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Arnold Chan

@slaz
Trusted contributorCompletionist
2 followers2,100 downloads4,387 copies51 likes26,185 views

7,555 detections

Detects network, HTTP, and DNS activity associated with the known three-tier delivery infrastructure (phishing sites, relay/dispatcher servers, and payload hosts) used by the threat actor UNC6671/SilverFox/Aurora. The rule distinguishes between high-confidence confirmed connections and low-confidence DNS-only events.
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Arnold Chan@slaz
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Hunters
2 months ago
203
Detects network, HTTP, and DNS activity associated with the known three-tier delivery infrastructure (phishing sites, relay/dispatcher servers, and payload hosts) used by the threat actor UNC6671/SilverFox/Aurora. The rule distinguishes between high-confidence confirmed connections and low-confidence DNS-only events.
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Arnold Chan@slaz
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Midnight Slayer
2 months ago
103
Detects network, HTTP, and DNS activity associated with the known three-tier delivery infrastructure (phishing sites, relay/dispatcher servers, and payload hosts) used by the threat actor UNC6671/SilverFox/Aurora. The rule distinguishes between high-confidence confirmed connections and low-confidence DNS-only events.
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Arnold Chan@slaz
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Detection & Hunting Community
2 months ago
203
Detects the execution of known malicious files associated with the BlueDelta threat group, specifically the HOOKEDGE and HEADLACE malware families, using a curated list of SHA-256 file hashes. Additionally, the rule monitors for the execution of scripts with GUID-based filenames, a common tactic for temporal or staged file deployment.
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Arnold Chan@slaz
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Detection & Hunting Community
2 months ago
004
Matches known HOOKEDGE malware samples attributed to BlueDelta (APT28) via SHA256 hash comparison
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Arnold Chan@slaz
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Midnight Slayer
2 months ago
004
Matches known HOOKEDGE malware samples attributed to BlueDelta (APT28) via SHA256 hash comparison
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Arnold Chan@slaz
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Detection & Hunting Community
2 months ago
004
Detects network activity and HTTP traffic associated with the Iron Man System kit, specifically targeting hardcoded C2 infrastructure (IP-based) and lure domain patterns involving specific brand tokens combined with disposable TLDs.
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Arnold Chan@slaz
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Detections.ai Community
2 months ago
003
Detects high-volume credential guessing attacks against applications, characterized by a large number of distinct source IP addresses and geographic locations. This pattern is indicative of attackers using residential proxy pools to distribute their attempts, effectively bypassing traditional rate limiting or per-IP WAF thresholds while targeting authentication or error oracles. The rule specifically filters for relevant failure codes and enforces constraints on IP/user diversity to reduce noise from single-point-of-failure events or legitimate infrastructure.
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Arnold Chan@slaz
avatar
Midnight Slayer
2 months ago
003
Detects high-volume credential guessing attacks against applications, characterized by a large number of distinct source IP addresses and geographic locations. This pattern is indicative of attackers using residential proxy pools to distribute their attempts, effectively bypassing traditional rate limiting or per-IP WAF thresholds while targeting authentication or error oracles. The rule specifically filters for relevant failure codes and enforces constraints on IP/user diversity to reduce noise from single-point-of-failure events or legitimate infrastructure.
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Arnold Chan@slaz
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Detection & Hunting Community
2 months ago
003
Detects rapid, sequential identity-validation API calls (such as GetCallerIdentity, GetSessionToken, or AssumeRole) performed across a large number of distinct AWS access keys from a single source within a short time window. This pattern is indicative of automated tooling validating a harvested corpus of AWS access keys to identify active credentials.
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Arnold Chan@slaz
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Hunters
2 months ago
103
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