Proofpoint Targeted Attack Protection
Overview
Proofpoint Targeted Attack Protection (TAP) helps detect, mitigate, and block advanced threats that target people through email.
- Vendor: Proofpoint
- Supported environment: Cloud
- Detection based on: Telemetry
- Supported application or feature: Email gateway
Configure
Proofpoint Targeted Attack Protection
As a prerequisite, you need to create a service principal and a secret on the setting page:
- Sign in to the dashboard
- Go to
Settings > Connected Applications - Click
Create New Credential - Type the name of the new credential set
- Generate the Service Principal and Secret values by clicking
Generate
Create the intake
- Go to the intake page and create a new intake from the format
Proofpoint TAP. - Set up the intake configuration with the service principal and the secret.
Start the playbook and enjoy your events.
Raw Events Samples
In this section, you will find examples of raw logs as generated natively by the source. These examples are provided to help integrators understand the data format before ingestion into Sekoia.io. It is crucial for setting up the correct parsing stages and ensuring that all relevant information is captured.
{
"campaignId": "46e01b8a-c899-404d-bcd9-189bb393d1a7",
"classification": "MALWARE",
"clickIP": "192.0.2.1",
"clickTime": "2016-06-24T19:17:44.000Z",
"GUID": "b27dbea0-87d5-463b-b93c-4e8b708289ce",
"id": "8c8b4895-a277-449f-r797-547e3c89b25a",
"messageID": "8c6cfedd-3050-4d65-8c09-c5f65c38da81",
"recipient": "bruce.wayne@pharmtech.zz",
"sender": "9facbf452def2d7efc5b5c48cdb837fa@badguy.zz",
"senderIP": "192.0.2.255",
"threatID": "61f7622167144dba5e3ae4480eeee78b23d66f7dfed970cfc3d086cc0dabdf50",
"threatTime": "2016-06-24T19:17:46.000Z",
"threatURL": "https://threatinsight.proofpoint.com/#/73aa0499-dfc8-75eb-1de8-a471b24a2e75/threat/u/61f7622167144dba5e3ae4480eeee78b23d66f7dfed970cfc3d086cc0dabdf50",
"threatStatus": "active",
"url": "http://badguy.zz/",
"userAgent": "Mozilla/5.0(WindowsNT6.1;WOW64;rv:27.0)Gecko/20100101Firefox/27.0",
"type": "click",
"status": "permitted"
}
{
"GUID": "c26dbea0-80d5-463b-b93c-4e8b708219ce",
"status": "delivered",
"type": "message",
"QID": "r2FNwRHF004109",
"ccAddresses": [
"bruce.wayne@university-of-education.zz"
],
"clusterId": "pharmtech_hosted",
"completelyRewritten": "true",
"fromAddress": [
"badguy@evil.zz"
],
"headerCC": "\"Bruce Wayne\" <bruce.wayne@university-of-education.zz>",
"headerFrom": "\"A. Badguy\" <badguy@evil.zz>",
"headerReplyTo": null,
"headerTo": "\"Clark Kent\" <clark.kent@pharmtech.zz>; \"Diana Prince\" <diana.prince@pharmtech.zz>",
"impostorScore": 0,
"malwareScore": 100,
"messageID": "20160624211145.62086.mail@evil.zz",
"messageParts": [
{
"contentType": "text/plain",
"disposition": "inline",
"filename": "text.txt",
"md5": "008c5926ca861023c1d2a36653fd88e2",
"oContentType": "text/plain",
"sandboxStatus": "unsupported",
"sha256": "85738f8f9a7f1b04b5329c590ebcb9e425925c6d0984089c43a022de4f19c281"
},
{
"contentType": "application/pdf",
"disposition": "attached",
"filename": "Invoice for Pharmtech.pdf",
"md5": "5873c7d37608e0d49bcaa6f32b6c731f",
"oContentType": "application/pdf",
"sandboxStatus": "threat",
"sha256": "2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca"
}
],
"messageTime": "2016-06-24T21:18:38.000Z",
"modulesRun": [
"pdr",
"sandbox",
"spam",
"urldefense"
],
"phishScore": 46,
"policyRoutes": [
"default_inbound",
"executives"
],
"quarantineFolder": "Attachment Defense",
"quarantineRule": "module.sandbox.threat",
"recipient": [
"clark.kent@pharmtech.zz",
"diana.prince@pharmtech.zz"
],
"replyToAddress": null,
"sender": "e99d7ed5580193f36a51f597bc2c0210@evil.zz",
"senderIP": "192.0.2.255",
"spamScore": 4,
"subject": "Please find a totally safe invoice attached.",
"threatsInfoMap": [
{
"campaignId": "46e01b8a-c899-404d-bcd9-189bb393d1a7",
"classification": "MALWARE",
"threat": "2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca",
"threatId": "2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca",
"threatStatus": "active",
"threatTime": "2016-06-24T21:18:38.000Z",
"threatType": "ATTACHMENT",
"threatUrl": "https://threatinsight.proofpoint.com/#/73aa0499-dfc8-75eb-1de8-a471b24a2e75/threat/u/2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca"
},
{
"campaignId": "46e01b8a-c899-404d-bcd9-189bb393d1a7",
"classification": "MALWARE",
"threat": "badsite.zz",
"threatId": "3ba97fc852c66a7ba761450edfdfb9f4ffab74715b591294f78b5e37a76481aa",
"threatTime": "2016-06-24T21:18:07.000Z",
"threatType": "url",
"threatUrl": "https://threatinsight.proofpoint.com/#/73aa0499-dfc8-75eb-1de8-a471b24a2e75/threat/u/3ba97fc852c66a7ba761450edfdfb9f4ffab74715b591294f78b5e37a76481aa"
}
],
"toAddresses": [
"clark.kent@pharmtech.zz",
"diana.prince@pharmtech.zz"
],
"xmailer": "Spambot v2.5"
}
Detection section
The following section provides information for those who wish to learn more about the detection capabilities enabled by collecting this intake. It includes details about the built-in rule catalog, event categories, and ECS fields extracted from raw events. This is essential for users aiming to create custom detection rules, perform hunting activities, or pivot in the events page.
Related Built-in Rules
The following Sekoia.io built-in rules match the intake Proofpoint TAP. This documentation is updated automatically and is based solely on the fields used by the intake which are checked against our rules. This means that some rules will be listed but might not be relevant with the intake.
SEKOIA.IO x Proofpoint TAP on ATT&CK Navigator
Burp Suite Tool Detected
Burp Suite is a cybersecurity tool. When used as a proxy service, its purpose is to intercept packets and modify them to send them to the server. Burp Collaborator is a network service that Burp Suite uses to help discover many kinds of vulnerabilities (vulnerabilities scanner).
- Effort: intermediate
CVE-2020-0688 Microsoft Exchange Server Exploit
Detects the exploitation of CVE-2020-0688. The POC exploit a .NET serialization vulnerability in the Exchange Control Panel (ECP) web page. The vulnerability is due to Microsoft Exchange Server not randomizing the keys on a per-installation basis resulting in them using the same validationKey and decryptionKey values. With knowledge of these, values an attacker can craft a special viewstate to use an OS command to be executed by NT_AUTHORITY\SYSTEM using .NET deserialization. To exploit this vulnerability, an attacker needs to leverage the credentials of an account it had already compromised to authenticate to OWA.
- Effort: elementary
CVE-2020-17530 Apache Struts RCE
Detects the exploitation of the Apache Struts RCE vulnerability (CVE-2020-17530).
- Effort: intermediate
CVE-2021-20021 SonicWall Unauthenticated Administrator Access
Detects the exploitation of SonicWall Unauthenticated Admin Access.
- Effort: advanced
CVE-2021-20023 SonicWall Arbitrary File Read
Detects Arbitrary File Read, which can be used with other vulnerabilities as a mean to obtain outputs generated by attackers, or sensitive data.
- Effort: advanced
CVE-2021-22893 Pulse Connect Secure RCE Vulnerability
Detects potential exploitation of the authentication by-pass vulnerability that can allow an unauthenticated user to perform remote arbitrary file execution on the Pulse Connect Secure gateway. It is highly recommended to apply the Pulse Secure mitigations and seach for indicators of compromise on affected servers if you are in doubt over the integrity of your Pulse Connect Secure product.
- Effort: intermediate
Covenant Default HTTP Beaconing
Detects potential Covenant communications through the user-agent and specific urls
- Effort: intermediate
Cryptomining
Detection of domain names potentially related to cryptomining activities.
- Effort: master
Detect requests to Konni C2 servers
This rule detects requests to Konni C2 servers. These patterns come from an analysis done in 2022, September.
- Effort: elementary
Discord Suspicious Download
Discord is a messaging application. It allows users to create their own communities to share messages and attachments. Those attachments have little to no overview and can be downloaded by almost anyone, which has been abused by attackers to host malicious payloads.
- Effort: intermediate
Download Files From Suspicious TLDs
Detects download of certain file types from hosts in suspicious TLDs
- Effort: master
Dynamic DNS Contacted
Detect communication with dynamic dns domain. This kind of domain is often used by attackers. This rule can trigger false positive in non-controlled environment because dynamic dns is not always malicious.
- Effort: master
Exfiltration Domain
Detects traffic toward a domain flagged as a possible exfiltration vector.
- Effort: master
Koadic MSHTML Command
Detects Koadic payload using MSHTML module
- Effort: intermediate
Nimbo-C2 User Agent
Nimbo-C2 Uses an unusual User-Agent format in its implants.
- Effort: intermediate
Possible Malicious File Double Extension
Detects request to potential malicious file with double extension
- Effort: elementary
Potential Bazar Loader User-Agents
Detects potential Bazar loader communications through the user-agent
- Effort: elementary
Potential Lemon Duck User-Agent
Detects LemonDuck user agent. The format used two sets of alphabetical characters separated by dashes, for example "User-Agent: Lemon-Duck-[A-Z]-[A-Z]".
- Effort: elementary
Proofpoint TAP Email Classified As Malware But Allowed
An email was classified as malware with a threat score greater than 0 by Proofpoint TAP but was not blocked. The threshold on the Threat Score has been defined to avoid a high amount of false positives.
- Effort: advanced
Proofpoint TAP Email Classified As Phishing But Allowed
An email was classified as phishing with a threat score greater than 50 by Proofpoint TAP but was not blocked. The threshold on the Threat Score has been defined to avoid a high amount of false positives.
- Effort: advanced
Proofpoint TAP Email Classified As Spam But Allowed
An email was classified as spam with a threat score greater than 50 by Proofpoint TAP but was not blocked. The threshold on the Threat Score has been defined to avoid a high amount of false positives.
- Effort: advanced
ProxyShell Microsoft Exchange Suspicious Paths
Detects suspicious calls to Microsoft Exchange resources, in locations related to webshells observed in campaigns using this vulnerability.
- Effort: elementary
Raccoon Stealer 2.0 Legitimate Third-Party DLL Download URL
Detects Raccoon Stealer 2.0 malware downloading legitimate third-party DLLs from its C2 server. These legitimate DLLs are used by the information stealer to collect data on the compromised hosts.
- Effort: elementary
Remote Access Tool Domain
Detects traffic toward a domain flagged as a Remote Administration Tool (RAT).
- Effort: master
Remote Monitoring and Management Software - AnyDesk
Detect artifacts related to the installation or execution of the Remote Monitoring and Management tool AnyDesk.
- Effort: master
SEKOIA.IO Intelligence Feed
Detect threats based on indicators of compromise (IOCs) collected by SEKOIA's Threat and Detection Research team.
- Effort: elementary
Sekoia.io EICAR Detection
Detects observables in Sekoia.io CTI tagged as EICAR, which are fake samples meant to test detection.
- Effort: master
Suspicious Download Links From Legitimate Services
Detects users clicking on Google docs links to download suspicious files. This technique was used a lot by Bazar Loader in the past.
- Effort: intermediate
Suspicious TOR Gateway
Detects suspicious TOR gateways. Gateways are often used by the victim to pay and decrypt the encrypted files without installing TOR. Tor intercepts the network traffic from one or more apps on user’s computer, usually the user web browser, and shuffles it through a number of randomly-chosen computers before passing it on to its destination. This disguises user location, and makes it harder for servers to pick him/her out on repeat visits, or to tie together separate visits to different sites, this making tracking and surveillance more difficult. Before a network packet starts its journey, user’s computer chooses a random list of relays and repeatedly encrypts the data in multiple layers, like an onion. Each relay knows only enough to strip off the outermost layer of encryption, before passing what’s left on to the next relay in the list.
- Effort: advanced
Suspicious URI Used In A Lazarus Campaign
Detects suspicious requests to a specific URI, usually on an .asp page. The website is often compromised.
- Effort: intermediate
TOR Usage Generic Rule
Detects TOR usage globally, whether the IP is a destination or source. TOR is short for The Onion Router, and it gets its name from how it works. TOR intercepts the network traffic from one or more apps on user’s computer, usually the user web browser, and shuffles it through a number of randomly-chosen computers before passing it on to its destination. This disguises user location, and makes it harder for servers to pick him/her out on repeat visits, or to tie together separate visits to different sites, this making tracking and surveillance more difficult. Before a network packet starts its journey, user’s computer chooses a random list of relays and repeatedly encrypts the data in multiple layers, like an onion. Each relay knows only enough to strip off the outermost layer of encryption, before passing what’s left on to the next relay in the list.
- Effort: master
TrevorC2 HTTP Communication
Detects TrevorC2 HTTP communication based on the HTTP request URI and the user-agent.
- Effort: elementary
Event Categories
The following table lists the data source offered by this integration.
| Data Source | Description |
|---|---|
Email gateway |
Proofpoint TAP inspect, classify and detect threats targetting people through email. |
In details, the following table denotes the type of events produced by this integration.
| Name | Values |
|---|---|
| Kind | `` |
| Category | email, network |
| Type | info |
Transformed Events Samples after Ingestion
This section demonstrates how the raw logs will be transformed by our parsers. It shows the extracted fields that will be available for use in the built-in detection rules and hunting activities in the events page. Understanding these transformations is essential for analysts to create effective detection mechanisms with custom detection rules and to leverage the full potential of the collected data.
{
"message": "{\"campaignId\":\"46e01b8a-c899-404d-bcd9-189bb393d1a7\",\"classification\":\"MALWARE\",\"clickIP\":\"192.0.2.1\",\"clickTime\":\"2016-06-24T19:17:44.000Z\",\"GUID\":\"b27dbea0-87d5-463b-b93c-4e8b708289ce\",\"id\":\"8c8b4895-a277-449f-r797-547e3c89b25a\",\"messageID\":\"8c6cfedd-3050-4d65-8c09-c5f65c38da81\",\"recipient\":\"bruce.wayne@pharmtech.zz\",\"sender\":\"9facbf452def2d7efc5b5c48cdb837fa@badguy.zz\",\"senderIP\":\"192.0.2.255\",\"threatID\":\"61f7622167144dba5e3ae4480eeee78b23d66f7dfed970cfc3d086cc0dabdf50\",\"threatTime\":\"2016-06-24T19:17:46.000Z\",\"threatURL\":\"https://threatinsight.proofpoint.com/#/73aa0499-dfc8-75eb-1de8-a471b24a2e75/threat/u/61f7622167144dba5e3ae4480eeee78b23d66f7dfed970cfc3d086cc0dabdf50\",\"threatStatus\":\"active\",\"url\":\"http://badguy.zz/\",\"userAgent\":\"Mozilla/5.0(WindowsNT6.1;WOW64;rv:27.0)Gecko/20100101Firefox/27.0\",\"type\":\"click\",\"status\":\"permitted\"}\n",
"event": {
"action": "permitted",
"category": [
"network"
],
"dataset": "click",
"type": [
"allowed"
]
},
"@timestamp": "2016-06-24T19:17:44Z",
"email": {
"local_id": "b27dbea0-87d5-463b-b93c-4e8b708289ce",
"message_id": "8c6cfedd-3050-4d65-8c09-c5f65c38da81",
"sender": {
"address": [
"9facbf452def2d7efc5b5c48cdb837fa@badguy.zz"
]
},
"to": {
"address": [
"bruce.wayne@pharmtech.zz"
]
}
},
"observer": {
"product": "Targeted Attack Protection",
"vendor": "ProofPoint"
},
"proofpoint": {
"tap": {
"threat": {
"classifications": [
"malware"
]
}
}
},
"related": {
"ip": [
"192.0.2.255"
]
},
"source": {
"address": "192.0.2.255",
"ip": "192.0.2.255"
},
"threat": {
"enrichments": [
{
"indicator": {
"first_seen": "2016-06-24T19:17:46.000Z",
"last_seen": "2016-06-24T19:17:46.000Z",
"reference": "https://threatinsight.proofpoint.com/#/73aa0499-dfc8-75eb-1de8-a471b24a2e75/threat/u/61f7622167144dba5e3ae4480eeee78b23d66f7dfed970cfc3d086cc0dabdf50",
"type": "domain-name",
"url": {
"original": "http://badguy.zz/"
}
}
}
]
},
"url": {
"domain": "badguy.zz",
"original": "http://badguy.zz/",
"path": "/",
"port": 80,
"scheme": "http",
"subdomain": "badguy"
},
"user_agent": {
"device": {
"name": "Other"
},
"name": "Firefox",
"original": "Mozilla/5.0(WindowsNT6.1;WOW64;rv:27.0)Gecko/20100101Firefox/27.0",
"os": {
"name": "Windows",
"version": "NT"
},
"version": "27.0"
}
}
{
"message": "{\"GUID\":\"c26dbea0-80d5-463b-b93c-4e8b708219ce\",\"status\":\"delivered\",\"type\":\"message\",\"QID\":\"r2FNwRHF004109\",\"ccAddresses\":[\"bruce.wayne@university-of-education.zz\"],\"clusterId\":\"pharmtech_hosted\",\"completelyRewritten\":\"true\",\"fromAddress\":[\"badguy@evil.zz\"],\"headerCC\":\"\\\"Bruce Wayne\\\" <bruce.wayne@university-of-education.zz>\",\"headerFrom\":\"\\\"A. Badguy\\\" <badguy@evil.zz>\",\"headerReplyTo\":null,\"headerTo\":\"\\\"Clark Kent\\\" <clark.kent@pharmtech.zz>; \\\"Diana Prince\\\" <diana.prince@pharmtech.zz>\",\"impostorScore\":0,\"malwareScore\":100,\"messageID\":\"20160624211145.62086.mail@evil.zz\",\"messageParts\":[{\"contentType\":\"text/plain\",\"disposition\":\"inline\",\"filename\":\"text.txt\",\"md5\":\"008c5926ca861023c1d2a36653fd88e2\",\"oContentType\":\"text/plain\",\"sandboxStatus\":\"unsupported\",\"sha256\":\"85738f8f9a7f1b04b5329c590ebcb9e425925c6d0984089c43a022de4f19c281\"},{\"contentType\":\"application/pdf\",\"disposition\":\"attached\",\"filename\":\"Invoice for Pharmtech.pdf\",\"md5\":\"5873c7d37608e0d49bcaa6f32b6c731f\",\"oContentType\":\"application/pdf\",\"sandboxStatus\":\"threat\",\"sha256\":\"2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca\"}],\"messageTime\":\"2016-06-24T21:18:38.000Z\",\"modulesRun\":[\"pdr\",\"sandbox\",\"spam\",\"urldefense\"],\"phishScore\":46,\"policyRoutes\":[\"default_inbound\",\"executives\"],\"quarantineFolder\":\"Attachment Defense\",\"quarantineRule\":\"module.sandbox.threat\",\"recipient\":[\"clark.kent@pharmtech.zz\",\"diana.prince@pharmtech.zz\"],\"replyToAddress\":null,\"sender\":\"e99d7ed5580193f36a51f597bc2c0210@evil.zz\",\"senderIP\":\"192.0.2.255\",\"spamScore\":4,\"subject\":\"Please find a totally safe invoice attached.\",\"threatsInfoMap\":[{\"campaignId\":\"46e01b8a-c899-404d-bcd9-189bb393d1a7\",\"classification\":\"MALWARE\",\"threat\":\"2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca\",\"threatId\":\"2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca\",\"threatStatus\":\"active\",\"threatTime\":\"2016-06-24T21:18:38.000Z\",\"threatType\":\"ATTACHMENT\",\"threatUrl\":\"https://threatinsight.proofpoint.com/#/73aa0499-dfc8-75eb-1de8-a471b24a2e75/threat/u/2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca\"},{\"campaignId\":\"46e01b8a-c899-404d-bcd9-189bb393d1a7\",\"classification\":\"MALWARE\",\"threat\":\"badsite.zz\",\"threatId\":\"3ba97fc852c66a7ba761450edfdfb9f4ffab74715b591294f78b5e37a76481aa\",\"threatTime\":\"2016-06-24T21:18:07.000Z\",\"threatType\":\"url\",\"threatUrl\":\"https://threatinsight.proofpoint.com/#/73aa0499-dfc8-75eb-1de8-a471b24a2e75/threat/u/3ba97fc852c66a7ba761450edfdfb9f4ffab74715b591294f78b5e37a76481aa\"}],\"toAddresses\":[\"clark.kent@pharmtech.zz\",\"diana.prince@pharmtech.zz\"],\"xmailer\":\"Spambot v2.5\"}",
"event": {
"action": "delivered",
"category": [
"email"
],
"dataset": "message",
"type": [
"info"
]
},
"@timestamp": "2016-06-24T21:18:38Z",
"email": {
"attachments": [
{
"file": {
"hash": {
"md5": "008c5926ca861023c1d2a36653fd88e2",
"sha256": "85738f8f9a7f1b04b5329c590ebcb9e425925c6d0984089c43a022de4f19c281"
},
"mime_type": "text/plain",
"name": "text.txt"
}
},
{
"file": {
"hash": {
"md5": "5873c7d37608e0d49bcaa6f32b6c731f",
"sha256": "2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca"
},
"mime_type": "application/pdf",
"name": "Invoice for Pharmtech.pdf"
}
}
],
"cc": {
"address": [
"bruce.wayne@university-of-education.zz"
]
},
"from": {
"address": [
"badguy@evil.zz"
]
},
"local_id": "c26dbea0-80d5-463b-b93c-4e8b708219ce",
"message_id": "20160624211145.62086.mail@evil.zz",
"sender": {
"address": [
"e99d7ed5580193f36a51f597bc2c0210@evil.zz"
]
},
"subject": "Please find a totally safe invoice attached.",
"to": {
"address": [
"clark.kent@pharmtech.zz",
"diana.prince@pharmtech.zz"
]
},
"x_mailer": "Spambot v2.5"
},
"observer": {
"product": "Targeted Attack Protection",
"vendor": "ProofPoint"
},
"proofpoint": {
"tap": {
"cluster": {
"id": "pharmtech_hosted"
},
"completely_rewritten": true,
"email": {
"to": {
"address": [
"clark.kent@pharmtech.zz",
"diana.prince@pharmtech.zz"
]
}
},
"modules": [
"pdr",
"sandbox",
"spam",
"urldefense"
],
"threat": {
"classifications": [
"malware"
],
"scores": {
"impostor": 0,
"malware": 100,
"phish": 46,
"spam": 4
}
}
}
},
"related": {
"ip": [
"192.0.2.255"
]
},
"rule": {
"name": "module.sandbox.threat"
},
"source": {
"address": "192.0.2.255",
"ip": "192.0.2.255"
},
"threat": {
"enrichments": [
{
"indicator": {
"file": {
"hash": {
"sha256": "2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca"
}
},
"first_seen": "2016-06-24T21:18:38.000Z",
"last_seen": "2016-06-24T21:18:38.000Z",
"reference": "https://threatinsight.proofpoint.com/#/73aa0499-dfc8-75eb-1de8-a471b24a2e75/threat/u/2fab740f143fc1aa4c1cd0146d334c5593b1428f6d062b2c406e5efe8abe95ca",
"type": "file"
}
},
{
"indicator": {
"first_seen": "2016-06-24T21:18:07.000Z",
"last_seen": "2016-06-24T21:18:07.000Z",
"reference": "https://threatinsight.proofpoint.com/#/73aa0499-dfc8-75eb-1de8-a471b24a2e75/threat/u/3ba97fc852c66a7ba761450edfdfb9f4ffab74715b591294f78b5e37a76481aa",
"type": "domain-name",
"url": {
"domain": "badsite.zz",
"original": "badsite.zz"
}
}
}
]
},
"url": {
"original": "badsite.zz",
"path": "badsite.zz"
}
}
Extracted Fields
The following table lists the fields that are extracted, normalized under the ECS format, analyzed and indexed by the parser. It should be noted that infered fields are not listed.
| Name | Type | Description |
|---|---|---|
@timestamp |
date |
Date/time when the event originated. |
email.attachments |
array |
A list of objects describing the attachment files sent along with an email message |
email.cc.address |
keyword |
The email address of CC recipient |
email.from.address |
keyword |
The email address of the sender, typically from the RFC 5322 From: header field |
email.local_id |
keyword |
Unique identifier given to the email by the source that created the event |
email.message_id |
keyword |
Identifier from the RFC 5322 Message-ID: email header that refers to a particular email message |
email.reply_to.address |
keyword |
The address that replies should be delivered to based on the value in the RFC 5322 Reply-To: header |
email.sender.address |
keyword |
Per RFC 5322, specifies the address responsible for the actual transmission of the message |
email.subject |
keyword |
A brief summary of the topic of the message |
email.to.address |
keyword |
The email address of recipient |
email.x_mailer |
keyword |
The mailer that send the message |
event.action |
keyword |
The action captured by the event. |
event.category |
keyword |
Event category. The second categorization field in the hierarchy. |
event.dataset |
keyword |
Name of the dataset. |
event.type |
keyword |
Event type. The third categorization field in the hierarchy. |
observer.product |
keyword |
The product name of the observer. |
observer.vendor |
keyword |
Vendor name of the observer. |
proofpoint.tap.cluster.id |
keyword |
The name of the cluster which processed the message |
proofpoint.tap.completely_rewritten |
boolean |
Falg if the message was rewritten |
proofpoint.tap.email.to.address |
array |
The list of recipients from the TO header |
proofpoint.tap.modules |
array |
The list of modules which processed the message |
proofpoint.tap.threat.classifications |
array |
The list of classifications of the threat |
proofpoint.tap.threat.scores.impostor |
number |
The impostor score of the message |
proofpoint.tap.threat.scores.malware |
number |
The malware score of the message |
proofpoint.tap.threat.scores.phish |
number |
The phish score of the message |
proofpoint.tap.threat.scores.spam |
number |
The spam score of the message |
rule.name |
keyword |
Rule name |
source.ip |
ip |
IP address of the source. |
threat.enrichments |
array |
Threat indicators associated to the message |
url.original |
wildcard |
Unmodified original url as seen in the event source. |
user_agent.original |
keyword |
Unparsed user_agent string. |
For more information on the Intake Format, please find the code of the Parser, Smart Descriptions, and Supported Events here.