spam control

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Spam Control Mechanism using Identity based Message Admission

Spam Control Mechanism using Identity based Message Admission

Spam research has received an important attention from the research community in recent years. Anti-spam work groups have been formed, such as the Anti-Spam Research Group (ASRG) of the internet Research Task Force (IRTF) and Anti- Phishing Working Group (APWG) [6]. Variety of spam handling techniques is designed by various research communities. They can be categorized as filter-based approaches, signature-based approaches, protocol-level approaches, Identity based approaches and policy based approach and so on. Here each class of approaches has its own advantages. Using two or more approaches at the same time may work more effectively in better spam control. SMTP servers do not provide any sort of facility to authenticate entities in communication of email system. Hence Signature-based approach can attempt to deal with the authentication problem by successfully authenticating the sender by the receiver. The basic science behind signature- based approaches is that for a message, the sender signs the message using a key and transmits it. At the other side, the recipients can effectively authenticate the message by signature verification.
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Applying Spam-Control Techniques To Negate High Frequency Trading Advantages

Applying Spam-Control Techniques To Negate High Frequency Trading Advantages

In fact since thousands of flash orders are sent out per second and flash orders are sent out as execute immediately or cancel on receipt, this means a substantial amount of them self-destruct, and perhaps only result in a successful trade at the same rate as the one-tenth of one percent success rate in spam. But suppose, using the time- based spam control method, these orders also had to be kept valid for a minimum period of time, perhaps ten seconds, the HFT trick of placing and cancelling orders in microseconds would be negated. This would eliminate the flash order advantage.
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Instant Spam Control

Instant Spam Control

You can actively contribute to minimizing the amount of spam e-mails. It’s an everlasting process and has no immediate effect on your quarantine inbox but you’ll be part of making the Barracuda products better for all its users over the world. When enough users like you have classified an e-mail address it’ll be added to the spam filter’s list. Do the following to train the global filter: Use the link already described to go to your quarantine inbox. Here you need to classify the e- mail as being spam or not. In the example below all e-mails are classified as being spam.
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Using Security Tools for Spam Control with MDaemon 6.0

Using Security Tools for Spam Control with MDaemon 6.0

Spam foes say no one should receive bulk email unless they specifically seek it out. They propose opting in, where users sign up to receive periodic email from a vendor or organization. Spam supporters say this is overly restrictive and limits the ways they can contact new people. They suggest opting out as an

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Using MDaemon AntiSpam for Spam Control

Using MDaemon AntiSpam for Spam Control

Some ISPs, organizations and businesses unknowingly provide spammers with free email services by allowing their SMTP servers to relay messages from any sender to any receiver. These are called open relay servers and were very common before the advent of spam. Having an open relay means neither the sender nor the receiver of a message must have an account on the server doing the relaying. To counteract this, several Internet organizations specialize in testing email servers and black listing those with open relays. Enterprises hoping to fight spam can use the lists to block all incoming email from open-relay
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Letterman Spam Control v3. User Manual. User Manual 1/45

Letterman Spam Control v3. User Manual. User Manual 1/45

A spam filtration software catering for the needs of the worldwide Chinese, which can correctly distinguish emails in English, Japanese, Korean, Traditional Chinese and Simplified Chinese, and even can intercept hostile link from advertisement service provider.

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Collaborative Anti Spam Technique to Detect Spam Mails in E-Mails

Collaborative Anti Spam Technique to Detect Spam Mails in E-Mails

at layer 7 makes detection at intermediate nodes (between the sending and the receiving MTAs) infeasible due to the need for complex Transport Control Protocol/Internet Protocol (TCP/IP) processing at link speed. TCP, which requires reassembly, byte alignment, and state tracking requires large computation overhead. As a result, spam control is restricted to MTA implementation as an end-to-end spam control mechanism.An improved spam detection approach, at lower e-mail abstraction levels, is needed to lift the end-to-end implementation restriction and to allow fast spam detection, closer to spam sources. Third, due to the lack of outbound spam control, e-mails are effectively classless upon reception at the receiving MTA. In the current spam control, e-mail classes are unknown until e-mails have been classified and detected for spam. Thus, all incoming e- mails are queued in a common queue and delivered (to recipients) with equal priority. For e-mail traffic that consists mainly of spam, non-spam e-mails are delayed due to the presence of spam in the queue. Furthermore, non-spam e-mails maybe lost during queuing. Spam wastes MTA processing and bandwidth resources. Any attack on the common queue could disrupt server operations. A scheme at receiving MTAs to maintain e-mail delivery is needed to reduce the non-spam delay and loss due to queuing. [1]so the identified mail must be correctly identified .A more effective spam detection using vendors reply helps to detects spam more accurately and privacy is achieved by rabin fingerprinting hash algorithm, as only the hash values with ranges are sent to different vendors privacy is achieved in a this algorithm and spams are detected with more effectively.
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Controlling Spam at the Routers

Controlling Spam at the Routers

become a regular menu item on the Internet information diet. To combat the daily onslaught of spam clogging people’s email inboxes, much work is being done on the development of effective spam control methods, most of which follow the same basic theme of establishing a “front line” of defense at the end-user level. However, dealing with spam is like fighting a battle against a large army; the most effective approach is to employ multiple tactics. Thus, in this paper we propose a method for blocking the supply lines. More specifically, since the daily replen- ishment of all those in-boxes with new spam consumes a significant amount of network resources, we describe a mechanism to allow network administrators to impose rate controls on bulk email delivery. In our approach, we separate SMTP email delivery traffic from other types of traffic at the router. We then apply a two- step process to the email delivery traffic, which first identifies bulk email streams by comparison with a cache of recently-seen emails, and then uses a Bayesian classifier to decide whether or not a particular bulk emails stream is spam. If a bulk email stream is classified as a spam, we then rate limit it (e.g., no more than 1 copy per minute).
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DiffMail: A Differentiated Message Delivery Architecture to Control Spam

DiffMail: A Differentiated Message Delivery Architecture to Control Spam

Unsolicited bulk electronic mail (spam) is increasingly plaguing the Internet Email users and deteriorating the value of Email as a convenient communication tool. Although many different spam control schemes have been proposed and deployed on the Internet, the proportion of Email spam seen on the Internet has been increasing in recent years. In this paper we argue that the difficulties in controlling spam can be attributed to the lack of user control on how different Email messages should be delievered on the Internet. In the current Email delivery architecture, a party can at will force another party to be involved in an Email communication, regardless of whether the latter is willing to accept the message. Based on this observation, we propose a differentiated message delivery architecture—DiffMail. In DiffMail, a user can classify Email senders into multiple classes and handle messages from each class differently. For example, the user may directly accept messages from regular contacts, while asking other senders to hold their messages at their own mail servers. Such messages can be retrieved from the sender’s mail server if and when the receiver wishes to do so. In this way, DiffMail achieves several appealing objectives. Amongst others, regular correspondence is handled in the same way as in the current Email architecture, spammers are discouraged from blindly sending spam to arbitrary users, and it helps to improve the effectiveness of real-time blacklists of spammers. In this paper we present a detailed design of the DiffMail architecture and conduct empirical studies to illustrate its performance trade-offs using real-world Email archives. Furthermore, we also illustrate how DiffMail can be incrementally deployed on the Internet.
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Survey Paper on Effective Email Classification into Spam and Non-Spam Mails

Survey Paper on Effective Email Classification into Spam and Non-Spam Mails

ABSTRACT: Emails are used by number of users for educational purpose or professional purpose. But the spam mails causes serious problem for email users likes wasting of user‟s energy and wasting of searching time of users. This paper present as survey paper based on some popular classification technique to identify whether an email is spam and non-spam. For representing spam mails ,we use vector space model(VSM). Since there are so many different word in emails, and all classifier can not be handle such a high dimension ,only few powerful classification terms should be used. Other reason is that some of the terms may not have any standard meaning which may create confusion for classifier.
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Detecting Spam Comments Using Spam Word Dictionary and NLP Technique

Detecting Spam Comments Using Spam Word Dictionary and NLP Technique

The system takes the input as comments from social media sites. These comments are pre-processed in pre- processing module or phase. The output of pre-processing is simple plain text. This plane text is give as an input to feature extraction module. In this phase actual spam is detected by comparing text with self-extensible spam word dictionary. If spam word detected then it is replace with star (***).Finally we get the output as spam has been detected or not.

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Anti-Spam Settings: Using Only SmarterMail Anti-Spam Tools

Anti-Spam Settings: Using Only SmarterMail Anti-Spam Tools

PREFACE: Since the production of the original SmarterMail Anti-Spam Tools document in 2009, much has changed in the fight against spam. SPF has finally started to become more widely used, and even mandated by some ISPs. rDNS, originally a “strongly suggested” lookup tool has, albeit not officially per the IETF , become mandatory lookup match for e-mail to be accepted by most large ISPs and many smaller providers have also adopted those same requirements. With the addition of DOMAIN KEYS , DKIM , the DMARC protocol, those three tests, along with SPF, can be used to tie them all together and provide almost proof positive evidence that an e-mail either did, or didn’t actually originate from a stated MX server.
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Mystery meat revisited: spam, anti spam measures and digital redlining

Mystery meat revisited: spam, anti spam measures and digital redlining

One area that receives quite some attention is the question of accuracy or false- positives. If employing technical criteria typically used in the spam filtering community, some professional anti-spam technologies seem to be performing extremely well in terms of effectiveness and a low number of false-positives (i.e., genuine email falsely classified as spam). MessageLabs, for example, stated in 2003 that their spam filtering technology achieves 96.4% effectiveness and 0.04% false-positives. Advertising slogans such as "Stop the emails you don't want - before they come anywhere near your corporate network" imply that spam filtering is a straightforward business. However, Balvanz et al.'s (2004) experiences with a number of spam filters built into desktop email clients typically indicate much higher false-positive rates than those published by some of the professional mail filtering services. The spam filter (brand not known) used by the first author's former employer certainly does not meet the above mentioned performance either: out of 81 spam messages received by a relatively new email account between February and April 2005 merely 21 were correctly identified as spam, resulting in mere 17% effectiveness (assuming that effectiveness resembles what is called precision in information retrieval). The number of emails dropped without notifying the recipient --if any-- is unknown. However, on a number of occasions, important genuine messages, such as program committee invitations and conference announcements, had been tagged as spam. Very low false-positive figures also appear to contradict Fallows’ (2004) findings summarized above. Unintended consequences of spam filtering 2: Digital Redlining
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Spam Detection Approaches with Case Study Implementation on Spam Corpora

Spam Detection Approaches with Case Study Implementation on Spam Corpora

These are the traditional type of spam filters that analyze the message subject, headers and content searching for specific words or phrases, or other indicators of spam. Whenever an unsolicited mail comes into your mail box, the user can create a new filter by choosing certain words, or phrases from the message that indicate it is spam. But spammers know that their messages were being marked by these content filters and have resorted to counter the content filter through words with special char- acters inserted like “Vi@gra”, “p.0.r.n”, “L|0|a|n|$” etc. This effort is getting increasingly popular that previous versions of content-based filters are not delivering well in terms of performance. But as one can perform wildcard searches and has the ability to see the spammer’s attempts at obfuscating the words such as in the examples shown above, the mails can be classified as spam. A vast majority of spam emails are less legible because of their effort to bypass the content-based filters. The content based approach nevertheless is quite flexible. We can easily specify the filtering to the exact type of spam message that is in question and avoid regular words that we use daily communication. But on the downside, it requires more effort and hands on tuning, along with regular updation. As spammers look to novel approaches to circumvent the filters, the filters need to be modified to deal with them.
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Preemption of State Spam Laws by the Federal Can-Spam Act

Preemption of State Spam Laws by the Federal Can-Spam Act

The scope of its preemption clause will determine, in part, the CAN-SPAM Act’s overall effectiveness. As explained in Part I, the Act is broad in scope and prohibits many of the worst aspects of spam. It contains at least some restrictions in nearly every potential area of regulation the states have identified, short of banning spam outright or limiting it to recipients who have opted to receive messages. In other areas, however, the Act’s restrictions are weaker than those en- acted by the states, and the Act ultimately ignores several ways to fight spam. The set of tools available to fight spam will depend, then, on the extent to which the Act permits these state laws to remain in force: a narrow interpretation of the Act’s savings clause will hinder states’ efforts to fight spam, while a broader interpretation could give states stronger tools.
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Opting Out of Spam: A Domain Level Do-Not-Spam Registry

Opting Out of Spam: A Domain Level Do-Not-Spam Registry

This Note does not propose an opt-in system like that in Europe and Australia because this system loses the value of unsolicited speech that many actually do want[r]

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The Bayesian Spam Filter with NCD. The Bayesian Spam Filter with NCD

The Bayesian Spam Filter with NCD. The Bayesian Spam Filter with NCD

Bayesian spam filter is a statistic technique for filtering e-mails. It uses naive Bayesian classifiers for spam identification. The Bayesian classifiers work with relations between elements (typically words) from unrequested (spam) and re- quested e-mails. They calculate probability whether an e-mail is spam or not by the help of the Bayesian statistics. Particular words have a particular proba- bilities of occurrence in unrequested e-mails and legitimate e-mails. Filter does not recognize these probabilities in advance. It first has to learn them so that he could build upon them. Each new e-mail must be manually marked whether it is spam or is not. For all words in each e-mail the filter must adjust proba- bility with which the given word occurs in spam or in legitimate e-mails in its database. For instance words ”viagra” or ”refinance” are often found in spam e-mails and names of friends or family members are often found in legitimate e-mails.
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Stopping Outgoing Spam

Stopping Outgoing Spam

One of the earliest attempts at stopping inbound spam was to impose a computational cost on spammers [6, 9]. In the computational approach, spammers are in some way re- quired to prove that they have performed a computation. For instance, they could be sent a challenge if their mail does not include computation, or all mail without attached computation could be rejected. The computation is cho- sen to be easy to verify, and time consuming to compute. Typically, for stopping inbound spam, the computation re- quired is a function of various header fields, such as From, To, Subject and Date. One possible computation is to com- bine together these fields, and then require the sender to find a number, which, when prepended to this combined string has a hash whose first k bits are 0. (This is roughly how the Camram [9] system works for inbound spam, as inspired by Hashcash [3].) k can be chosen so that a certain amount of time is required, on average, to find such a hash value. If, for instance, we choose k such that the time required on average is 1 minute, and assuming it costs roughly $1000 to purchase a computer, maintain it, power it, etc. for one year, then the cost of solving such a puzzle is approximately 100000/(365 × 24 × 60) = 0.2 cents. Another option is to use memory bound puzzles [1, 5], which are more robust to variations in CPU speed. For both CPU and memory-bound puzzles, most legitimate users have many unused cycles on their computers, and there is effectively zero incremental cost to performing such a computation (which can be done in the background at low priority, where it will not be no- ticed), while a spammer trying to send millions of messages must actually purchase the computers (or otherwise acquire the cycles, such as by stealing them – a problem we will discuss later.) Later we will describe how this approach can be leveraged for stopping outbound spam as well.
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Opinion Spam Mining

Opinion Spam Mining

As we know that e commerce is booming around the world on large scale so is the technology. This results in production of various products. All these products are sold in the market with a hype created through advertisements. So nowadays people read reviews from customers who have already bought the product so as to understand what the product really is. But for this purpose the review needs to be genuine otherwise it is done by the manufacturer himself or by people who work for money. There are a few existing systems that deal with problem of identifying fake reviews and also to provide a trustworthy rating for a product using different algorithms. So to overcome the problem we are creating a system in which he filter out fake reviews and spammers so that we obtain a clean set of review data which will help the customers to know the product more closely. Before doing anything we need to segregate the data in number of parent products and further segregate into sub products. This classification will help us to understand the product type. Then we go on to spam. Once we clear the spam we go on to divided the clean data set to development and test set. The two
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Categorizing Blog Spam

Categorizing Blog Spam

• Classification: Using the entities outlined above, we determine the most dis- tinct features of each entity through the use of Natural Language Processing (NLP) and Machine Learning (ML). We derive a number of feature sets that express a particular attribute of the content posts, such as the Part Of Speech (PoS), which represents the syntactical structure of a post, or the Bag Of Words (BoW), which gives an idea of the semantic content of a post. We find that among the attributes we consider, semantic modeling is the most effective way of distinguishing one entity from another, both by using the actual words present in the content of the spam, or more efficiently, a derived taxonomy of the con- tent.
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