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@ -83,7 +83,7 @@ Table of Contents
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1. Introduction
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1. Introduction
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Sharing threat information became a fundamental requirements in the
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Sharing threat information became a fundamental requirements on the
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Internet, security and intelligence community at large. Threat
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Internet, security and intelligence community at large. Threat
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information can include indicators of compromise, malicious file
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information can include indicators of compromise, malicious file
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indicators, financial fraud indicators or even detailed information
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indicators, financial fraud indicators or even detailed information
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vocabularies that can be used in threat information sharing.
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vocabularies that can be used in threat information sharing.
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Machine tags were introduced in 2007 [machine-tags] to allow users to
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Machine tags were introduced in 2007 [machine-tags] to allow users to
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be more precise when tagging their picture with geolocation. So a
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be more precise when tagging their pictures with geolocation. So a
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machine tag is a tag which use a special syntax to provide more
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machine tag is a tag which uses a special syntax to provide more
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information to user and machines. Machine tags are also known as
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information to users and machines. Machine tags are also known as
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triple tags due to the their format.
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triple tags due to their format.
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In MISP taxonomy context, machine tags help analysts to classify
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In the MISP taxonomy context, machine tags help analysts to classify
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their cybersecurity events, indicators or threats. MISP taxonomy can
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their cybersecurity events, indicators or threats. MISP taxonomies
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be used for classification, filtering, triggering action or
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can be used for classification, filtering, triggering actions or
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visualization depending on their use in threat intelligence platforms
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visualisation depending on their use in threat intelligence platforms
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like MISP [MISP-P].
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such as MISP [MISP-P].
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A machine tag is composed of a namespace (MUST), a predicate (MUST)
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A machine tag is composed of a namespace (MUST), a predicate (MUST)
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and an optional value (OPTIONAL).
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and an optional value (OPTIONAL).
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Machine tags are represented as a string. Below a set of sample
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Machine tags are represented as a string. Below listed are a set of
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machine tags for different namespaces like tlp, admiralty-scale or
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sample machine tags for different namespaces such as tlp, admiralty-
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osint.
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scale and osint.
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tlp:amber
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tlp:amber
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admiralty-scale:information-credibility="1"
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admiralty-scale:information-credibility="1"
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The MISP taxonomy format describes how to define a machine tag
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The MISP taxonomy format describes how to define a machine tag
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namespace in a parseable format. The objective is to provide a
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namespace in a parseable format. The objective is to provide a
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simple format to describe machine tags (aka triple tags)
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simple format to describe machine tag (aka triple tag) vocabularies.
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vocabularies.
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2.1. Overview
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2.1. Overview
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The MISP taxonomy format uses the JSON [RFC4627] format. Each
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The MISP taxonomy format uses the JSON [RFC4627] format. Each
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namespace is represented as a JSON object with meta information
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namespace is represented as a JSON object with meta information
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including the following fields namespace, description, version.
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including the following fields: namespace, description, version.
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namespace defines the overall namespace of the machine tag. The
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namespace defines the overall namespace of the machine tag. The
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namespace is represented as a string and MUST be present. The
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namespace is represented as a string and MUST be present. The
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predicates MUST be present and MUST at least content one element.
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predicates MUST be present and MUST at least content one element.
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values defines all the values for each predicate in the namespace
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values defines all the values for each predicate in the namespace
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defined. values SHOULD ne present.
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defined. values SHOULD be present.
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2.2. predicates
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2.2. predicates
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predicates array contain one or more JSON objects which lists all the
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The predicates array contains one or more JSON objects which lists
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possible predicate. The JSON object contains two fields: value and
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all the possible predicates. The JSON object contains two fields:
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expanded. value and expanded MUST be present. value is represented as
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value and expanded. value and expanded MUST be present. value is
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a string and describes the predicate value. The predicate value MUST
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represented as a string and describes the predicate value. The
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predicate value MUST not contain spaces or colons. expanded is
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@ -170,18 +170,18 @@ Dulaunoy & Iklody Expires April 16, 2017 [Page 3]
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Internet-Draft MISP taxonomy format October 2016
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Internet-Draft MISP taxonomy format October 2016
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not contain spaces or colons. expanded is represented as a string and
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represented as a string and describes the human-readable version of
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describes the human-readable version of the predicate value.
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the predicate value.
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2.3. values
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2.3. values
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values array contain one or more JSON objects which lists all the
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The values array contain one or more JSON objects which lists all the
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possible values of a predicate. The JSON object contain two fields:
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possible values of a predicate. The JSON object contains two fields:
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predicate and entry. predicate is represented as a string and
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predicate and entry. predicate is represented as a string and
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describes the predicate value. entry is an array with one or more
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describes the predicate value. entry is an array with one or more
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JSON objects. The JSON object contains two fields: value and
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JSON objects. The JSON object contains two fields: value and
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expanded. value and expanded MUST be present. value is represented as
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expanded. value and expanded MUST be present. value is represented as
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a string and describe the value machine parsable. expanded is
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a string and describes the machine parsable value. expanded is
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represented as a string and describes the human-readable version of
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represented as a string and describes the human-readable version of
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the value.
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the value.
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2.4.1. colour
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2.4.1. colour
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colour fields MAY be used at predicates or values level to set a
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colour fields MAY be used at predicates or values level to set a
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specify colour that *MAY** be used by the implementation. The colour
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specify colour that MAY be used by the implementation. The colour
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field is described as an RGB colour fill in hexadecimal
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field is described as an RGB colour fill in hexadecimal
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representation.
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representation.
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description fields MAY be used at predicates or values level to add a
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description fields MAY be used at predicates or values level to add a
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descriptive and human-readable information about the specific
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descriptive and human-readable information about the specific
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predicate or value. The field is represented as a string.
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predicate or value. The field is represented as a string.
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Implementations *_MAY_ use the description field to improve more
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Implementations MAY use the description field to improve more
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2.4.3. numerical_value
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2.4.3. numerical_value
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numerical_value fields MAY be used at predicates or values level to
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numerical_value fields MAY be used at a predicate or value level to
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add a machine-readable numeric value to a specific predicate or
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add a machine-readable numeric value to a specific predicate or
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value. The field is represented as JSON number. Implementations
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value. The field is represented as a JSON number. Implementations
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SHOULD use the decimal value provided to support scoring or
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SHOULD use the decimal value provided to support scoring or
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filtering.
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filtering.
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