed technique is examined in contrast to the existing technique.

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“…The accuracy of Naï ve Bayes was enhanced using FBL feature selection and used filtered Bayesian Learning with Naï ve Bayes. The modified Naï ve Bayes showed the accuracy of 91% [4].…”

Section: Related Workmentioning

confidence: 99%

K-MLP Based Classifier for Discernment of Gratuitous Mails using N-Gram Filtration

Kaur¹,

Verma²

2017

IJCNIS

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“…Classification algorithms whose performances have been so far compared include Naï ve Bayes [1], [12]- [17], other algorithms compared include C-PLS, ANN, C-RT, CS-CRT, CS-MC4, CS-SVC, Continouns PLS-DA, PLS-LDA, LDA [1], Bayesnet [4], [12], [13], Multilayer perceptron [1], [15], SVM [1], [4], [12]- [14], [16], [17]. Table 1 shows the summary of algorithms used in previous comparative research.…”

Section: Related Workmentioning

confidence: 99%

“…Other performance metrics used are TP Rate, FP Rate, Precision, Recall, F-Measure and ROC [4], [13]. A few researchers also considered the time taken to load models in determining the performance of the algorithms [15], [22]. Table 2 shows performance metrics employed by previous research works.…”

Section: Related Workmentioning

confidence: 99%

Comparative Analysis of Classification Algorithms for Email Spam Detection

Abdulhamid¹,

Shuaib²,

Osho³

et al. 2018

IJCNIS

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Abstract-The increase in the use of email in every day transactions for a lot of businesses or general communication due to its cost effectiveness and efficiency has made emails vulnerable to attacks including spamming. Spam emails also called junk emails are unsolicited messages that are almost identical and sent to multiple recipients randomly. In this study, a performance analysis is done on some classification algorithms including: Bayesian Logistic Regression, Hidden Naï ve Bayes, Radial Basis Function (RBF) Network, Voted Perceptron, Lazy Bayesian Rule, Logit Boost, Rotation Forest, NNge, Logistic Model Tree, REP Tree, Naï ve Bayes, Multilayer Perceptron, Random Tree and J48. The performance of the algorithms were measured in terms of Accuracy, Precision, Recall, FMeasure, Root Mean Squared Error, Receiver Operator Characteristics Area and Root Relative Squared Error using WEKA data mining tool. To have a balanced view on the classification algorithms' performance, no feature selection or performance boosting method was employed. The research showed that a number of classification algorithms exist that if properly explored through feature selection means will yield more accurate results for email classification. Rotation Forest is found to be the classifier that gives the best accuracy of 94.2%. Though none of the algorithms did not achieve 100% accuracy in sorting spam emails, Rotation Forest has shown a near degree to achieving most accurate result.

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“…In [15], ant colony optimization (ACO) algorithm is proposed to detect spam in host level. From the machine learning viewpoint, spam filtering based on the textual content of e-mail can be viewed as a special case of text categorization, with the categories being spam or non-spam [5,6,16].…”

Section: Related Workmentioning

confidence: 99%

“…Usually, the most common techniques to deal with the spam problem is the spam filtering techniques. Traditional anti-spam techniques include the Bayesian-based filters [1][2][3][4], Rule-based Scoring Systems [5][6][7], DNS MX Record Lookup and Reverse lookup systems [8], DNS Realtime Blackhole List (DNSRBLs) or IP Blacklists [9].…”

Section: Introductionmentioning

confidence: 99%

A Solution for Fighting Spammer's Resources and Minimizing the Impact of Spam

Mohamed¹

2012

IJCNS

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Spam or unsolicited emails constitute a major threat to the Internet, the corporations, and the end-users. Statistics show that about 70% -80% of the emails are spam. There are several techniques that have been implemented to react to the spam on its arrival. These techniques consist in filtering the emails and placing them in the Junk or Spam folders of the users. Regardless of the accuracy of these techniques, they are all passive. In other words, they are like someone is hitting you and you are trying by all the means to protect yourself from these hits without fighting your opponent. As we know the proverbs "The best defense is a good offense" or "Attack is the best form of defense". Thus, we believe that attacking the spammers is the best way to minimize their impact. Spammers send millions of emails to the users for several reasons and usually they include some links or images that direct the user to some web pages or simply to track the users. The proposed idea of attacking the spammers is by building some software to collect these links from the Spam and Junk folders of the users. Then, the software periodically and actively visit these links and the subsequent redirect links as if a user clicks on these links or as if the user open the email containing the tracking link. If this software is used by millions of users (included in the major email providers), then this will act as a storm of Distributed Denial of Service attack on the spammers servers and there bandwidth will be completely consumed by this act. In this case, no human can visit their sites because they will be unavailable. In this paper, we describe this approach and show its effectiveness. In addition, we present an application we have developed that can be used for this reason.

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Spam Classification Based on Supervised Learning Using Machine Learning Techniques

Cited by 39 publications

References 1 publication

K-MLP Based Classifier for Discernment of Gratuitous Mails using N-Gram Filtration

K-MLP Based Classifier for Discernment of Gratuitous Mails using N-Gram Filtration

Comparative Analysis of Classification Algorithms for Email Spam Detection

A Solution for Fighting Spammer's Resources and Minimizing the Impact of Spam

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