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在规则中使用经过丰富上下文的数据

支持的平台：

Google SecOps SIEM

为了帮助安全分析师在调查期间顺利开展工作，Google Security Operations 会提取来自不同来源的上下文数据，对提取的数据进行分析，并提供有关客户环境中工件的实用背景信息。本文档提供了一些示例，说明分析师如何在检测引擎规则中使用经过情境丰富的数据。

如需详细了解数据丰富功能，请参阅 Google SecOps 如何丰富事件和实体数据。

在规则中使用“患病率”经过丰富的字段

以下示例演示了如何在检测引擎中使用与流行病学相关的丰富字段。如需参考，请参阅与患病率相关的丰富字段列表。

识别低普及率网域访问

当检测到匹配项时，此检测规则会生成检测事件，而不是检测提醒。它主要用作调查素材资源时的辅助指标。例如，还有其他严重程度更高的提醒触发了突发事件。

$enrichment.graph.metadata.entity_type = "FILE"
$enrichment.graph.metadata.product_name = "VirusTotal Relationships"
$enrichment.graph.metadata.vendor_name = "VirusTotal"

如需详细了解如何添加过滤条件以提升规则性能，请参阅添加事件类型过滤条件。

如需了解每种类型的丰富功能，请参阅 Google SecOps 如何丰富事件和实体数据。

在规则中使用“患病率”经过丰富的字段

以下示例演示了如何在检测引擎中使用与流行病学相关的丰富字段。如需参考，请参阅与患病率相关的丰富字段列表。

识别对普及率得分较低的网域的访问

此规则可用于检测对普及率得分较低的网域的访问。为确保有效，必须存在工件基准普遍性得分。以下示例使用参考列表来调整结果，并应用阈值患病率值。

rule network_prevalence_low_prevalence_domain_access {
  meta:
    author = "Google Security Operations"
    description = "Detects access to a low prevalence domain. Requires baseline of prevalence be in place for effective deployment."
    severity = "LOW"

  events:
        $e.metadata.event_type = "NETWORK_HTTP"
        $e.principal.ip = $ip

        // filter out URLs with RFC 1918 IP addresses, internal assets
        not re.regex($e.target.hostname, `(127(?:\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)){3}$)|(10(?:\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)){3}$)|(192\.168(?:\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)){2}$)|(172\.(?:1[6-9]|2\d|3[0-1])(?:\.(25[0-5]|2[0-4][0-9]|[01]?[0-9][0-9]?)){2})`)

        // used an explicit exclusion reference list
        not $e.target.hostname in %exclusion_network_prevalence_low_prevalence_domain_access

        // only match valid FQDN, filter out background non-routable noise
        re.regex($e.target.hostname, `(?:[a-z0-9](?:[a-z0-9-]{0,61}[a-z0-9])?\.)+[a-z0-9][a-z0-9-]{0,61}[a-z0-9]`)

        $domainName = $e.target.hostname

        //join event ($e) to entity graph ($d)
        $e.target.hostname = $d.graph.entity.domain.name

        $d.graph.metadata.entity_type = "DOMAIN_NAME"

        // tune prevalence as fits your results
        $d.graph.entity.domain.prevalence.rolling_max > 0
        $d.graph.entity.domain.prevalence.rolling_max <= 10

  match:
        $ip over 1h

  outcome:
    $risk_score = max(
        // increment risk score based upon rolling_max prevalence
        if ( $d.graph.entity.domain.prevalence.rolling_max >= 10, 10) +
        if ( $d.graph.entity.domain.prevalence.rolling_max >= 2 and $d.graph.entity.domain.prevalence.rolling_max <= 9 , 20) +
        if ( $d.graph.entity.domain.prevalence.rolling_max = 1, 30)
    )

    $domain_list = array_distinct($domainName)
    $domain_count = count_distinct($domainName)

  condition:
    $e and #d > 10
}

识别存在 IOC 匹配项的低普及率网域

此检测规则会生成检测提醒，并通过与已知的 IOC 进行比较，提供高保真度匹配。

rule network_prevalence_uncommon_domain_ioc_match {

  meta:
    author = "Google Security Operations"
    description = "Lookup Network DNS queries against Entity Graph for low prevalence domains with a matching IOC entry."
    severity = "MEDIUM"

  events:
    $e.metadata.event_type = "NETWORK_DNS"
    $e.network.dns.questions.name = $hostname

    //only match FQDNs, such as: exclude chrome dns access tests and other internal hosts
    $e.network.dns.questions.name = /(?:[a-z0-9](?:[a-z0-9-]{0,61}[a-z0-9])?\.)+[a-z0-9][a-z0-9-]{0,61}[a-z0-9]/

    //prevalence entity graph lookup
    $p.graph.metadata.entity_type = "DOMAIN_NAME"
    $p.graph.entity.domain.prevalence.rolling_max > 0
    $p.graph.entity.domain.prevalence.rolling_max <= 3
    $p.graph.entity.domain.name = $hostname

    //ioc entity graph lookup
    $i.graph.metadata.vendor_name = "ET_PRO_IOC"
    $i.graph.metadata.entity_type = "DOMAIN_NAME"
    $i.graph.entity.hostname = $hostname

  match:
    $hostname over 10m

  outcome:
    $risk_score = max(
        //increment risk score based upon rolling_max prevalence
        if ( $p.graph.entity.domain.prevalence.rolling_max = 3, 50) +
        if ( $p.graph.entity.domain.prevalence.rolling_max = 2, 70) +
        if ( $p.graph.entity.domain.prevalence.rolling_max = 1, 90)
    )

  condition:
    $e and $p and $i
}

在规则中使用实体的首次看到时间

您可以编写包含实体记录中的 first_seen_time 或 last_seen_time 字段的规则。

first_seen_time 和 last_seen_time 字段会填充描述网域、IP 地址和文件（哈希）的实体。对于用于描述用户或素材资源的实体，系统只会填充 first_seen_time 字段。系统不会针对描述其他类型（例如群组或资源）的实体计算这些值。

如需查看填充的 UDM 字段列表，请参阅计算实体的首次看到时间和上次看到时间。

以下示例展示了如何在规则中使用 first_seen_time：

rule first_seen_data_exfil {
    meta:
        author = "Google Security Operations"
        description = "Example usage first_seen data"
        severity = "LOW"

    events:
        $first_access.metadata.event_type = "NETWORK_HTTP"
        $ip = $first_access.principal.ip

        // Join first_access event with entity graph to use first/last seen data.
        $ip = $first_last_seen.graph.entity.ip
        $first_last_seen.graph.metadata.entity_type = "IP_ADDRESS"

        // Check that the first_access UDM event is the first_seen occurrence in the enterprise.
        $first_last_seen.graph.entity.artifact.first_seen_time.seconds = $first_access.metadata.event_timestamp.seconds
        $first_last_seen.graph.entity.artifact.first_seen_time.nanos   = $first_access.metadata.event_timestamp.nanos

        // Check for another access event that appears shortly after the first_seen event,
        // where lots of data is being sent.
        $next_access_data_exfil.metadata.event_type = "NETWORK_CONNECTION"
        // Next access event goes to the same IP as the first.
        $next_access_data_exfil.principal.ip = $ip

        // Next access occurs within 60 seconds after first access.
        $next_access_data_exfil.metadata.event_timestamp.seconds > $first_access.metadata.event_timestamp.seconds
        60 > $next_access_data_exfil.metadata.event_timestamp.seconds  - $first_access.metadata.event_timestamp.seconds

        // Lots of data is being sent over the next access event.
        $next_access_data_exfil.network.sent_bytes > 10 * 1024 * 1024 * 1024 // 10GB

        // Extract hostname of next access event, for match section.
        $hostname = $next_access_data_exfil.principal.hostname

    match:
        $hostname over 1h

    condition:
        $first_access and $next_access_data_exfil and $first_last_seen
}

在规则中使用经过地理位置丰富的字段

存储经过地理位置丰富的数据的 UDM 字段可在 Detection Engine 规则中使用。如需查看填充的 UDM 字段列表，请参阅使用地理位置数据丰富事件。

以下示例展示了如何检测用户实体是否在多个不同的状态下进行身份验证。

rule geoip_user_login_multiple_states_within_1d {

  meta:
    author = "Google Security Operations"
    description = "Detect multiple authentication attempts from multiple distinct locations using geolocation-enriched UDM fields."
    severity = "INFORMATIONAL"

  events:
    $geoip.metadata.event_type = "USER_LOGIN"
    (
      $geoip.metadata.vendor_name = "Google Workspace" or
      $geoip.metadata.vendor_name = "Google Cloud Platform"
    )
    /* optionally, detect distinct locations at a country */
    (
      $geoip.principal.ip_geo_artifact.location.country_or_region != "" and
      $geoip.principal.ip_geo_artifact.location.country_or_region = $country
    )
    (
      $geoip.principal.ip_geo_artifact.location.state != "" and
      $geoip.principal.ip_geo_artifact.location.state = $state
    )

    $geoip.target.user.email_addresses = $user

  match:
    $user over 1d

  condition:
    $geoip and #state > 1
}

在规则中使用“安全浏览”增强字段

Google SecOps 会从与文件哈希相关的威胁列表中提取数据。这些经过丰富的信息会以实体形式存储在 Google SecOps 中。

如需查看填充的 UDM 字段列表，请参阅使用安全浏览威胁列表中的信息丰富实体。

您可以创建检测引擎规则，以识别与从安全浏览提取的实体匹配的内容。以下是检测引擎规则示例，用于对这些丰富的信息进行查询，以构建情境感知分析。

rule safe_browsing_file_execution {
    meta:
        author = "Google Security Operations"
        description = "Example usage of Safe Browsing data, to detect execution of a file that's been deemed malicious"
        severity = "LOW"

    events:
        // find a process launch event, match on hostname
        $execution.metadata.event_type = "PROCESS_LAUNCH"
        $execution.principal.hostname = $hostname

        // join execution event with Safe Browsing graph
        $sb.graph.entity.file.sha256 = $execution.target.process.file.sha256

        // look for files deemed malicious
        $sb.graph.metadata.entity_type = "FILE"
        $sb.graph.metadata.threat.severity = "CRITICAL"
        $sb.graph.metadata.product_name = "Google Safe Browsing"
        $sb.graph.metadata.source_type = "GLOBAL_CONTEXT"

    match:
        $hostname over 1h

    condition:
        $execution and $sb
}

在规则中使用 WHOIS 丰富字段

您可以编写规则，在代表网域的实体中搜索 WHOIS 丰富字段。这些实体的 entity.metadata.entity_type 字段设置为 DOMAIN_NAME。如需查看填充的 UDM 字段列表，请参阅使用 WHOIS 数据丰富实体。

以下示例规则展示了如何执行此操作。此规则在 events 部分包含以下过滤器字段，以帮助优化规则的性能。

$whois.graph.metadata.entity_type = "DOMAIN_NAME"
$whois.graph.metadata.product_name = "WHOISXMLAPI Simple Whois"
$whois.graph.metadata.vendor_name = "WHOIS"

rule whois_expired_domain_executable_download {
 meta:
   author = "Google Security Operations"
   description = "Example usage of WHOIS data, detecting an executable file download from a domain that's recently expired"
   severity = "LOW"

 events:
        $access.metadata.event_type = "NETWORK_HTTP"
        $hostname = $access.principal.hostname

        // join access event to entity graph to use WHOIS data
        $whois.graph.entity.domain.name = $access.target.hostname

        // use WHOIS data to look for expired domains
        $whois.graph.metadata.entity_type = "DOMAIN_NAME"
        $whois.graph.metadata.product_name = "WHOISXMLAPI Simple Whois"
        $whois.graph.metadata.vendor_name = "WHOIS"
        $whois.graph.entity.domain.expiration_time.seconds < $access.metadata.event_timestamp.seconds

        // join access event with executable file creation event by principal hostname
        $creation.principal.hostname = $access.principal.hostname
        $creation.metadata.event_type = "FILE_CREATION"
        $creation.target.file.full_path = /exe/ nocase

        // file creation comes after expired domain access
        $creation.metadata.event_timestamp.seconds >
           $access.metadata.event_timestamp.seconds

   match:
       $hostname over 1h

 condition:
        $access and $whois and $creation
}

查询 Google Cloud 威胁情报数据

Google SecOps 会从 Google Cloud 威胁情报 (GCTI) 数据源提取数据，为您提供环境中活动的背景信息，以便您在调查这些活动时使用。您可以查询以下数据源：

GCTI Tor 退出节点
GCTI 良性二进制文件
GCTI 远程访问工具

如需了解这些威胁 Feed 以及填充的所有字段，请参阅提取和存储 Google Cloud 威胁情报数据。

在本文档中，占位符 <variable_name> 表示规则中用于标识 UDM 记录的唯一变量名称。

查询 Tor 退出节点 IP 地址

以下示例规则会在 NETWORK_CONNECTION 事件包含存储在 target.ip 字段中的 IP 地址（该 IP 地址也存在于 GCTI Tor Exit Nodes 数据源中）时返回检测结果。请务必在规则中添加 <variable_name>.graph.metadata.threat.threat_feed_name、<variable_name>.graph.metadata.vendor_name 和 <variable_name>.graph.metadata.product_name 字段。

这是基于时间的数据源。事件将与数据源在该时间点的快照相匹配。

rule gcti_tor_exit_nodes {
  meta:
    author = "Google Cloud Threat Intelligence"
    description = "Alert on known Tor exit nodes."
    severity = "High"

  events:
    // Event
    $e.metadata.event_type = "NETWORK_CONNECTION"
    $e.target.ip = $tor_ip

    // Tor IP search in GCTI Feed
    $tor.graph.entity.artifact.ip = $tor_ip
    $tor.graph.metadata.entity_type = "IP_ADDRESS"
    $tor.graph.metadata.threat.threat_feed_name = "Tor Exit Nodes"
    $tor.graph.metadata.source_type = "GLOBAL_CONTEXT"
    $tor.graph.metadata.vendor_name = "Google Cloud Threat Intelligence"
    $tor.graph.metadata.product_name = "GCTI Feed"

  match:
    $tor_ip over 1h

  outcome:
    $tor_ips = array_distinct($tor_ip)
    $tor_geoip_country = array_distinct($e.target.ip_geo_artifact.location.country_or_region)
    $tor_geoip_state = array_distinct($e.target.ip_geo_artifact.location.state)

  condition:
    $e and $tor
}

查询良性操作系统文件

以下示例规则会组合使用 Benign Binaries 和 Tor Exit Nodes 数据源，以便在良性二进制文件与 Tor 出口节点联系时返回提醒。该规则会使用 Google SecOps 使用目标 IP 地址丰富的地理位置数据计算风险得分。请务必在规则中为 Benign Binaries 和 Tor Exit Nodes 数据源添加 <variable_name>.graph.metadata.vendor_name、<variable_name>.graph.metadata.product_name 和 <variable_name>.graph.metadata.threat.threat_feed_name。

这是永不过时的数据源。无论时间如何，事件始终与数据源的最新快照匹配。

rule gcti_benign_binaries_contacts_tor_exit_node {
 meta:
   author = "Google Cloud Threat Intelligence"
   description = "Alert on Benign Binary contacting a Tor IP address."
   severity = "High"

 events:
   // Event
   $e.metadata.event_type = "NETWORK_CONNECTION"
   $e.principal.process.file.sha256 = $benign_hash
   $e.target.ip = $ip
   $e.principal.hostname = $hostname

   // Benign File search in GCTI Feed
   $benign.graph.entity.file.sha256 = $benign_hash
   $benign.graph.metadata.entity_type = "FILE"
   $benign.graph.metadata.threat.threat_feed_name = "Benign Binaries"
   $benign.graph.metadata.source_type = "GLOBAL_CONTEXT"
   $benign.graph.metadata.vendor_name = "Google Cloud Threat Intelligence"
   $benign.graph.metadata.product_name = "GCTI Feed"

   // Tor IP search in GCTI Feed
   $tor.graph.entity.artifact.ip = $ip
   $tor.graph.metadata.entity_type = "IP_ADDRESS"
   $tor.graph.metadata.threat.threat_feed_name = "Tor Exit Nodes"
   $tor.graph.metadata.source_type = "GLOBAL_CONTEXT"
   $tor.graph.metadata.vendor_name = "Google Cloud Threat Intelligence"
   $tor.graph.metadata.product_name = "GCTI Feed"

 match:
   $hostname over 1h

 outcome:
   $risk_score = max(
       if($tor.graph.metadata.threat.confidence = "HIGH_CONFIDENCE", 70) +
       // Unauthorized target geographies
       if($e.target.ip_geo_artifact.location.country_or_region = "Cuba", 20) +
       if($e.target.ip_geo_artifact.location.country_or_region = "Iran", 20) +
       if($e.target.ip_geo_artifact.location.country_or_region = "North Korea", 20) +
       if($e.target.ip_geo_artifact.location.country_or_region = "Russia", 20) +
       if($e.target.ip_geo_artifact.location.country_or_region = "Syria", 20)
   )
   $benign_hashes = array_distinct($benign_hash)
   $benign_files = array_distinct($e.principal.process.file.full_path)
   $tor_ips = array_distinct($ip)
   $tor_geoip_country = array_distinct($e.target.ip_geo_artifact.location.country_or_region)
   $tor_geoip_state = array_distinct($e.target.ip_geo_artifact.location.state)

 condition:
   $e and $benign and $tor
}

查询有关远程访问工具的数据

以下示例规则会在 PROCESS_LAUNCH 事件类型包含在 Google Cloud Threat Intelligence Remote Access Tools 数据源中也找到的哈希时返回检测结果。

这是永不过时的数据源。无论时间如何，事件始终与数据源的最新快照匹配。

rule gcti_remote_access_tools {
 meta:
   author = "Google Cloud Threat Intelligence"
   description = "Alert on Remote Access Tools."
   severity = "High"

 events:
    // find a process launch event
    $e.metadata.event_type = "PROCESS_LAUNCH"
    $e.target.process.file.sha256 != ""
    $rat_hash = $e.target.process.file.sha256

    // join graph and event hashes
    $gcti.graph.entity.file.sha256 = $rat_hash

    // look for files identified as likely remote access tools
    $gcti.graph.metadata.entity_type = "FILE"
    $gcti.graph.metadata.vendor_name = "Google Cloud Threat Intelligence"
    $gcti.graph.metadata.product_name = "GCTI Feed"
    $gcti.graph.metadata.threat.threat_feed_name = "Remote Access Tools"

  match:
    $rat_hash over 5m

 outcome:
   $remote_hash = array_distinct($e.target.process.file.sha256)

  condition:
    $e and $gcti

}

在规则中使用 VirusTotal 丰富的元数据字段

以下规则会检测特定文件类型的文件创建或进程启动，表明系统中存在一些已列入监视名单的哈希。当文件使用 VirusTotal 文件元数据丰富功能标记为 exploit 时，系统会设置风险信号。

如需查看填充的所有 UDM 字段的列表，请参阅使用 VirusTotal 文件元数据丰富事件。

rule vt_filemetadata_hash_match_ioc {
 meta:
   author = "Google Cloud Threat Intelligence"
   description = "Detect file/process events that indicate watchlisted hashes are on a system"
   severity = "High"

 events:
   // Process launch or file creation events
   $process.metadata.event_type = "PROCESS_LAUNCH" or $process.metadata.event_type ="FILE_CREATION"
   $process.principal.hostname = $hostname
   $process.target.file.sha256 != ""
   $process.target.file.sha256 = $sha256
   $process.target.file.file_type = "FILE_TYPE_DOCX"

   // IOC matching
   $ioc.graph.metadata.product_name = "MISP"
   $ioc.graph.metadata.entity_type = "FILE"
   $ioc.graph.metadata.source_type = "ENTITY_CONTEXT"
   $ioc.graph.entity.file.sha256 = $sha256

 match:
   $hostname over 15m

 outcome:
   $risk_score = max(
       // Tag enrichment from VirusTotal file metadata
       if($process.target.file.tags = "exploit", 90)
   )
   $file_sha256 = array($process.target.file.sha256)
   $host = array($process.principal.hostname)

 condition:
   $process and $ioc
}

在规则中使用 VirusTotal 关联数据

Google SecOps 会提取来自 VirusTotal 相关连接的数据。这些数据可提供有关文件哈希与文件、网域、IP 地址和网址之间关系的信息。这些经过丰富的信息会以实体形式存储在 Google SecOps 中。

您可以创建 Detection Engine 规则，以便识别与从 VirusTotal 提取的实体匹配的对象。以下规则会在从与 VirusTotal 有关系的已知 IP 地址下载已知文件哈希时发送提醒。风险信号基于 VirusTotal 文件元数据中的文件类型和标记。

只有部分 VirusTotal 和 Google SecOps 许可才能提供此类数据。请与您的客户经理确认您的使用权。如需查看填充的所有 UDM 字段的列表，请参阅使用 VirusTotal 关系数据丰富实体。

rule virustotal_file_downloaded_from_url {
  meta:
    author = "Google Cloud Threat Intelligence"
    description = "Alerts on downloading a known file hash from a known IP with VirusTotal relationships. The risk score is based on file type and tags from VirusTotal file metadata."
    severity = "High"

  events:
    // Filter network HTTP events
    $e1.metadata.event_type = "NETWORK_HTTP"
    $e1.principal.user.userid = $userid
    $e1.target.url = $url

    // Filter file creation events
    $e2.metadata.event_type = "FILE_CREATION"
    $e2.target.user.userid = $userid
    $e2.target.file.sha256 = $file_hash

    // The file creation event timestamp should be equal or greater than the network http event timestamp
    $e1.metadata.event_timestamp.seconds <= $e2.metadata.event_timestamp.seconds

    // Join event file hash with VirusTotal relationships entity graph
    $vt.graph.metadata.entity_type = "FILE"
    $vt.graph.metadata.source_type = "GLOBAL_CONTEXT"
    $vt.graph.metadata.vendor_name = "VirusTotal"
    $vt.graph.metadata.product_name = "VirusTotal Relationships"
    $vt.graph.entity.file.sha256 = $file_hash

    // Join network HTTP target URL with VirusTotal relationships entity graph
    $vt.graph.relations.entity_type = "URL"
    $vt.graph.relations.relationship = "DOWNLOADED_FROM"
    $vt.graph.relations.entity.url = $url

  match:
    $userid over 1m

  outcome:
      $risk_score = max(
        // Tag enrichment from VirusTotal file metadata
        if($e2.target.file.tags = "via-tor" or $e2.target.file.tags = "malware" or $e2.target.file.tags = "crypto", 50) +
        // File types enrichment from VirusTotal file metadata
        if($e2.target.file.file_type = "FILE_TYPE_HTML", 5) +
        if($e2.target.file.file_type = "FILE_TYPE_ELF", 10) +
        if($e2.target.file.file_type = "FILE_TYPE_PE_DLL",15) +
        if($e2.target.file.file_type = "FILE_TYPE_PE_EXE", 20)
    )

  condition:
    $e1 and $e2 and $vt and $risk_score >= 50
}

最终一致性

依赖于丰富功能的规则需要先处理额外数据，然后才能全面评估规则。随着时间的推移，丰富流程会完成，系统会使用最新的准确数据重新评估规则。这种最终一致性过程是预期的，这意味着虽然最初可能会出现不一致的情况，但系统会确保所有事件最终都会得到充分丰富，并且规则会得到准确的评估。了解 Google SecOps 如何丰富事件和实体数据。

后续步骤

如需了解如何将经过丰富的数据与其他 Google SecOps 功能搭配使用，请参阅以下内容：