364 results about "Spamming" patented technology

A system, apparatus, and method are directed towards enabling information filtering using measures of an affinity of a relationship between subscribers of an online portal system. The affinity of a relationship may be determined based, in part, on the tracking of various online behaviors of and between subscribers of the portal system. Any of a variety of behaviors may be tracked, including message communications between subscribers, participation in instant messaging groups, purchases, activities, categories, and so forth. Such behaviors may be employed to determine a level of trust (or affinity) between subscribers of the portal system. This affinity measurement may be used to filter various information, including, but not limited to, product recommendations, ratings, polling queries, advertising, social network communications, personal ads, search results, and the like. Moreover, this affinity measurement may also be employed to perform message spam detection.

A system, various methods, and various apparatuses are provided for the purpose of supplying and including in an electronic message or multimedia session signalling unit a valid cryptographic authentication token, verifying said token's validity upon arrival of said message or signalling unit, and thereby providing message recipients or session parties with the assurance that said message or signalling unit is from a valid sender. A system, apparatus, and various methods are further provided for the purpose of protecting legitimate application traffic and the network elements exchanging it from intrusion by wild packets attempting to consume application resources and thereby deny service to legitimate users or network elements. A system, various methods, and various apparatuses are further provided for the purpose of enabling legitimate advertising via electronic messages, relying upon message and sender authentication to assure both advertisers and viewers of advertising messages that all participants are valid, legitimate, and accountable for any abuse that may occur.

The inventive methodology provides an integrated plug and play solution designed to protect home networks against spam, phishing emails, viruses, spyware as well as other similar threats. The described content filtering appliance can be used for processing of web and email traffic implemented and can be deployed as a stand-alone appliance. In one implementation, the content processing appliance utilizes backend content filtering provided by a remote scanning service accessed via a network. The system employs network level analysis and translation of content and executes various procedures to handle the network traffic. In an embodiment of the invention, the appliance is provided with an automatic remote updating capability, wherein the software and data used by the appliance can be updated remotely via a network. Finally, the appliance may also implement parental controls.

Dynamically filtering and classifying messages, as good messages, bulk periodicals, or spam. A regular expression recognizer, and pre-trained neural networks. The neural networks distinguish “likely good” from “likely spam,” and also operate at a more discriminating level to distinguish among the three categories above. A dynamic whitelist and blacklist; sending addresses are collected when the number of their messages indicates the sender is good or a spammer. A dynamically selected set of regular expressions input to the neural networks.

An e-mail filtering method and system that categorizes received e-mail messages based on information about the origin, such as the sender sending the message or the site from where the message was sent, which is contained in the message header and the SMTP session used to send the message. This data, whether the sender is familiar to the receiver, and other information about the message is then sent by each member of an e-mail network to one or more central databases (in one embodiment, the information will also be stored at a database associated with the recipient's e-mail program and filtering software) which stores the information and compiles statistics about e-mails sent from the origin to indicate the likelihood that the e-mail is unsolicited and determine the reputation of the origin (a good reputation indicates the sender sends solicited messages while a bad reputation indicates the sender sends unsolicited e-mail messages). Information from the central database is then sent to recipients in order to determine the likelihood that a received e-mail message is spam (information may also be obtained from the local database associated with the recipient's e-mail program and filtering software).

Disclosed are signature-based systems and methods that facilitate spam detection and prevention at least in part by calculating hash values for an incoming message and then determining a probability that the hash values indicate spam. In particular, the signatures generated for each incoming message can be compared to a database of both spam and good signatures. A count of the number of matches can be divided by a denominator value. The denominator value can be an overall volume of messages sent to the system per signature for example. The denominator value can be discounted to account for different treatments and timing of incoming messages. Furthermore, secure hashes can be generated by combining portions of multiple hashing components. A secure hash can be made from a combination of multiple hashing components or multiple combinations thereof. The signature based system can also be integrated with machine learning systems to optimize spam prevention.

A set of general criteria have been defined to improve the efficacy of a tagging system, and have been applied to present collaborative tag suggestions to a user. The collaborative tag suggestions are based on a goodness measure for tags derived from collective user authorities to combat spam. The goodness measure is iteratively adjusted by a reward-penalty algorithm during tag selection. The collaborative tag suggestions can also incorporate other sources of tags, e.g., content-based auto-generated tags.

A method, apparatus, and system are directed towards communicating a message within a social network based, in part, on user-defined criteria. The user-defined criteria may be applied towards filtering outgoing and/or incoming messages. The user-defined criteria may include degrees of separation between members of the social network, a relationship to the prospective recipient, as well as criteria based, in part, on activities, such as dating, employment, hobbies, and the like. The user-defined criteria may also be based on a group membership, a strength of a relationship, and the like. Such user-defined relationship criteria may then be employed to determine to whom a message may be sent, as well from whom a message may be received.

A protocol for protected email transmission using micropayments and a segregated inbox in which protected emails are displayed. The protocol also involves authentication of the sender to defeat phishers and an opt out protocol which can be used to block protected emails from sources from which the user no longer wishes to receive emails even if the source has made a micropayment. Branded email is also taught wherein a sender of protected emails can pay extra to have a miniature version of its brand logo or trademark displayed with its email in the segregated inbox. A white list is maintained on the protected email server (along with the opt out black list) so that recipients can designate specific senders who may send email to that recipient without paying a micropayment and still have the protected email displayed in the segregated inbox.

The subject invention provides for a system and method that facilitates detecting and preventing spam in a variety of networked communication environments. In particular, the invention provides several techniques for monitoring outgoing communications to identify potential spammers. Identification of potential spammers can be accomplished at least in part by a detection component that monitors per sender at least one of volume of outgoing messages, volume of recipients, and/or rate of outgoing messages. In addition, outgoing messages can be scored based at least in part on their content. The scores can be added per message per sender and if the total scores) per message or per sender exceeds some threshold, then further action can be taken to verify whether the potential spammer is a spammer. Such actions include human-inspecting a sample of the messages, sending challenges to the account, sending a legal notice to warn potential spammers and/or shutting down the account.

An e-mail filtering method and system that categorize received e-mail messages based on information about the sender. Data about the sender is contained in the message and is used to identify the actual sender of the message using a signature combining pieces of information from the message header or derived from information in the message header. This and other information about the message is then sent by each member of an e-mail network to one or more central databases (in one embodiment, the information will also be stored at a database associated with the recipient's e-mail program and filtering software) which stores the information and compiles statistics about e-mails sent by the sender to indicate the likelihood that the e-mail is unsolicited and determine the reputation of the sender (a good reputation indicates the sender does not send unwanted messages while a bad reputation indicates the sender sends unsolicited e-mail messages). Information from the central database is then sent to recipients in order to determine the likelihood that a received e-mail message is spam (information may also be obtained from the local database associated with the recipient's e-mail program and filtering software).

Multiple inventions are included. Also users of an email system are limited in ways including to be able to send only a certain number of emails per day. This can ensure for example that if they spam the system they can be suspended before they can send an economically worthwhile amount of spam.

Electronic content is filtered to identify spam using image and linguistic processing. A plurality of information type gatherers assimilate and output different message attributes relating to message content associated with an information type. A categorizer may have a plurality of decision makers for providing as output a message class for classifying the message data. A history processor records the message attributes and the class decision as part of the prior history information and/or modifies the prior history information to reflect changes to fixed data and/or probability data. A categorizer coalescer assesses the message class output by the set of decision makers together with optional user input for producing a class decision identifying whether the message data is spam.

An arrangement for distributing information from Information Providers (IP) to Information Recipients (IR) that utilizes the email system and the Internet to communicate with popular hardware devices—computers, PDAs, cellular phones, etc. IRs receive information anonymously and only the information desired. IPs do not need to “spam” the world in the hope of getting their information into the right hands because it is organized in a manner that allows IRs to receive exactly what they are looking for. Information is provided by an IP to an IR in response to a request from an IR. A PR server organizes and stores information uploaded by IPs, retrieves information requested by an IR and sends an automated response to the IR. A web domain is associated with the PR server provides a common email addressing scheme for communicating between the PR server and the IRs.

An e-mail filtering method and system that categorize received e-mail messages based on information about the sender. Data about the sender is contained in the message and is used to identify the actual sender of the message using a signature combining pieces of information from the message header or derived from information in the message header. This and other information about the message is then sent by each member of an e-mail network to one or more central databases (in one embodiment, the information will also be stored at a database associated with the recipient's e-mail program and filtering software) which stores the information and compiles statistics about e-mails sent by the sender to indicate the likelihood that the e-mail is unsolicited and determine the reputation of the sender (a good reputation indicates the sender does not send unwanted messages while a bad reputation indicates the sender sends unsolicited e-mail messages). Information from the central database is then sent to recipients in order to determine the likelihood that a received e-mail message is spam (information may also be obtained from the local database associated with the recipient's e-mail program and filtering software).

Techniques for annotating email messages. In one aspect of the invention, a method is provided for annotating a query email message. According to the method, patterns associated with a database, comprising annotated email messages, which may typically be known unwelcome email messages (“SPAM), are accessed, as by use of a pattern discovery algorithm (e.g. the Teiresias pattern algorithm). Attributes are assigned to the patterns based on the annotated SPAM email messages. The patterns with assigned attributes are used to analyze the query email message.

The present invention involves a system and method that facilitate extracting data from messages for spam filtering. The extracted data can be in the form of features, which can be employed in connection with machine learning systems to build improved filters. Data associated with origination information as well as other information embedded in the body of the message that allows a recipient of the message to contact and/or respond to the sender of the message call be extracted as features. The features, or a subset thereof, can be normalized and/or deobfuscated prior to being employed as features of the machine learning systems. The (deobfuscated) features can be employed to populate a plurality of feature lists that facilitate spam detection and prevention. Exemplary features include an email address, an IP address, a URL, an embedded image pointing to a URL, and/or portions thereof.

In one embodiment, a method for operating a server is provided. The method comprises receiving a plurality of reports from a community of users, each report identifying an email message as spam or not spam; and determining if the email message is spam based on a number of the reports received from the community of users, and a trust factor associated with each user.

Provided herein is an electronic message management platform that enables management and execution of electronic message campaigns while appropriately managing challenges presented by spam filters, black lists, and domain blocking technologies, and that includes elements for managing an electronic message campaign based on dynamic conditions, quality measures, engagement factors, and other measures, factors and conditions.

Detecting unsolicited messages (spam) by aggregating information across multiple recipients and/or across the same or differing messaging protocols. Multiple messages are analyzed to detect a call to action pattern that specifies a target communication address with which the recipients are requested to communicate, such as an email address, an Internet address, a telephone number, and the like. Once a frequency threshold of messages containing the call to action pattern is reached, subsequent messages are temporarily quarantined for evaluation by a human operator. If the human determines that the messages are not spam, the human can release the quarantined messages, and indicate that future messages with the call to action pattern are not to be delayed. Conversely, if the human determines that the messages are spam, the human can delete the messages in quarantine, and indicate that all future messages with that call to action pattern are to be deleted automatically.

Various embodiments of the invention address two critical problems that current email service providers face. First, there is the problem of maintaining high levels of customer service when email server systems are inundated with spam. Second, there is the problem of reducing the system-wide impact that spam has on the email delivery system. Current embodiments are directed to determining whether an email message is an unwanted bulk email message without necessarily considering the message that is conveyed by any portion of the email message. Through analyses of patterns of delivery of these email messages, profiles are built that allow an email server to ascertain whether there is a likelihood that any one particular email message constitutes an unwanted email message. If an email message is determined to likely constitute an unwanted email message, then memory-saving measures are implemented. In preferred embodiments, one copy of the email message is saved at a central, shared location that can be accessed by each of the intended recipients. This avoids having to replicate the email message across the system for each of the recipients.

In one embodiment, a DNS security network includes several DNS appliances and a security operations center (SOC) server computer. The SOC server computer may receive telemetry data from the DNS appliances, the telemetry data comprising information about DNS client queries received in the respective DNS appliances. From the telemetry data, the SOC server computer may generate security policies for distribution to the DNS appliances. The security policies may be used by the DNS appliances to determine whether a DNS client query is originated by a client computer performing a prohibited activity (e.g., sending spam, communicating with a zombie control computer, navigating to a prohibited website, etc.). An answer to a client query may be replaced or discarded altogether in cases where the originator is performing a prohibited activity.

A phishing filter employs a plurality of heuristics or rules (in one embodiment, 12 rules) to detect and filter phishing attempts solicited by electronic mail. Generally, the rules fall within the following categories: (1) identification and analysis of the login URL (i.e., the “actual” URL) in the email, (2) analysis of the email headers, (3) analysis across URLs and images in the email other than the login URL, and (4) determining if the URL is accessible. The phishing filter does not need to be trained, does not rely on black or white lists and does not perform keyword analysis. The filter may be implemented as an alternative or supplemental to prior art spam detection filters.

A Voice over IP (VoIP) or Real Time Messaging (RTM) firewall device is claimed that protects VoIP or RTM network traffic by identifying and controlling the delivery of such network traffic that is unsolicited and undesired by the recipient (i.e. VoIP or RTM spam). The system involves applying a unique marking to RTM messages close to a point of message origination and then at a point close to message termination for the intended recipient examining a reputation store for information on the unique marking and using that information in conjunction with a set of policy rules to decide whether to pass, reject, pass on to an RTM store or otherwise filter the RTM message. The unique marking serves to identify a source characteristic of the message such as the message originator, a corporate affiliation for the originator, or a RTM network characteristic of the originator such as a transmission gateway.