A Comparative Analysis of Various Spam Classifications

Shah, Nasir Fareed; Kumar, Pramod

doi:10.1007/978-981-10-3376-6_29

Cited by 14 publications

(17 citation statements)

References 6 publications

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“…An irrelevant, unsolicited and unwanted email, massively used for marketing that annoys the user is called a spam email [29,273], or called ham otherwise. Spam email consumes bandwidth, storage, and time of Internet users and significantly decreases the efficiency of system and network [274,275]. Nowadays, more than 85% of received emails or messages are spam [184].…”

Section: ) Backgroundmentioning

confidence: 99%

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A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

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Pervasive growth and usage of the Internet and mobile applications have expanded cyberspace. The cyberspace has become more vulnerable to automated and prolonged cyberattacks. Cyber security techniques provide enhancements in security measures to detect and react against cyberattacks. The previously used security systems are no longer sufficient because cybercriminals are smart enough to evade conventional security systems. Conventional security systems lack efficiency in detecting previously unseen and polymorphic security attacks. Machine learning (ML) techniques are playing a vital role in numerous applications of cyber security. However, despite the ongoing success, there are significant challenges in ensuring the trustworthiness of ML systems. There are incentivized malicious adversaries present in the cyberspace that are willing to game and exploit such ML vulnerabilities. This paper aims to provide a comprehensive overview of the challenges that ML techniques face in protecting cyberspace against attacks, by presenting a literature on ML techniques for cyber security including intrusion detection, spam detection, and malware detection on computer networks and mobile networks in the last decade. It also provides brief descriptions of each ML method, frequently used security datasets, essential ML tools, and evaluation metrics to evaluate a classification model. It finally discusses the challenges of using ML techniques in cyber security. This paper provides the latest extensive bibliography and the current trends of ML in cyber security.

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Section: ) Backgroundmentioning

confidence: 99%

“…SVM is a commonly used ML technique to detect spam on blogs [350][351][352][353][354] and video [355,356]. Decision Tree was further used in [274,[357][358][359][360][361][362] for spam classification. Authors in [363] applied Firefly and Bays classifiers for spam detection.…”

Section: And Spam On Twittermentioning

confidence: 99%

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

et al. 2020

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“…Other popular learning algorithms that have been applied to spam email detection include machine learning algorithms, feature selection methods [23] [24] [28] and also Naïve Bayes, SVM [25] [26] KNN classification [27].…”

Section: Related Studiesmentioning

confidence: 99%

“…The spam not only consumes the user's time by forcing them to identify the unwanted messages, but also wastes mailbox space, network bandwidth and time. Therefore, spam classification is becoming a bigger challenge to process for individuals and organizations [4] [23]. The word spam was used to describe unwanted, junk mails sent to an internet user's inbox.…”

Section: Introductionmentioning

confidence: 99%

Hybrid Feature Selection and Ensemble Learning Method for Spam Email Classification

Ablel-Rheem¹

2020

IJATCSE

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The data mining techniques produce good work in many domains. The spam emails are becoming a serious dilemma and an important matter to have different solutions, and enhanced methods and algorithms. Using Ensemble methods which are well-established classifiers. In this paper data mining techniques used to classify spam email using the UCI spam base dataset. The results achieved by the machine learning tools and techniques, and the Ensemble learning methods, after applying feature selection methods on the data set; which gave better result, and better classification accuracy. For the evaluation method used the cross-validation for testing and training option, and the confusion matrix to show the accuracy and the performance result of the chosen classifiers; which are Naïve Bayes, decision tree, ensemble boosting and ensemble hybrid boosting classifiers.

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“…Gashti (2017) examined spam email classification with decision tree (CART), SVM, NB and multi-layer perception (MLP), achieving 87.05 -100.00 per cent accuracy for the CART algorithm. Conversely, work done by Shah and Kumar (2018) incorporates decision tree (Id3) and other machine learning; this shows accuracy up to 83.92 per cent…”

Section: Decision Tree Approachesmentioning

confidence: 99%

Spam classification: a comparative analysis of different boosted decision tree approaches

Trivedi

Panigrahi

2018

JSIT

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Purpose Email spam classification is now becoming a challenging area in the domain of text classification. Precise and robust classifiers are not only judged by classification accuracy but also by sensitivity (correctly classified legitimate emails) and specificity (correctly classified unsolicited emails) towards the accurate classification, captured by both false positive and false negative rates. This paper aims to present a comparative study between various decision tree classifiers (such as AD tree, decision stump and REP tree) with/without different boosting algorithms (bagging, boosting with re-sample and AdaBoost). Design/methodology/approach Artificial intelligence and text mining approaches have been incorporated in this study. Each decision tree classifier in this study is tested on informative words/features selected from the two publically available data sets (SpamAssassin and LingSpam) using a greedy step-wise feature search method. Findings Outcomes of this study show that without boosting, the REP tree provides high performance accuracy with the AD tree ranking as the second-best performer. Decision stump is found to be the under-performing classifier of this study. However, with boosting, the combination of REP tree and AdaBoost compares favourably with other classification models. If the metrics false positive rate and performance accuracy are taken together, AD tree and REP tree with AdaBoost were both found to carry out an effective classification task. Greedy stepwise has proven its worth in this study by selecting a subset of valuable features to identify the correct class of emails. Research limitations/implications This research is focussed on the classification of those email spams that are written in the English language only. The proposed models work with content (words/features) of email data that is mostly found in the body of the mail. Image spam has not been included in this study. Other messages such as short message service or multi-media messaging service were not included in this study. Practical implications In this research, a boosted decision tree approach has been proposed and used to classify email spam and ham files; this is found to be a highly effective approach in comparison with other state-of-the-art modes used in other studies. This classifier may be tested for different applications and may provide new insights for developers and researchers. Originality/value A comparison of decision tree classifiers with/without ensemble has been presented for spam classification.

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A Comparative Analysis of Various Spam Classifications

Cited by 14 publications

References 6 publications

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

A Survey on Machine Learning Techniques for Cyber Security in the Last Decade

Hybrid Feature Selection and Ensemble Learning Method for Spam Email Classification

Spam classification: a comparative analysis of different boosted decision tree approaches

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