Advance Fee Fraud (AFF) from freemail provider or suspicious TLD

 1name: "Advance Fee Fraud (AFF) from freemail provider or suspicious TLD"
 2description: |
 3  Advance Fee Fraud (AFF) is a type of BEC/Fraud involving upfront fees for promised
 4  future returns, such as lottery scams, inheritance payouts, and investment opportunities.
 5  This rule identifies messages from Freemail domains or suspicious TLDS, including those
 6  with suspicious reply-to addresses. It utilizes Natural Language Understanding to detect
 7  AFF language in their contents.  
 8type: "rule"
 9severity: "medium"
10source: |
11  type.inbound
12  and (
13    sender.email.domain.domain in $free_email_providers
14    or (
15      length(headers.reply_to) > 0
16      and all(headers.reply_to,
17              (
18                .email.domain.root_domain in $free_email_providers
19                or .email.domain.tld in $suspicious_tlds
20                or (
21                  network.whois(.email.domain).days_old < 365
22                  and length(coalesce(body.html.raw, "")) == 0
23                )
24              )
25              and .email.email != sender.email.email
26      )
27    )
28    or sender.email.domain.tld in $suspicious_tlds
29    or strings.iends_with(sender.email.domain.tld, ".jp")
30  )
31  and (
32    any(ml.nlu_classifier(body.current_thread.text).intents,
33        .name == "advance_fee" and .confidence in ("medium", "high")
34    )
35    or (
36      length(body.current_thread.text) < 200
37      and regex.icontains(body.current_thread.text,
38                          '(?:donation|inheritence|\$\d,\d{3}\,\d{3}|lottery)'
39      )
40      and not regex.icontains(body.current_thread.text,
41                              '(?:closed.{0,50})?\$\d,\d{3}\,\d{3}.{0,100}(?:homes|realty|sale)?'
42      )
43      and not any(body.links,
44                  regex.icontains(.href_url.url,
45                                  '(?:donation|inheritence|\$\d,\d{3}\,\d{3}|lottery)'
46                  )
47      )
48      and (
49        (
50          (
51            length(headers.references) > 0
52            or not any(headers.hops,
53                       any(.fields, strings.ilike(.name, "In-Reply-To"))
54            )
55          )
56          and not (
57            (
58              strings.istarts_with(subject.subject, "RE:")
59              // out of office auto-reply
60              or strings.istarts_with(subject.subject, "Automatic reply:")
61              or strings.istarts_with(subject.subject, "R:")
62              or strings.istarts_with(subject.subject, "ODG:")
63              or strings.istarts_with(subject.subject, "答复:")
64              or strings.istarts_with(subject.subject, "AW:")
65              or strings.istarts_with(subject.subject, "TR:")
66              or strings.istarts_with(subject.subject, "FWD:")
67              or regex.icontains(subject.subject,
68                                 '^(\[[^\]]+\]\s?){0,3}(re|fwd?)\s?:'
69              )
70            )
71          )
72        )
73        or any(headers.reply_to, .email.email != sender.email.email)
74      )
75    )
76  )
77  and (
78    not profile.by_sender().solicited
79    or profile.by_sender().any_messages_malicious_or_spam
80  )
81  and not profile.by_sender().any_messages_benign  
82attack_types:
83  - "BEC/Fraud"
84tactics_and_techniques:
85  - "Social engineering"
86detection_methods:
87  - "Content analysis"
88  - "Header analysis"
89  - "Natural Language Understanding"
90  - "Sender analysis"
91id: "6a5af373-a97b-5013-aeec-42ac8b4b8ba1"