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Review on Email Spam Filtering Techniques International Journal of Scientific Engineering and Applied Science (IJSEAS) – Volume-2, Issue-3, March 2016 ISSN: 2395-3470 www.ijseas.com Review on Email Spam Filtering Techniques Shiva Sharma1, U. Dutta 2 Computer Science and Engineering Department, Maharana Pratap College of technology Putli Ghar Road, Near Collectorate, Gwalior-474006, Madhya Pradesh, India ABSTRACT- In this paper we present email online messages, blogs, forums, whatsapp and spam filtering and email authorship instant messaging services. Among all CMC, identification. Electronic mail is used by email has remained a key source of written millions of people to communicate around the communication, especially in the last few years. world daily and is a mission-critical Due to its salient features, it is the preferred application for many businesses. Over the last source of written communication for almost 10 years, unsolicited bulk email has become a every population (a part from illiterate) major problem for email users called senders connected to the Internet. It is a very quick, and receivers. And the recent years spam asynchronous written communication channel became as a big problem of internet and that is used for various purposes ranging from electronic communication known as users. formal to informal communication. Email There is developed lot of techniques to fight messages can be sent to a single receiver or them. We presents the overview of existing e- broadcasted to groups known as users. An email mail spam filtering methods is given. The message can reach to a number of receivers classification, valuation and juxtaposition of simultaneously and instantly at singe time. traditional and learning based methods are These days, the majority of individuals even provided. I think using this paper user can cannot imagine the life exclusive of email. For understand easily spam filtering techniques these and countless other motives, email has also and user can protect from spammed mails to become a widely used medium for financial damage, mentally damage to communication of the people having ill companies and annoying individual users. intentions [1]. But we know that everywhere have some pitfalls by destructive persons who Keywords: Spam, E-mail spam, Unsolicited want to loss to other persons using spam Bulk Messages, Filtering, Traditional emailing. Methods, String Matching. Spam is flooding the Internet with many copies I. INTRODUCTION of the same message in an attempt to force the The internet has become an Indiscernible part of message on people who would not otherwise everyday life and email has become a powerful choose to receive it. Spam is a combination of: tool for information interchange. Along with the unsolicited commercial e-mail (UCE), growth of the Internet and e-mail, there has been unsolicited non commercial e-mail (UNCE) and a choreographic growth in spam in recent years. unsolicited bulk e-mail (UBE). [2, 3, 4] Spam is the use of electronic messaging systems The three main characteristics of spam email (including most broadcast media, digital are; [2] delivery systems) to send unfaithful bulk messages recklessly. 1. not requested by the recipients; 2. has commercial value; Written communication that employs the use of 3. always sent in bulk. computers using email is referred to as computer mediated communication (CMC), and email is Spam is a problem because it wastes the email one of the most necessary and widely used storage, user’s time, email recipients time to forms of CMC. Other types of CMC include open, read and delete the email. It targets 25 International Journal of Scientific Engineering and Applied Science (IJSEAS) – Volume-2, Issue-3, March 2016 ISSN: 2395-3470 www.ijseas.com individual users with direct mail messages and 1. Methods to Avoid Spam Distribution bulk messages for multiple users. Creation of email spam lists is done by scanning Usenet Legislative measures limiting spam distribution, postings, stealing Internet mailing lists, or development of e-mail protocols using sender searching the Web for addresses. authentication, blocking mail servers which distribute spam are the methods which avoid II. SPAM-UNSOLICITED BULK EMAIL spam distribution in origin. E-mail spam, known as unsolicited Pile Email Using these methods alone doesn’t give (UPE), junk mail, advertisements or unsolicited considerable results. For example, there are commercial email (UCE), is the practice of many hard legislative re-striations for spam sending unwanted e-mail messages for distribution in USA; nevertheless, the greatest promotions as well as to get information with amount of spam is distributed from this region. unauthenticated way, frequently with One of the reasons is an existence of high level commercial content, in large quantities to an broad-band Internet access in USA. There is a unscrupulous set of recipients. The technical number of the approaches, offering to make definition of spam is an electronic message is spam sending economically unprofitable. One of called "spam" if these statements is to make sending of each e- (A) The recipient’s personal equality and out of mail paid. The payment for one e-mail should be context because the message is equally the extremely insignificant. In this case for the applicable to many other forceful Consignee; usual user it will be imperceptible. For and spammers who send thousand and millions (B) The Consignee has not verifiably granted messages the cost of such mailing becomes intentionally, explicit, and still-revocable considerable that makes it economically un- permission for it to be sent. profitable. This type of methods avoiding spam The risks in filtering spam are sometimes in their origins is a subject of author’s another legitimate mails may be rejected or disallowed papers [5, 6]. They should be implemented and legitimate mails may be marked as spam. together with the methods described in the next The risks of not filtering spam are the constant section, which filter spam at the destination flood of spam clogs networks and contrarily point. impacts user inboxes, but also drain valuable resources such as bandwidth and storage 2. Methods to Avoid Spam Receiving competence, productivity lower and intervene with the expedient delivery of legal emails. Methods which filter spam in destination point General consultation to avoid spam is, Avoid can be divided into the following categories: giving your “real” email address to all but close associates, Setup web mail accounts (Google, Depending on used theoretical approaches: hotmail, yahoo, rediffmail etc.) for registering traditional, learning-based and hybrid with web sites or for communicating with people methods; you do not know, edify your contacts to exercise Depending on filtration area: server side, caution with email address, Do not open junk client side and filtration in public mail- email, just delete, remove it, Never click to servers. unsubscribe to a mailing however you are sure it is a venerable entity. 2.1. Classification of Spam Filtering Methods Depending on Theoretical Approaches III. INTRODUCTION CLASSIFICATION As we noted above depending on used theoretical OF SPAM-FILTERING METHODS approaches spam filtering methods are divided into Depending on used techniques spam filtering traditional, learning-based and hybrid methods. In methods are generally divided into two traditional methods the classification model or the categories. data (rights, pat-terns, keywords, lists of IP addresses of servers), based on which messages are classified, 26 International Journal of Scientific Engineering and Applied Science (IJSEAS) – Volume-2, Issue-3, March 2016 ISSN: 2395-3470 www.ijseas.com is defined by expert. The data storage collected by mail receiving process. Disadvantage is that the experts is called as the knowledge base. There are policy of addition and deletion of addresses is also used trusted and mistrusted senders lists, which not always transparent. Often the whole subnets help to select legal mail. Actually it makes sense only belonging to providers get to the Black lists. For creation of the “white” list, because spammers use such systems it is actually impossible to estimate fictitious e-mail addresses. This technique can’t represent itself as a high-grade anti-spam filter, but the level of false positives (the legitimate e-mail can reduce considerably amount of false operations, wrongly classified as spam) on real mail being a part of e-mail filtration system based on other streams. classification methods. In learning-based methods the classification 4) Methods based on verification of sender’s e- model is developed using Data Mining mail address and domain name: This is the techniques. There are some problems from the simplest method of filtration if DNS request’s point of view of data mining as changing of name is the same with the domain name of spam content with time, the proportion of spam sender. But spammers can use real ad-dresses, so to legitimate mail, insufficient amount of that current method is ineffective. In this case it training data are characteristic for learning-based may be verified with possibility of sending the methods. message from current IP address. Firstly, the Sender ID technology can be used where Traditional methods Traditional methods are sender’s e-mail ad-dress is protected from divided into the following categories: falsification by means of publishing the policy 1) Methods based on analysis of messages: The of domain name use in DNS. Secondly, there received e-mail is analyzed for specific signs of can be used SPF (Sender Policy Framework) spam on the base of: formal signs; content using technology, where DNS protocol is used for signature in updated database; content applying verification of sender’s e-mail address. The statistic methods based on Bayes theorem; principle is that if do-main’s owner wants content by means of use SURBL (Spam URL support SPF verification, then he adds special Real-time Block Lists)[7], when run search for entry to DNS entry of his domain, where located references in e-mail and their indicates the release of SPF and ranges of IP verification under base of SURBL.
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