Spam detection filter using KNN algorithm and resampling

Firte, Loredana; Lemnaru, Camelia; Potolea, Rodica

doi:10.1109/iccp.2010.5606466

Cited by 55 publications

(24 citation statements)

References 5 publications

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“…Physical amounts that are relied upon to be the whole of numerous free procedures, (for example, estimation mistakes) frequently have conveyances that are almost normal. [3] Moreover, numerous outcomes and techniques, (for example, spread of vulnerability and minimum squares parameter fitting) can be determined scientifically in express shape when the important factors are regularly disseminated.…”

Section: Gaussian Naive Bayesmentioning

confidence: 99%

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A Study of Bayesian Classifiers Detecting Gratuitous Email Spamming

Jain¹

2016

CAE

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Email turns into the real wellspring of correspondence nowadays. Most people on the earth utilize email for their own or expert utilize. Email is a successful, quicker and less expensive method for correspondence. The significance and use for the email is developing step by step. It gives an approach to effortlessly exchange data universally with the assistance of web. Because of it the email spamming is expanding step by step. As indicated by the examination, it is accounted for that a client gets more spam or insignificant sends than ham or pertinent sends. Spam is undesirable, garbage, spontaneous mass message which is accustomed to spreading infection, Trojans, noxious code, notice or to pick up benefit on irrelevant cost. Spam is a noteworthy issue that assaults the presence of electronic sends. Along these lines, it is vital to recognize ham messages from spam messages, numerous techniques have been proposed for arrangement of email as spam or ham messages. Spam channels are the projects which recognize undesirable, spontaneous, garbage messages, for example, spam messages, and counteract them to getting to the clients inbox. The channel grouping procedures are arranged into two either in view of machine learning method or in view of non-machine learning systems. Machine learning methods, for example, Naïve Bayes, Support Vector Machine, Ad boost, and choice tree and so forth though non-machine learning procedures, for example, dark/white rundown, marks, mail header checking and so on. in this paper we survey these procedures for arranging messages into spam or ham ,non-machine learning techniques, such as black/white list, signatures, mail header checking etc. in this paper we review these techniques for classifying emails into spam or ham.

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Section: Gaussian Naive Bayesmentioning

confidence: 99%

“…Spam message can contain text, image, video and also voice data. Spam can be sent via web, fax, telephonic (text messages) [3].…”

Section: Introductionmentioning

confidence: 99%

A Study of Bayesian Classifiers Detecting Gratuitous Email Spamming

Jain¹

2016

CAE

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“…Spam emails are the junk emails received from illegitimate users that might contain advertisement, malicious code, Virus or to gain personal profit from the user. Spam can be transmitted from any source like Web, Text messages, Fax etc., depending upon the mode of transmission spam can be categorised into various categories like email spam, web spam, text spam, social networking spam [3].…”

Section: Email Spammentioning

confidence: 99%

“…This paper makes use of the K-NN algorithm for classification of spam emails on the predefined dataset using feature's selected from the content and emails properties. Resampling of the datasets to appropriate set and positive distribution was carried out to make the algorithm efficient for feature selection [3].…”

Section: Email Spammentioning

confidence: 99%

E-Mail Spam Detection Using Refined MLP with Feature Selection

Kaur¹,

Verma²

2017

IJMECS

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Abstract-ElectronicMail (E-mail) has established a significant place in information user's life. E-Mails are used as a major and important mode of information sharing because emails are faster and effective way of communication. Email plays its important role of communication in both personal and professional aspects of one's life. The rapid increase in the number of account holders from last few decades and the increase in the volume of emails have generated various serious issues too. Emails are categorised into ham and spam emails. From past decades spam emails are spreading at a tremendous rate. These spam emails are illegitimate and unwanted emails that may contain junk, viruses, malicious codes, advertisements or threat messages to the authenticated account holders. This serious issue has generated a need for efficient and effective anti-spam filters that filter the email into spam or ham email. Spam filters prevent the spam emails from getting into user's inbox. Email spam filters can filter emails on content base or on header base. Various spam filters are labelled into two categories learning and non-machine learning techniques. This paper will discuss the process of filtering the emails into spam and ham using various techniques.

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“…A number of techniques benefit of clustering as a part of their spam detection approach like: clustering followed by KNN classification [10], [11] and clustering followed by SVM classification [12]. In [13], Jung and Sit check the use of DNS blacklists for address-based filtering of spams.…”

Section: Related Workmentioning

confidence: 99%

Spam Reduction by using E-mail History and Authentication (SREHA)

Yasin

2016

IJCNIS

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Abstract-Spam messages are today one of the most serious threats to users of E-mail messages. There are several ways to prevent and detect spam message, the most important way is filtering spam. Sometimes Filtering fails to discover some spam messages or even fails in the classification of non-spam messages as a spam messages. In this paper, we suggest a new effective method that reduces the spam messages by integrating prevention and detection techniques in one scheme. The reduction achieved by considering history and user authentication. This method based on issuing a certificate to each reliable user during the process of Email account Creation. The certificate used by Email servers to discard or forward ingoing or outgoing Emails. Each Server has to maintain white, gray and blacklist according to Email classification spam or ham, which determined by the user or by the contents examination of the message in terms of empty or contained only links without any text or by searching for a specific keywords in the subject and in the content. We believe that there are no bad or good E-mails forever, so the proposed model dynamically allows the transition of E-mail from one state to another state based on the number of received spam and ham messages.

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Spam detection filter using KNN algorithm and resampling

Cited by 55 publications

References 5 publications

A Study of Bayesian Classifiers Detecting Gratuitous Email Spamming

A Study of Bayesian Classifiers Detecting Gratuitous Email Spamming

E-Mail Spam Detection Using Refined MLP with Feature Selection

Spam Reduction by using E-mail History and Authentication (SREHA)

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