U.S. patent application number 11/142943 was filed with the patent office on 2006-01-19 for systems and methods for classification of messaging entities.
This patent application is currently assigned to CipherTrust, Inc.. Invention is credited to Dmitri Alperovitch, Paul Judge, Matt Moyer.
Application Number | 20060015942 11/142943 |
Document ID | / |
Family ID | 35600962 |
Filed Date | 2006-01-19 |
United States Patent
Application |
20060015942 |
Kind Code |
A1 |
Judge; Paul ; et
al. |
January 19, 2006 |
Systems and methods for classification of messaging entities
Abstract
Methods and systems for operation upon one or more data
processors for assigning a reputation to a messaging entity. A
method can include receiving data that identifies one or more
characteristics related to a messaging entity's communication. A
reputation score is determined based upon the received
identification data. The determined reputation score is indicative
of reputation of the messaging entity. The determined reputation
score is used in deciding what action is to be taken with respect
to a communication associated with the messaging entity.
Inventors: |
Judge; Paul; (Alpharetta,
GA) ; Alperovitch; Dmitri; (Atlanta, GA) ;
Moyer; Matt; (Lawrenceville, GA) |
Correspondence
Address: |
NAGENDRA SETTY;JONES DAY
1420 PEACHTREE ST, NE
SUITE 800
ATLANTA
GA
30309-3053
US
|
Assignee: |
CipherTrust, Inc.
|
Family ID: |
35600962 |
Appl. No.: |
11/142943 |
Filed: |
June 2, 2005 |
Related U.S. Patent Documents
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Application
Number |
Filing Date |
Patent Number |
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10384924 |
Mar 6, 2003 |
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11142943 |
Jun 2, 2005 |
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10373325 |
Feb 24, 2003 |
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11142943 |
Jun 2, 2005 |
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10361091 |
Feb 7, 2003 |
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11142943 |
Jun 2, 2005 |
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10361067 |
Feb 7, 2003 |
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11142943 |
Jun 2, 2005 |
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10094266 |
Mar 8, 2002 |
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11142943 |
Jun 2, 2005 |
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10094211 |
Mar 8, 2002 |
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11142943 |
Jun 2, 2005 |
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10093553 |
Mar 8, 2002 |
6941467 |
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11142943 |
Jun 2, 2005 |
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60625507 |
Nov 5, 2004 |
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Current U.S.
Class: |
726/24 ; 713/188;
714/E11.207 |
Current CPC
Class: |
G06F 11/008 20130101;
H04L 51/12 20130101 |
Class at
Publication: |
726/024 ;
713/188 |
International
Class: |
G06F 11/00 20060101
G06F011/00; G06F 11/30 20060101 G06F011/30; G06F 11/22 20060101
G06F011/22; G06F 12/14 20060101 G06F012/14; H04L 9/32 20060101
H04L009/32; G06F 11/32 20060101 G06F011/32; G06F 11/34 20060101
G06F011/34; G06F 11/36 20060101 G06F011/36; G06F 12/16 20060101
G06F012/16; G06F 15/18 20060101 G06F015/18; G08B 23/00 20060101
G08B023/00 |
Claims
1. A method for operation upon one or more data processors to
assign a reputation to a messaging entity, comprising: receiving
data that identifies one or more characteristics related to a
messaging entity's communication; determining a reputation score
based upon the received identification data; wherein the determined
reputation score is indicative of reputation of the messaging
entity; wherein the determined reputation score is used in deciding
what action is to be taken with respect to a communication
associated with the messaging entity.
2. The method of claim 1, wherein the determined reputation score
is distributed to one or more computer systems for use in filtering
transmissions.
3. The method of claim 1, wherein the determined reputation score
is locally distributed to a program for use in filtering
transmissions.
4. The method of claim 1, wherein reputation scores include
numeric, textual or categorical reputations that are assigned to
messaging entities based on characteristics of the messaging
entities and their behavior; wherein the numeric reputations
fluctuate between a continuous spectrum of reputable and
non-reputable classifications.
5. The method of claim 1 further comprising: determining reputation
indicative probabilities based upon the received identification
data; wherein a reputation indicative probability indicates
reputability of a messaging entity based upon extent to which the
identified one or more communication's characteristics exhibit or
conform to one or more reputation-related criteria; wherein
determining the reputation score includes determining the
reputation score based upon aggregation of the determined
probabilities.
6. The method of claim 5, wherein a type of messaging entity to
which reputations are assigned is a domain name, IP address, phone
number, or individual electronic address or username representing
an organization, computer, or individual user that transmits
electronic messages.
7. The method of claim 1 further comprising: identifying a set of
criteria for use in discriminating between reputable and
non-reputable classifications; wherein the criteria include
non-reputable criteria and reputable criteria; using statistical
sampling to estimate a conditional probability that a messaging
entity displays each criteria; computing a reputation for each
messaging entity, wherein the computing step comprises: calculating
probability that a messaging entity deserves a reputable reputation
by computing an estimate of joint conditional probability that the
messaging entity is reputable, given the set of criteria that the
messaging entity exhibits or conforms to and the individual
conditional probability that the messaging entity exhibits or
conforms to each such criteria is actually a reputable messaging
entity; calculating the probability that the messaging entity
deserves a negative reputation by computing an estimate of joint
conditional probability that the messaging entity is non-reputable,
given the set of criteria that the messaging entity exhibits or
conforms to and the individual conditional probability that the
messaging entity exhibits or conforms to each such criteria is
actually a non-reputable messaging entity; computing a reputation
for a messaging entity by applying a function to the
probabilities.
8. The method of claim 7, wherein the reputation of each messaging
entity is encoded within the form of a 32-bit, dotted decimal IP
address; said method further comprising: creating a domain name
server (DNS) zone comprising the reputations of all messaging
entities in a universe of messaging entities; and distributing
reputations of messaging entities, via the DNS protocol, to one or
more computer systems that make use of the reputations for their
work.
9. The method of claim 7, wherein the set of criteria are metrics
selected from the group: a mean Spam Profiler score; a reverse
domain name server lookup failure; membership on one or more
real-time blacklists (RBLs); mail volume; mail burstiness; mail
breadth; a geographic location; malware activity; a type of
address; a classless inter-domain routing (CIDR) block comprising a
number of internet protocol addresses identified to send spam; rate
of user complaints; rate of honeypot detections; rate of
undeliverable transmissions, identified conformance with laws,
regulations, and well-established standards of transmission
behavior; continuity of operation; responsiveness to recipient
demands; and combinations thereof.
10. The method of claim 7, wherein a technique used to compute the
joint conditional probabilities is based on probabilistic
independence between all criteria.
11. The method of claim 7, wherein a technique used to compute the
joint conditional probabilities is based on a joint probability
estimation technique.
12. The method of claim 7, wherein a technique used to compute
joint conditional probabilities is based on probabilistic
non-independence between all criteria.
13. The method of claim 7, wherein the function used to encode the
messaging entity reputation within a 32-bit dotted decimal IP
address is: IP = 172 ( rep - rep 2 .times. rep ) ( rep .times. div
.times. .times. 256 ) ( rep .times. .times. mod .times. .times.
.times. 256 ) . ##EQU7##
14. The method of claim 7, wherein classifications of reputable and
non-reputable are related to a tendency for an IP address to send
unwanted transmissions or legitimate communication.
15. The method of claim 1 further comprising: determining
reputation indicative probabilities based upon the received
identification data; wherein a reputation indicative probability
indicates reputability of a messaging entity based upon extent to
which the identified one or more communication's characteristics
exhibit or conform to one or more reputation-related criteria;
wherein determining the reputation score includes determining the
reputation score based upon aggregation of the determined
probabilities. wherein the reputation score is determined based
upon applying the aggregation of the determined probabilities to a
function; wherein the function is a function of each of the
probabilities that the messaging entity exhibits a
reputation-related criterion.
16. A method of performing transmission filtering utilizing
reputation scores of transmission sender, the method comprising:
identifying at least one characteristic about a transmission from a
sender; performing a real-time query to the reputation system that
includes the transmission characteristic; receiving a score
representing reputation related to the transmission; performing an
action on the transmission from the sender corresponding to the
score range of the sender's reputation.
17. The method of claim 16, wherein the action includes at least
one of the following actions: rejecting all further transmissions
from that sender for a preset period of time or number of
transmissions; silently dropping all further transmissions from
that sender for a preset period of time or number of transmissions;
quarantining all further transmissions from that sender for a
preset period of time or number of transmissions; bypassing certain
filtering tests for all further transmissions from that sender for
a preset period of time or number of transmissions.
18. The method of claim 16, wherein the step of identifying at
least one characteristic includes extracting unique identifying
information about the transmission, or authenticating unique
identifying information about the transmission, or combinations
thereof.
19. The method of claim 18, wherein the unique identifying
information includes information about the sender of the
transmission.
20. A method of performing filtering of groups of transmissions
utilizing reputation scores of senders of transmissions, the method
comprising: grouping multiple transmissions together based on
content similarities or similarities in transmission sender
behavior; identifying at least one characteristic about each
transmission in the groupings; performing a query to the reputation
system and receiving a score representing reputation of each
sender; classifying groups of transmissions based on the percentage
of reputable and non-reputable senders in the group.
21. The method of claim 20, wherein the step of identifying at
least one characteristic includes extracting unique identifying
information about the transmission, or authenticating unique
identifying information about the transmission, or combinations
thereof.
22. The method of claim 21, wherein the unique identifying
information includes information about the sender of a
transmission.
23. A method of performing tuning and training of filtering systems
utilizing reputation scores of senders of transmissions in sets of
trainable transmissions, the method comprising: identifying at
least one characteristic about transmissions from senders;
performing queries to a reputation system and receiving scores
representing reputations of the senders; classifying transmissions
into multiple categories based on a range a sender's reputation
score falls into; passing on transmissions and their classification
categories to a trainer of another filtering system to be used for
optimization of the filtering system.
24. The method of claim 23, wherein the step of identifying at
least one characteristic includes extracting unique identifying
information about the transmissions, or authenticating unique
identifying information about the transmissions, or combinations
thereof.
25. The method of claim 24, wherein the unique identifying
information includes information about the senders of the
transmissions.
26. An article of manufacture comprising a digital signal for
transmission using a network; wherein the digital signal includes a
query to a reputation process; wherein the reputation process
assigns a reputation to a messaging entity by receiving the query
containing data related to a messaging entity's identity; wherein
the identity data is used by the reputation process to determine
reputation indicative probabilities; wherein a reputation
indicative probability indicates reputability of a messaging entity
based upon extent to which the messaging entity exhibits or
conforms to a reputation-related criterion; wherein a reputation
score is determined based upon aggregation of the determined
probabilities; wherein the determined reputation score is
indicative of reputation of the messaging entity; wherein the
determined reputation score is used in deciding what action is to
be taken with respect to a communication associated with the
messaging entity.
27. The digital signal of claim 26, wherein a filtering system
generates the digital signal and the reputation process receives
the digital signal; wherein the digital signal includes packetized
data that is transmitted through the network.
Description
CROSS-REFERENCE TO RELATED APPLICATIONS
[0001] This application claims priority to and the benefit of U.S.
Provisional Application Ser. No. 60/625,507 (entitled
"Classification of Messaging Entities") filed on Nov. 5, 2004, of
which the entire disclosure (including any and all figures) is
incorporated herein by reference.
[0002] This application is a continuation-in-part of, and claims
priority to and the benefit of, commonly assigned U.S. patent
application Ser. No. 10/093,553, entitled "SYSTEMS AND METHODS FOR
ADAPTIVE MESSAGE INTERROGATION THROUGH MULTIPLE QUEUES," U.S.
patent application Ser. No. 10/094,211, entitled "SYSTEMS AND
METHODS FOR ENHANCING ELECTRONIC COMMUNICATION SECURITY," and U.S.
patent application Ser. No. 10/094,266, entitled "SYSTEMS AND
METHODS FOR ANOMALY DETECTION IN PATTERNS OF MONITORED
COMMUNICATIONS," all filed on Mar. 8, 2002, each of which are
hereby incorporated by reference in their entirety. This
application is also a continuation-in-part of, and claims priority
to and the benefit of, commonly assigned U.S. patent application
Ser. No. 10/361,091, filed Feb. 7, 2003, entitled "SYSTEMS AND
METHODS FOR MESSAGE THREAT MANAGEMENT," U.S. patent application
Ser. No. 10/373,325, filed Feb. 24, 2003, entitled "SYSTEMS AND
METHODS FOR UPSTREAM THREAT PUSHBACK," U.S. patent application Ser.
No. 10/361,067, filed Feb. 7, 2003, entitled "SYSTEMS AND METHODS
FOR AUTOMATED WHITELISTING IN MONITORED COMMUNICATIONS," and U.S.
patent application Ser. No. 10/384,924, filed Mar. 6, 2003,
entitled "SYSTEMS AND METHODS FOR SECURE COMMUNICATION DELIVERY."
The entire disclosure of all of these applications is incorporated
herein by reference.
BACKGROUND AND SUMMARY
[0003] This document relates generally to systems and methods for
processing communications and more particularly to systems and
methods for filtering communications.
[0004] In the anti-spam industry, spammers use various creative
means for evading detection by spam filters. Accordingly, spam
filter designers adopt a strategy of combining various detection
techniques in their filters.
[0005] Current tools for message sender analysis include IP
blacklists (sometimes called real-time blacklists (RBLs)) and IP
whitelists (real-time whitelists (RWLs)). Whitelists and blacklists
certainly add value to the spam classification process; however,
whitelists and blacklists are inherently limited to providing a
binary-type (YES/NO) response to each query. In contrast, a
reputation system has the ability to express an opinion of a sender
in terms of a scalar number in some defined range. Thus, where
blacklists and whitelists are limited to "black and white"
responses, a reputation system can express "shades of gray" in its
response.
[0006] In accordance with the teachings disclosed herein, methods
and systems are provided for operation upon one or more data
processors for assigning a reputation to a messaging entity. A
method can include receiving data that identifies one or more
characteristics related to a messaging entity's communication. A
reputation score is determined based upon the received
identification data. The determined reputation score is indicative
of reputation of the messaging entity. The determined reputation
score is used in deciding what action is to be taken with respect
to a communication associated with the messaging entity.
BRIEF DESCRIPTION OF THE DRAWINGS
[0007] FIG. 1 is a block diagram depicting a system for handling
transmissions received over a network.
[0008] FIG. 2 is a block diagram depicting a reputation system that
has been configured for determining reputation scores.
[0009] FIG. 3 is a table depicting reputation scores at various
calculated probability values.
[0010] FIG. 4 is a graph depicting reputation scores at various
calculated probability values.
[0011] FIG. 5 is a flowchart depicting an operational scenario for
generating reputation scores.
[0012] FIG. 6 is a block diagram depicting use of non-reputable
criteria and reputable criteria for determining reputation
scores.
[0013] FIG. 7 is a block diagram depicting a reputation system
configured to respond with a return value that includes the
reputation score of a sender.
[0014] FIG. 8 is a block diagram depicting a server access
architecture.
DETAILED DESCRIPTION
[0015] FIG. 1 depicts at 30 a system for handling transmissions
received over a network 40. The transmissions can be many different
types of communications, such as electronic mail (e-mail) messages
sent from one or more messaging entities 50. The system 30 assigns
a classification to a messaging entity (e.g., messaging entity 52),
and based upon the classification assigned to the messaging entity,
an action is taken with respect to the messaging entity's
communication.
[0016] The system 30 uses a filtering system 60 and a reputation
system 70 to help process communications from the messaging
entities 50. The filtering system 60 uses the reputation system 70
to help determine what filtering action (if any) should be taken
upon the messaging entities' communications. For example, the
communication may be determined to be from a reputable source and
thus the communication should not be filtered.
[0017] The filtering system 60 identifies at 62 one or more message
characteristics associated with a received communication and
provides that identification information to the reputation system
70. The reputation system 70 evaluates the reputation by
calculating probabilities that the identified message
characteristic(s) exhibit certain qualities. An overall reputation
score is determined based upon the calculated probabilities and is
provided to the filtering system 60.
[0018] The filtering system 60 examines at 64 the reputation score
in order to determine what action should be taken for the sender's
communication (such as whether the communication transmission
should be delivered to the communication's designated recipient
located within a message receiving system 80). The filtering system
60 could decide that a communication should be handled differently
based in whole or in part upon the reputation scored that was
provided by the reputation system 70. As an illustration, a
communication may be determined to be from a non-reputable sender
and thus the communication should be handled as Spam (e.g.,
deleted, quarantined, etc.).
[0019] Reputation systems may be configured in many different ways
in order to assist a filtering system. For example, a reputation
system 70 can be located externally or internally relative to the
filtering system 60 depending upon the situation at hand. As
another example, FIG. 2 depicts a reputation system 70 that has
been configured to calculate reputation scores based upon such
message characteristic identification information as sender
identity as shown at 82. It should be understood that other message
characteristics can be used instead of or in addition to sender
identity. Moreover, transmissions may be from many different types
of messaging entities, such as a domain name, IP address, phone
number, or individual electronic address or username representing
an organization, computer, or individual user that transmits
electronic messages. For example, generated classifications of
reputable and non-reputable can be based upon a tendency for an IP
address to send unwanted transmissions or legitimate
communication.
[0020] The system's configuration 90 could also, as shown in FIG.
2, be established by identifying a set of binary, testable criteria
92 which appear to be strong discriminators between good and bad
senders. P (NR|C.sub.i) can be defined as the probability that a
sender is non-reputable, given that it conforms to
quality/criterion C.sub.i, and P (R|C.sub.i) can be defined as the
probability that a sender is reputable, given that it conforms to
quality/criterion C.sub.i.
[0021] For each quality/criterion C.sub.i, periodic (e.g., daily,
weekly, monthly, etc.) sampling exercises can be performed to
recalculate P (NR|C.sub.i). A sampling exercise may include
selecting a random sample set S of N senders for which
quality/criterion C.sub.i is known to be true. The senders in the
sample are then sorted into one of the following sets: reputable
(R), non-reputable (NR) or unknown (U). N.sub.R is the number of
senders in the sample that are reputable senders, N.sub.NR is the
number of senders that are non-reputable senders, etc. Then, P
(NR|C.sub.i) and P (R|C.sub.i) are estimated using the formulas: P
.function. ( NR | C i ) = N NR N P .function. ( R | C i ) = N R N
##EQU1## For this purpose, N=30 was determined to be a large enough
sample size to achieve an accurate estimate of P (NR|C.sub.i) and P
(R|C.sub.i) for each quality/criterion C.sub.i.
[0022] After calculating P (NR|C.sub.i) and P (R|C.sub.i) for all
criteria, the computed probabilities are used to calculate an
aggregate non-reputable probability 94, P.sub.NR, and an aggregate
reputable sender probability 96, P.sub.R, for each sender in the
reputation space. These probabilities can be calculated using the
formulas: P NR = ( 1 - i = 1 N .times. { 1 - P .function. ( NR | C
i ) if .times. .times. criterion .times. .times. i .times. .times.
applies 1 otherwise ) ( # .times. .times. of .times. .times.
criteria .times. .times. that .times. .times. apply ) P R = ( 1 - i
= 1 N .times. { 1 - P .function. ( R | C i ) if .times. .times.
criterion .times. .times. i .times. .times. applies 1 otherwise ) (
# .times. .times. of .times. .times. criteria .times. .times. that
.times. .times. apply ) ##EQU2## In experimentation, the above
formulas appeared to behave very well for a wide range of input
criteria combinations, and in practice their behavior appears to be
similar to the behavior of the formula for correctly computing
naive joint conditional probabilities of "non-reputable" and
"reputable" behavior for the input criteria.
[0023] After calculating P.sub.NR and P.sub.R for each sender, a
reputation score is calculated for that sender using the following
reputation function: f(P.sub.NR,
P.sub.R)=(c.sub.1+c.sub.2P.sub.NR+c.sub.2P.sub.R+c.sub.3P.sub.NR.sup.2+c.-
sub.3P.sup.R.sup.2+c.sub.4P.sub.NRP.sub.R+c.sub.5P.sub.NR.sup.3+c.sub.5P.s-
ub.R.sup.3+c.sub.6P.sub.NRP.sub.R.sup.2+c.sub.6P.sub.NR.sup.2P.sub.R)((P.s-
ub.NR-P.sub.R).sup.3+c.sub.7(P.sub.NR-P.sub.R))
[0024] where [0025] c.sub.1=86.50 [0026] c.sub.2=-193.45 [0027]
c.sub.3=-35.19 [0028] c.sub.4=581.09 [0029] c.sub.5=234.81 [0030]
c.sub.6=-233.18 [0031] c.sub.7=0.51 It should be understood that
different functions can act as a reputation score determinator 98
and can be expressed in many different forms in addition to a
functional expression. As an illustration, FIG. 3 depicts at 100 a
tabular form for determining reputation scores. The table shows
reputation scores produced by the above function, based on values
of P.sub.NR and P.sub.R as they each vary between 0.0 and 1.0. For
example as shown at 110, a reputation score of 53 is obtained for
the combination of P.sub.NR=0.9 and P.sub.R=0.2. This reputation
score is a relatively high indicator that the sender should not be
considered reputable. A reputation score of 0 is obtained if
P.sub.NR and P.sub.R are the same (e.g., the reputation score is 0
if P.sub.NR=0.7 and P.sub.R=0.7 as shown at 120). A reputation
score can have a negative value to indicate that a sender is
relatively reputable as determined when P.sub.R is greater than
P.sub.NR. For example, if P.sub.NR=0.5 and P.sub.R=0.8 as shown at
130, then the reputation score is -12.
[0032] Reputation scores can be shown graphically as depicted in
FIG. 4 at 150. Graph 150 was produced by the above function, based
on values of P.sub.NR and P.sub.R. FIG. 4 illustrates reputation
score determinations in the context of Spam in that the terms
P.sub.NR and P.sub.R are used respectively as probability of
hamminess and probability of spamminess as the probabilities each
vary between 0.0 and 1.0.
[0033] As shown in these examples, reputation scores can be numeric
reputations that are assigned to messaging entities based on
characteristics of a communication (e.g., messaging entity
characteristic(s)) and/or a messaging entity's behavior. Numeric
reputations can fluctuate between a continuous spectrum of
reputable and non-reputable classifications. However, reputations
may be non-numeric, such as by having textual, or multiple level
textual categories.
[0034] FIG. 5 depicts an operational scenario wherein a reputation
system is used by a filtering system to generate reputation scores.
In this operational scenario, a reputation score is computed for a
particular sender (e.g., IP address, domain name, phone number,
address, name, etc), from a set of input data. With reference to
FIG. 5, data is gathered at step 200 that is needed to calculate
non-reputable and reputable probabilities for a sender. The data is
then aggregated at step 210 and used in probability calculations at
step 220. This includes determining, for a sender, non-reputable
probabilities and reputable probabilities for various selected
criteria. An aggregate non-reputable probability and an aggregate
reputable probability are then calculated for each sender.
[0035] After calculating an aggregate non-reputable probability and
an aggregate reputable probability for each sender, a reputation
score is calculated at 230 for that sender using a reputation
function. At step 240, the sender's reputation score is distributed
locally and/or to one or more systems to evaluate a communication
associated with the sender. As an illustration, reputation scores
can be distributed to a filtering system. With the reputation
score, the filtering system can choose to take an action on the
transmission based on the range the sender reputation score falls
into. For unreputable senders, a filtering system can choose to
drop the transmission (e.g., silently), save it in a quarantine
area, or flag the transmission as suspicious. In addition, a filter
system can choose to apply such actions to all future transmissions
from this sender for a specified period of time, without requiring
new lookup queries to be made to the reputation system. For
reputable senders, a filtering system can similarly apply actions
to the transmissions to allow them to bypass all or certain
filtering techniques that cause significant processing, network, or
storage overhead for the filtering system.
[0036] It should be understood that similar to the other processing
flows described herein, the processing and the order of the
processing may be altered, modified and/or augmented and still
achieve the desired outcome. For example, an optional addition to
the step of extracting unique identifying information about the
sender of the transmission would be to use sender authentication
techniques to authenticate certain parts of the transmission, such
as the purported sending domain name in the header of the message,
to unforgeable information about the sender, such as the IP address
the transmission originated from. This process can allow the
filtering system to perform lookups on the reputation system by
querying for information that can potentially be forged, had it not
been authenticated, such as a domain name or email address. If such
domain or address has a positive reputation, the transmission can
be delivered directly to the recipient system bypassing all or some
filtering techniques. If it has a negative reputation, the
filtering system can choose to drop the transmission, save it in a
quarantine area, or flag it as suspicious.
[0037] Many different types of sender authentication techniques can
be used, such as the Sender Policy Framework (SPF) technique. SPF
is a protocol by which domain owners publish DNS records that
indicate which IP addresses are allowed to send mail on behalf of a
given domain. As other non-limiting examples, SenderID or
DomainKeys can be used as sender authentication techniques.
[0038] As another example, many different types of criteria may be
used in processing a sender's communication. FIG. 6 depicts the use
of non-reputable criteria 300 and reputable criteria 310 for use in
determining reputation scores.
[0039] The non-reputable criteria 300 and reputable criteria 310
help to distinguish non-reputable senders and reputable senders. A
set of criteria can change often without significantly affecting
the reputation scores produced using this scoring technique. As an
illustration within the context of SPAM identification, the
following is a list of spamminess criteria that could be used in
the reputation scoring of a message sender. The list is not
intended to be exhaustive, and can be adapted to include other
criteria or remove criteria based upon observed behavior. [0040] 1.
Mean Spam Score: A sender is declared "non-reputable" if a mean
spam profiler score of transmissions that it sends exceeds some
threshold, W. [0041] 2. RDNS Lookup Failure: A sender is declared
"non-reputable" if reverse domain name system (RDNS) queries for
its IP addresses fail. [0042] 3. RBL Membership: A sender is
declared "non-reputable" if it is included in a real-time blackhole
list (RBL). (Note: multiple RBLs may be used. Each RBL can
constitute a separate testing criterion.) [0043] 4. Mail Volume: A
sender is declared "non-reputable" if its average (mean or median)
transmission volume exceeds a threshold, X, where X is measured in
transmissions over a period of time (such as, e.g., a day, week, or
month). (Note: multiple average volumes over multiple time periods
may be used, and each average volume can constitute a separate
testing criterion.) [0044] 5. Mail Burstiness/Sending History: A
sender is declared "non-reputable" if its average (mean or median)
transmission traffic pattern burstiness (defined by the number of
active sending sub-periods within a larger time period, e.g.,
number of active sending hours in a day or number of active sending
days in a month) is less than some threshold, Y, where Y is
measured in sub-periods per period. (Note: multiple average
burstiness measures over multiple time periods may be used, and
each average burstiness measure can constitute a separate testing
criterion.) [0045] 6. Mail Breadth: A sender is declared
"non-reputable" if its average (mean or median) transmission
traffic breadth (as defined by the percentage of systems that
receive transmissions from the same sender during a period of time
(such as, e.g., a day, week, or month)) exceeds some threshold, Z.
(Note: multiple average breadths over multiple time periods may be
used, and each average breadth measure can constitute a separate
testing criterion.) [0046] 7. Malware Activity: A sender is
declared "non-reputable" if it is known to have delivered one or
more malware codes (such as, e.g., viruses, spyware, intrusion
code, etc) during a measurement period (e.g., a day, week, or
month). [0047] 8. Type of Address: A sender is declared
"non-reputable" if it is known to be dynamically assigned to
dial-up or broadband dynamic host control protocol (DHCP) clients
by an internet service provider (ISP). [0048] 9. CIDR Block
Spamminess: A sender is declared "non-reputable" if its IP
addresses are known to exist within classless inter-domain routing
(CIDR) blocks that contain predominantly "non-reputable" IP
addresses. [0049] 10. Human Feedback: A sender is declared
"non-reputable" if it is reported to have sent undesirable
transmissions by people analyzing the content and other
characteristics of those transmissions. [0050] 11. SpamTrap
Feedback: A sender is declared "non-reputable" if it is sending
transmissions to accounts that have been declared as spamtraps and
as such are not supposed to receive any legitimate transmissions.
[0051] 12. Bounceback Feedback: A sender is declared
"non-reputable" if it is sending bounceback transmissions or
transmissions to accounts that do not exist on the destination
system. [0052] 13. Legislation/Standards Conformance: A sender is
declared "non-reputable" if it is not conforming to laws,
regulations, and well-established standards of transmission
behavior in the countries of operation of either the sender and/or
the recipient of the transmissions. [0053] 14. Continuity of
Operation: A sender is declared "non-reputable" if it has not
operated at that sending location longer than some threshold Z.
[0054] 15. Responsiveness to Recipient Demands: A sender is
declared "non-reputable" if it is not responding in a reasonable
timeframe to legitimate demands of the recipients to terminate
their relationship with the sender to not receive any more
transmissions from them.
[0055] The following is a list of "reputable" criteria that could
be used in determining the "reputability" of a sender. The list is
not intended to be exhaustive, and can be adapted to include other
criteria or remove criteria based upon observed behavior. [0056] 1.
Mean Spam Score: A sender is declared "reputable" if the mean spam
profiler score of transmissions that it sends falls below some
threshold, W. [0057] 2. Human Feedback: A sender is declared
"reputable" if it is reported to have sent only legitimate
transmissions by people analyzing transmission flows from that
sender, in conjunction with the reputation of the organization that
owns those sending stations.
[0058] After computing a reputation grade for each sender in the
universe of senders, a reputation classification can be made
available via a communication protocol that can be interpreted by
the queriers that make use of the reputation system (e.g., DNS,
HTTP, etc). As shown in FIG. 7, when a query 350 is issued for a
sender, the reputation system can respond with a return value 360
that includes the reputation score of that sender, as well as any
other relevant additional information that can be used by the
querier to make the final judgment on the acceptability of the
sender's transmission (e.g., age of the reputation score, input
data that determined the score, etc).
[0059] An example of a communication protocol that can be used is a
domain name system (DNS) server which can respond with a return
value in the form of an IP address: 172.x.y.z. The IP address can
be encoded using the formula: IP = 172 ( rep - rep 2 .times. rep )
( rep .times. div .times. .times. 256 ) ( rep .times. .times. mod
.times. .times. .times. 256 ) ##EQU3##
[0060] The reputation of the queried sender can be deciphered from
the return value as follows: rep=(-1).sup.2-x.times.(256y+z)
[0061] Therefore, when x=0, the returned reputation is a positive
number, and when x=1, the returned reputation is a negative number.
The absolute value of the reputation is determined by the values of
y and z. This encoding scheme enables the server to return via the
DNS protocol reputation values within the range [-65535, 65535]. It
also leaves seven (7) unused bits, namely the seven high-order bits
of x. These bits can be reserved for extensions to the reputation
system. (For example, the age of a reputation score may be
communicated back to the querier.)
[0062] The systems and methods disclosed herein may be implemented
on various types of computer architectures, such as for example on
different types of networked environments. As an illustration, FIG.
8 depicts a server access architecture within which the disclosed
systems and methods may be used (e.g., as shown at 30 in FIG. 8).
The architecture in this example includes a corporation's local
network 490 and a variety of computer systems residing within the
local network 490. These systems can include application servers
420 such as Web servers and e-mail servers, user workstations
running local clients 430 such as e-mail readers and Web browsers,
and data storage devices 410 such as databases and network
connected disks. These systems communicate with each other via a
local communication network such as Ethernet 450. Firewall system
440 resides between the local communication network and Internet
460. Connected to the Internet 460 are a host of external servers
470 and external clients 480.
[0063] Local clients 430 can access application servers 420 and
shared data storage 410 via the local communication network.
External clients 480 can access external application servers 470
via the Internet 460. In instances where a local server 420 or a
local client 430 requires access to an external server 470 or where
an external client 480 or an external server 470 requires access to
a local server 420, electronic communications in the appropriate
protocol for a given application server flow through "always open"
ports of firewall system 440.
[0064] A system 30 as disclosed herein may be located in a hardware
device or on one or more servers connected to the local
communication network such as Ethernet 480 and logically interposed
between the firewall system 440 and the local servers 420 and
clients 430. Application-related electronic communications
attempting to enter or leave the local communications network
through the firewall system 440 are routed to the system 30.
[0065] In the example of FIG. 8, system 30 could be configured to
store and process reputation data about many millions of senders as
part of a threat management system. This would allow the threat
management system to make better informed decisions about allowing
or blocking electronic mail (e-mail).
[0066] System 30 could be used to handle many different types of
e-mail and its variety of protocols that are used for e-mail
transmission, delivery and processing including SMTP and POP3.
These protocols refer, respectively, to standards for communicating
e-mail messages between servers and for server-client communication
related to e-mail messages. These protocols are defined
respectively in particular RFC's (Request for Comments) promulgated
by the IETF (Internet Engineering Task Force). The SMTP protocol is
defined in RFC 821, and the POP3 protocol is defined in RFC
1939.
[0067] Since the inception of these standards, various needs have
evolved in the field of e-mail leading to the development of
further standards including enhancements or additional protocols.
For instance, various enhancements have evolved to the SMTP
standards leading to the evolution of extended SMTP. Examples of
extensions may be seen in (1) RFC 1869 that defines a framework for
extending the SMTP service by defining a means whereby a server
SMTP can inform a client SMTP as to the service extensions it
supports and in (2) RFC 1891 that defines an extension to the SMTP
service, which allows an SMTP client to specify (a) that delivery
status notifications (DSNs) should be generated under certain
conditions, (b) whether such notifications should return the
contents of the message, and (c) additional information, to be
returned with a DSN, that allows the sender to identify both the
recipient(s) for which the DSN was issued, and the transaction in
which the original message was sent. In addition, the IMAP protocol
has evolved as an alternative to POP3 that supports more advanced
interactions between e-mail servers and clients. This protocol is
described in RFC 2060.
[0068] Other communication mechanisms are also widely used over
networks. These communication mechanisms include, but are not
limited to, Voice Over IP (VoIP) and Instant Messaging. VoIP is
used in IP telephony to provide a set of facilities for managing
the delivery of voice information using the Internet Protocol (IP).
Instant Messaging is a type of communication involving a client
which hooks up to an instant messaging service that delivers
communications (e.g., conversations) in realtime.
[0069] As the Internet has become more widely used, it has also
created new troubles for users. In particular, the amount of spam
received by individual users has increased dramatically in the
recent past. Spam, as used in this specification, refers to any
communication receipt of which is either unsolicited or not desired
by its recipient. A system and method can be configured as
disclosed herein to address these types of unsolicited or undesired
communications. This can be helpful in that e-mail spamming
consumes corporate resources and impacts productivity.
[0070] The systems and methods disclosed herein are presented only
by way of example and are not meant to limit the scope of the
invention. Other variations of the systems and methods described
above will be apparent to those skilled in the art and as such are
considered to be within the scope of the invention. For example,
using the systems and methods of sender classification described
herein, a reputation system can be configured for use in training
and tuning of external filtering techniques. Such techniques may
include Bayesian, Support Vector Machine (SVM) and other
statistical content filtering techniques, as well as
signature-based techniques such as distributed bulk message
identification and message clustering-type techniques. The training
strategies for such techniques can require sets of classified
legitimate and unwanted transmissions, which can be provided to the
trainer by classifying streams of transmissions based on the
reputation scores of their senders. Transmissions from senders
classified as un-reputable can be provided to the filtering system
trainer as unwanted, and the wanted transmissions can be taken from
the stream sent by the legitimate senders.
[0071] As an illustration, methods and systems can be configured to
perform tuning and training of filtering systems utilizing
reputation scores of senders of transmissions in sets of trainable
transmissions. At least one characteristic is identified about
transmissions from senders. The identifying of at least one
characteristic can include extracting unique identifying
information about the transmissions (e.g., information about the
senders of the transmissions), or authenticating unique identifying
information about the transmissions, or combinations thereof.
Queries are sent to a reputation system and scores are received
representing reputations of the senders. Transmissions are
classified into multiple categories based on a range a sender's
reputation score falls into. Transmissions and their classification
categories are passed on to a trainer of another filtering system
to be used for optimization of the filtering system.
[0072] As another example, methods and systems can be configured to
perform filtering of groups of transmissions utilizing reputation
scores of senders of transmissions. Multiple transmissions can be
grouped together based on content similarities or similarities in
transmission sender behavior. At least one characteristic can be
identified about each transmission in the groupings. The
identifying of at least one characteristic can include extracting
unique identifying information about the transmission (e.g.,
information about the sender of a transmission), or authenticating
unique identifying information about the transmission, or
combinations thereof. A query can be sent to the reputation system
and receive a score representing reputation of each sender. Groups
of transmissions can be classified based on the percentage of
reputable and non-reputable senders in the group.
[0073] As another example of the wide variations of the disclosed
systems and methods, different techniques can be used for
computation of joint conditional probabilities. More specifically,
different mathematical techniques can be used for computing the
aggregate non-reputable sender probability, P.sub.NR, and the
aggregate reputable sender probability, P.sub.R, for each sender in
the reputation space. As an illustration, two techniques are
described. Both techniques use P (NR|C.sub.i) and P (R|C.sub.i),
the conditional probabilities of non-reputable and reputable
behavior, for each testing criterion C.sub.i. The first technique
makes the assumption that all testing criteria are independent. The
second technique incorporates the assumption that the testing
criteria are not independent. Therefore, the second technique is
more difficult to carry out, but produces more accurate
results.
[0074] 1. Technique for Independent Testing Criteria
[0075] In the independent case, it is assumed that each criterion
C.sub.i is independent of all other criteria. The probability that
the sender is non-reputable, P.sub.NR, is calculated using the
following formula: P NR = P .function. ( NR | C i ) P .function. (
NR | C j ) + ( 1 - P .function. ( NR | C j ) ) ##EQU4## where j
ranges over all criteria that apply to the sender in question.
Similarly, the probability that the sender is a reputable sender,
P.sub.R, is calculated using the following formula: P R = P
.function. ( R | C j ) P .function. ( R | C j ) + ( 1 - P
.function. ( R | C j ) ) ##EQU5## where j ranges over all criteria
that apply to the sender in question.
[0076] 2. Technique for Non-Independent Testing Criteria
[0077] In the dependent case, it is assumed that each criterion
C.sub.i is not independent of all other criteria, so the analysis
must take into account "non-linear" interactions between criteria
within their joint probability distribution. To find the correct
values for P.sub.NR and P.sub.R for a given sender, a table is
constructed to represent the entire joint probability distribution.
Below is a sample table for a joint distribution of four
qualities/criteria. TABLE-US-00001 Case C.sub.1 C.sub.2 C.sub.3
C.sub.4 P.sub.NR P.sub.R 1 N N N N N/A N/A 2 N N N Y P(NR|C.sub.4)
P(R|C.sub.4) 3 N N Y N P(NR|C.sub.3) P(R|C.sub.3) 4 N N Y Y
P(NR|C.sub.3, C.sub.4) P(R|C.sub.3, C.sub.4) 5 N Y N N
P(NR|C.sub.2) P(R|C.sub.2) 6 N Y N Y P(NR|C.sub.2, C.sub.4)
P(R|C.sub.2, C.sub.4) 7 N Y Y N P(NR|C.sub.2, C.sub.3) P(R|C.sub.2,
C.sub.3) 8 N Y Y Y P(NR|C.sub.2, C.sub.3, C.sub.4) P(R|C.sub.2,
C.sub.3, C.sub.4) 9 Y N N N P(NR|C.sub.1) P(R|C.sub.1) 10 Y N N Y
P(NR|C.sub.1, C.sub.4) P(R|C.sub.1, C.sub.4) 11 Y N Y N
P(NR|C.sub.1, C.sub.3) P(R|C.sub.1, C.sub.3) 12 Y N Y Y
P(NR|C.sub.1, C.sub.3, C.sub.4) P(R|C.sub.1, C.sub.3, C.sub.4) 13 Y
Y N N P(NR|C.sub.1, C.sub.2) P(R|C.sub.1, C.sub.2) 14 Y Y N Y
P(NR|C.sub.1, C.sub.2, C.sub.4) P(R|C.sub.1, C.sub.2, C.sub.4) 15 Y
Y Y N P(NR|C.sub.1, C.sub.2, C.sub.3) P(R|C.sub.1, C.sub.2,
C.sub.3) 16 Y Y Y Y P(NR|C.sub.1, C.sub.2, C.sub.3, C.sub.4)
P(R|C.sub.1, C.sub.2, C.sub.3, C.sub.4)
For a distribution of M criteria, there exist (2M-1) distinct cases
within the joint probability distribution. Each case constitutes a
particular combination of characteristics. The probability that the
sender is non-reputable, P.sub.NR, is estimated for each case using
the following technique. For each one of the (2M-1) cases, a random
sample of N senders is gathered that exhibit the combination of
characteristics described by that case. (For this purposes, N=30 is
a large enough sample). Each sender is sorted into one of the
following sets: reputable (R), non-reputable (NR) or unknown (U).
NR is the number of sender in the sample that are reputable
senders, N.sub.NR is the number of senders that are non-reputable
senders, etc. Then, P.sub.NR and P.sub.R is estimated using the
formulas: P NR = N NR N P R = N R N ##EQU6## The sampling of the IP
addresses is repeated periodically (e.g., daily, weekly, monthly)
to update the joint probability distribution.
[0078] It is further noted that the systems and methods disclosed
herein may use articles of manufacture having data/digital signals
conveyed via networks (e.g., local area network, wide area network,
internet, etc.), fiber optic medium, carrier waves, wireless
networks, etc. for communication with one or more data processing
devices. The data/digital signals can carry any or all of the data
disclosed herein that is provided to or from a device.
[0079] Additionally, the methods and systems described herein may
be implemented on many different types of processing devices by
program code comprising program instructions that are executable by
one or more processors. The software program instructions may
include source code, object code, machine code, or any other stored
data that is operable to cause a processing system to perform
methods described herein.
[0080] The systems' and methods' data (e.g., associations,
mappings, etc.) may be stored and implemented in one or more
different types of computer-implemented ways, such as different
types of storage devices and programming constructs (e.g., data
stores, RAM, ROM, Flash memory, flat files, databases, programming
data structures, programming variables, IF-THEN (or similar type)
statement constructs, etc.). It is noted that data structures
describe formats for use in organizing and storing data in
databases, programs, memory, or other computer-readable media for
use by a computer program.
[0081] The systems and methods may be provided on many different
types of computer-readable media including computer storage
mechanisms (e.g., CD-ROM, diskette, RAM, flash memory, computer's
hard drive, etc.) that contain instructions for use in execution by
a processor to perform the methods' operations and implement the
systems described herein.
[0082] The computer components, software modules, functions and
data structures described herein may be connected directly or
indirectly to each other in order to allow the flow of data needed
for their operations. It is also noted that software instructions
or a module can be implemented for example as a subroutine unit of
code, or as a software function unit of code, or as an object (as
in an object-oriented paradigm), or as an applet, or in a computer
script language, or as another type of computer code or firmware.
The software components and/or functionality may be located on a
single device or distributed across multiple devices depending upon
the situation at hand.
[0083] It should be understood that as used in the description
herein and throughout the claims that follow, the meaning of "a,"
"an," and "the" includes plural reference unless the context
clearly dictates otherwise. Also, as used in the description herein
and throughout the claims that follow, the meaning of "in" includes
"in" and "on" unless the context clearly dictates otherwise.
Finally, as used in the description herein and throughout the
claims that follow, the meanings of "and" and "or" include both the
conjunctive and disjunctive and may be used interchangeably unless
the context clearly dictates otherwise; the phrase "exclusive or"
may be used to indicate situation where only the disjunctive
meaning may apply.
* * * * *