Source: http://www.google.com/patents/US7756930?dq=6,408,309
Timestamp: 2014-07-10 05:45:25
Document Index: 95895112

Matched Legal Cases: ['application No. 60', 'application No. 60', 'application No. 60', 'application No. 10', 'application No. 05', 'application No. 05', 'application No. 05', 'application No. 05']

Patent US7756930 - Techniques for determining the reputation of a message sender - Google PatentsSearch Images Maps Play YouTube News Gmail Drive More »Sign in<nobr>Advanced Patent Search</nobr>PatentsTechniques are provided for determining a reputation of a message sender by obtaining two or more lists from two or more list providers; determining which lists of the two or more lists indicate the message sender; and determining a reputation score for the message sender based on which lists of the...http://www.google.com/patents/US7756930?utm_source=gb-gplus-sharePatent US7756930 - Techniques for determining the reputation of a message senderAdvanced Patent SearchPublication numberUS7756930 B2Publication typeGrantApplication numberUS 10/857,641Publication dateJul 13, 2010Filing dateMay 28, 2004Priority dateMay 28, 2004Fee statusPaidAlso published asUS20060031314, WO2005119488A2, WO2005119488A3Publication number10857641, 857641, US 7756930 B2, US 7756930B2, US-B2-7756930, US7756930 B2, US7756930B2InventorsRobert Brahms, Daniel Quinlan, Craig SprostsOriginal AssigneeIronport Systems, Inc.Export CitationBiBTeX, EndNote, RefManPatent Citations (127), Non-Patent Citations (95), Referenced by (17), Classifications (10), Legal Events (2) External Links: USPTO, USPTO Assignment, EspacenetTechniques for determining the reputation of a message senderUS 7756930 B2Abstract Techniques are provided for determining a reputation of a message sender by obtaining two or more lists from two or more list providers; determining which lists of the two or more lists indicate the message sender; and determining a reputation score for the message sender based on which lists of the two or more lists indicate the message sender. Techniques are also provided for indicating that a message is unsolicited based on a reputation score.
RELATED APPLICATIONS; PRIORITY CLAIM This is related to U.S. Non-Provisional patent application Ser No. 10/717,441, filed Nov. 18, 2003, naming Banister et al. as inventors, which claims domestic priority under 35 U.S.C. 119 from prior U.S. Provisional Patent application No. 60/428,134, filed Nov. 20, 2002, naming Banister et al. as inventors, and 60/482,883, filed Jun. 25, 2003 naming Banister et al. as inventors, the entire contents of which are hereby incorporated by reference for all purposes as if fully act forth herein.
This application is related to U.S. Provisional patent application No. 60/545,609, filed Feb. 17, 2004, entitled �COLLECTING, AGGREGATING, AND MANAGING INFORMATION RELATING TO ELECTRONIC MESSAGES�, naming Flury et al. as inventors, which is hereby incorporated by reference for all purposes as if fully set forth herein.
This application is related to U.S. Provisional patent application No. 60/574,530, filed May 25, 2004, entitled �COLLECTING, AGGREGATING, AND MANAGING INFORMATION RELATING TO ELECTRONIC MESSAGES�, naming Flury et al. as inventors, which is hereby incorporated by reference for all purposes as if fully set forth herein.
This application is related to U.S. patent application No. 10/856,693, filed May 28, 2004, entitled �ELECTRONIC MESSAGE DELIVERY WITH ESTIMATION APPROACHE�, naming Perry et al. as inventors, which is hereby incorporated by reference for all purposes as if fully set forth herein.
FIELD OF THE INVENTION The present invention generally relates to electronic message delivery in a networked system. The invention relates more specifically to techniques for determining the reputation of a message sender.
In the e-mail context, in one past approach, senders marketing commercial products or services would acquire or develop lists of e-mail addresses and then periodically send mass unsolicited e-mail messages (�spam�) to all addresses in the lists. Using modern electronic systems, the cost of sending millions of such messages has been negligible, and a response rate of even less than one percent has been considered worthwhile. Thus, successful delivery of unsolicited messages to valid in-boxes of recipients normally translates into income for the sender.
Unfortunately, this approach causes receivers to receive unwanted messages. The direct and indirect costs of receiving �spam� are high. In response, receivers have adopted a variety of approaches to prevent receipt or viewing of unwanted messages.
In one approach, receivers use filtering, marking, or blocking technologies that attempt to classify messages as �spam� or not spam by examining various aspects of the message. For example, some filters look for keywords in the message subject line and reject or quarantine messages that contain keywords matching a list of prohibited words. In another approach, receivers use �blacklists� to identify and prohibit or less easily admit messages from suspect senders of unsolicited messages. Some receivers augment these technologies with personal �white lists� of friends or other acceptable senders; messages from senders in the white list are admitted or more easily admitted. The white lists and blacklists also may come from networked sources. Techniques for performing blacklist lookups are described at the �ip4r� HTML document that is available online at the time of this writing at the �support� subdirectory of the �junkmail� directory of the �declude� commercial domain of the World Wide Web, and at the �bill� section of the �scconsult� commercial domain of the World Wide Web. Example blacklists include the series of blacklists provided by the �njabl� organization domain of the World Wide Web. Example white lists could include lists of Fortune 500 companies and other reputable senders.
One problem with these approaches is that some messages that receivers want may not reach the intended receivers because they are identified as �spam� by the filtering or blocking technologies. Receivers who use filtering or blocking technologies regularly fail to receive some legitimate messages because the filtering and blocking technologies cannot always properly distinguish legitimate messages from unsolicited messages. For example, certain industry-standard terms or technical abbreviations may be identical to prohibited keywords, confusing the �spam� filter.
Further, receivers continue to receive large volumes of unwanted messages that are not properly trapped by the �spam� filter. As a result, many receivers now refuse to disclose their address except under limited circumstances. In response, many legitimate senders, such as reputable commercial enterprises, have developed �opt-in� procedures in which the addresses of receivers, such as customers, are not used at all unless the receiver affirmatively agrees to receive messages. Even when this is done, the filtering or blocking technologies may delete or quarantine even those messages from legitimate senders that are directed to receivers who have �opted in.� Consequently, the value of e-mail as a marketing tool for responsible communications directed to receivers who have �opted in� is decreasing. Many receivers remain essentially defenseless to the daily onslaught of �spam� arriving in their e-mail in-boxes. Whereas many states have enacted legislation that imposes civil or criminal penalties for sending �spam,� these remedies are time-consuming for receivers to pursue. In addition, while many Internet Service Providers (�ISPs�) actively identify and refuse to communicate or do business with those who send �spam,� however, policing such improper activity imposes a significant cost on the ISP. In addition, ISPs are burdened with the aggregated network and disk usage costs associated with the sending and receiving the unwanted messages. End users may also be burdened with bandwidth costs associated with downloading these messages.
ISPs also incur costs associated with processing messages directed to recipients who do not hold an account with the ISP. For these recipients, the ISPs mail system typically generates an automatic �bounce� message that states that the recipient is unknown. Indeed, a �double bounce� may occur when a message bears an invalid sender address, and is sent to an invalid recipient. Costs are associated with maintaining the equipment, network bandwidth, and software that generates the bounce messages and for dispatching the bounce messages back into the network to the sender. Thus, there is a need for a system or method that can reduce the number of �bounce� and �double bounce� events experienced by ISPs and derived from unwanted messages.
Even when ISPs and enterprises use anti-�spam� technologies, large numbers of �spam� messages may not be identified as spam, and many non-spam messages may be misclassified as spam. This costs e-mail marketers, and causes senders to lose confidence in the benefits of e-mail marketing. Moreover, end users are required to invest time in monitoring, checking, delivering, and negotiating blacklists, white lists, and similar mechanisms. The information from these lists can be conflicting, and therefore making a decision for a particular email sender based on the information in these lists can be difficult.
DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENT Techniques for determining the reputation of a message sender are described. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present invention. It will be apparent, however, to one skilled in the art that the present invention may be practiced without these specific details. In other instances, well-known structures and devices are shown in block diagram form in order to avoid unnecessarily obscuring the present invention.
2.1 Example System Organization 2.2 Sample Data Structure 3.0 Functional Overview
3.1 Maintaining Aggregate Lists 3.2 Adding Entries to an Aggregate Data Structure 3.3 Example Reputation Score Calculations 3.4 Example Process for Estimating Whether a Message is Unsolicited 4.0 Implementation Mechanisms�Hardware Overview 5.0 Extensions and Alternatives
The keys 212A, 212B, 212C, 212D in the bit length hash table 210 represent the length of defined significant digits of an IP address range associated with a reputation. Typically, IP addresses are 32 bits long, so the range of possible entries for a 32 bit IP address would be from /0 (no significant bits are defined) to /32 (all the bits are defined). For example, �/8� refers to a range where only the first eight bits are defined and is associated with key 212D. An example /8 entry could be �152.*.*.*� (where �*� represents a wildcard and signifies that the corresponding bits are not defined). IP addresses �152.2.128.152� and �152.123.234.4� would fall into the /8 range of �152.*.*.*�. The IP address �153.2.128.152� would not fall into the /8 range of �152.*.*.*�. In one embodiment, a key 212A, 212B, 212C, 212D is only added to the bit length hash table 210 if a range of IP addresses corresponding to that length is received in one of the reputation-related lists.
There is one IP address range hash table 220 for each key 212A-D in the bit length hash table 210. Each IP address range hash table 220 has a key 222A-N for each IP address range of the particular range length that is received from a list provider. For example, if two �/8� IP address ranges �152.*.*.*� and �159.*.*.*� were received from one or more list providers as part of one or more reputation lists, then two keys would be added to the IP address range hash table for /8: one corresponding to each of �152.*.*.*� and �159.*.*.*�.
There is a list bit mask 230 corresponding to each entry 222A-222N in the IP address range hash table 220. The list bit mask 230 records which black or white lists include the IP address or range value of the entry 222A-222N that reference the list bit mask 230. In one embodiment, each list provider 105 a-150 c a corresponding bit 232A-232N in the list bit mask 230. In another embodiment, two or more list providers 105 a-150 c correspond to a single bit 232A-232N. In yet another embodiment, one list provider 150 a corresponds to one or more bits 232A-232N. For simplicity in explanation, in the examples herein each list provider 150 a-150 c corresponds to a single bit 232A-232N. In one embodiment, if a list indicates or includes a particular IP address range of an entry 222A-222N, then a bit corresponding to that list is set to �1�.
As an example, in the context of FIG. 1, consider a list provider 150 a corresponding to bit 232C and a list provider 150 b corresponding to bit 232B. If both list provider 150 a and list provider 150 b each provide a list that includes a /8 entry of �152.*.*.*� then bits 232C, 232B are set to �1�. The rest of the bits in the list bit mask default to zero. If subsequently list provider 150 c (corresponding to bit 232A) provides a list that does not include �152.*.*.*�, then bit 232A will not be set to one, but will remain zero. Therefore the first three bits of the list bit mask 230 would read �011� as shown in FIG. 2.
In step 320, the difference between the current version of the particular list and any previous version of the particular list is determined. In one embodiment, if there is no previous version of the particular list then the difference between the particular list obtained in step 310 and �the previous list� is defined as the full list obtained in step 310. In various embodiments, if there is a previous version of the particular list, the difference between the version of the particular list obtained in step 310 and the previous version of the particular list is determined by using any appropriate tool, such as the Unix �diff� command, for example.
When an entry is deleted from a particular list, its corresponding entries must be deleted from the aggregate list data structure as part of step 340. Deleting an entry from an aggregate list data structure can be accomplished by finding the IP address range hash table associated with the appropriate length entry in the bit length hash table; finding the list bit mask associated with the appropriate entry in the IP address range hash table; and setting the bit in the list bit mask corresponding to the particular list to �0�. For example, in the context of FIG. 2, the entry �152.*.*.*� is deleted from the aggregate list data structure 200 by finding the �/8� entry 212D in the bit length hash table 210, finding the appropriate entry 222A in the IP address range hash table 220, and setting the bit 232A-232N corresponding to the particular list to �0� in the corresponding list bit mask 230.
In step 410, two or more lists are obtained from two or more list providers. In various embodiments, these lists are obtained using DNS zone transfers; database exports and later imports; obtaining files containing the lists via file transfer protocol (FTP), hypertext transfer protocol (HTTP), secure HTTP (HTTPS), or the rsync protocol; or any other appropriate means. For example, in the context of FIG. 1, two or more lists are obtained from two list providers 150 a and 150 b. In step 420, the lists that contain the sender are determined. In various embodiments, step 420 comprises parsing each list from each sender or querying an aggregate list, and aggregate list data structure, or other appropriate mechanism. For example, in the context of FIG. 2, determining if a particular list contains the IP address comprises accessing each IP address range hash table 220 for each length or key 212A-212D in the bit length hash table 210 and determining whether the IP address falls into any IP address range of an entry 222A-222N in the IP address range hash table 220 and checking to determine which bits 232A-232N are set in the list bit mask 230 for each matching entry in the IP address range hash table 220.
In one embodiment, in order to determine which lists contain the IP address, the steps above are performed for each individual list separately or all lists are checked at once. In a related embodiment, there are two or more list bit masks 230 corresponding to matching entries 222A-222N in two or more IP address range hash table 220 corresponding to two or more entries in the bit length hash table 210. Further, determining which lists contain the IP address comprises performing the �or� operation on the two or more bit masks to result in creating a result bit mask. The result bit mask will have �1�s in any place that any individual list bit mask 230 has a �1� and will have a �0� only at those bits where no list bit mask 230 has a �1�. In other embodiments, other logical or mathematical functions could be used to combine the list bit masks 230, such as addition, weighted addition, bitwise averaging, bitwise exclusive or, or any other appropriate function. In one embodiment, an aggregate list bit mask is used to store which lists indicate the IP address of the sender.
In step 520 a check is made to determine whether a corresponding entry exists in the bit length hash table. In various embodiments, this comprises determining the length of the item obtained in step 510. For example, in the context of FIG. 2, the item obtained in step 510 could be �152.*.*.*� which corresponds to a length of 8 bits �/8�. Determining whether an entry for �/8� exists in the bit length hash table 210 would then comprise determining whether there already exists a �/8� key 212A-212D in the hash table.
After an appropriate entry is added in step 530 or if an entry already exists for that range (step 520), then a check is performed to determine whether the IP address range for the new entry already exists in the IP address range hash table. For example, in the context of FIG. 2, if the item obtained in step 510 is �152.*.*.*�, a check is made to determine whether an entry 222A-222N exists for �152.*.*.*� in the /8 IP address range hash table 220 corresponding to the �/8� key 212D in the bit length hash table 210.
If there is no corresponding entry 222A-222N in the IP address range hash table 220, then in step 550 an entry is added to the appropriate data structure corresponding to the item obtained in step 510. In one embodiment, adding an entry comprises setting all the bits in the corresponding list bit mask 230 to zeros. For example, in the context of FIG. 2, if there is no entry for �152.*.*.*� in the IP address range hash table 220, then an entry is made for �152.*.*.*� in step 550 and all bits 232A-232N in the list bit mask 230 corresponding to the �152.*.*.*� are set to zero.
If an entry has been added or there is already a corresponding entry in the IP address range hash table, then in step 560, the list bit mask corresponding to the IP address range hash table entry for the added item is altered to indicate the particular list. For example, in the context of FIG. 2, if the entry for �152.*.*.*� is added to the IP address range hash table 220 or the entry already existed in the IP address range hash table 220, then in step 560 the bit in the list bit mask 230 corresponding to the list is set. For example, in the context of FIG. 2, the entry for �152.*.*.*� already exists and the bit in the list bit mask 230 corresponding to the list is set.
In one embodiment, this score is obtained by determining, for each �white� list, whether the sender's IP address is in the particular list. A white list is a list of IP addresses and ranges that are believed to be associated with senders of legitimate electronic messages. If the IP address is indicated in the particular list, then the score for the list represents a certain percentage likelihood that the message is unsolicited (often less than 50%). If the IP address is not indicated in the particular list, then the score for the list represents a certain percentage likelihood that the message is unsolicited (often higher than 50%).
In step 620, an aggregate score is generated based on the scores for each list determined in step 610. In one embodiment, the score for each list is a percentage likelihood that a message is unsolicited and the aggregate score is an aggregate percentage likelihood that is generated based on the individual percentages likelihoods. In various embodiments, this aggregate percentage likelihood is based on a weighted average of the individual percentages likelihoods, a sum or product of the individual percentages likelihoods, a polynomial of the individual percentages likelihoods, or any appropriate calculation. In various embodiments, the aggregate percentage is based in part on the Chi Squared function over the probabilities, a Robinson calculation, a Bayes calculation, or any other appropriate mechanism. A particular embodiment of the Chi Squared function is depicted in the Python Programming Language (see the �python� commercial domain of the World Wide Web) code of Appendix A.
Mapped ⁢ ⁢ Percentage ⁢ ⁢ ( MP ) = ⁢ { if ⁢ ⁢ ( P < AP ) ; ( P - LP ) * 50 / ( AP - LP ) ⁢ { else ; ( P - AP ) * 50 / ( HP - AP ) + 50. For example, if LP is 30%, AP is 40% and HP is 80%, then percentages from 30% to 40% would map to 0% to 50%; and percentages from 40% to 80% would map to 50% to 100%. In such an example, 35% would map to 25% and 60% would map to 75%.
Mapped ⁢ ⁢ Score ⁢ ⁢ ( MS ) = ⁢ { if ⁢ ⁢ MP < .5 ; ( - log ⁡ ( MP ) / log ⁡ ( 2 ) - 1 ) / lo_k ⁢ { else ; 1.0 / hi_k * ( 1 - 1 / log ⁡ ( 2 ) * log ⁡ ( 1 / ( 1 - MP ) ) , where lo_k and hi_k are constants. It may be beneficial to use hi_k and lo_k values approximately in the range of 0.5 and 2.0. It may be beneficial to use hi_k and lo_k values approximately in the range of 0.6 and 1.0. Hi_k and lo_k may each have the same value or may have different values.
In step 720, the reputation score is compared to a first predefined threshold to determine whether it is worse than the predefined threshold. If the reputation score is worse than the predefined threshold, then the message is indicated as unsolicited in step 730. In various embodiments, if the message is indicated as unsolicited, the message is deleted, put in a trash folder, put in a �bulk mail folder�, flagged to indicate that it is estimated as unsolicited, or any other appropriate action. After step 730 is performed, the process completes.
The term �computer-readable medium� as used herein refers to any medium that participates in providing instructions to processor 804 for execution. Such a medium may take many forms, including but not limited to, and non-volatile media. Non-volatile media includes, for example, optical or magnetic disks, such as storage device 810. Volatile media includes dynamic memory, such as main memory 806.
APPENDIX A A.1 Function for Summing Terms for Chi Squared:
def chi2q(x2, v): if v and v % 2: raise �Error: v must be even in chi2q.� m = x2/2.0 term = math.exp(0 − m) sum = term for i in range (1, v/2): term *= m / i sum += term return sum < 1.0 and sum or 1.0 A.2 Function for Calculating Chi Squared Value for a List of Probabilities
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