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Arnold Chan

@slaz
Trusted contributorCompletionist
2 followers2,099 downloads4,360 copies51 likes25,998 views

7,555 detections

Detects unauthorized or suspicious use of USSD codes by the corporate MDM agent to modify call-forwarding settings. This activity is indicative of a compromise where an attacker uses an MDM-controlled device to intercept calls or divert communications.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
003
This rule correlates multiple telemetry sources (File Events, Certificate Info, Network Events, and DNS Events) to detect known malicious indicators, including specific file hashes, IP addresses, domains, and URLs associated with malicious activity.
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Arnold Chan@slaz
Defender - KQL
1 month ago
003
Detects outbound HTTP requests from endpoints to a known C2 infrastructure associated with mobile device spyware. The rule identifies two specific stages: polling for new commands using a GET request and posting command execution results using a POST request, both targeting an MDM-mimicking API structure.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
003
This rule monitors for outbound TCP and HTTP traffic to a known malicious infrastructure hosting a fake or compromised 'Corp MDM' (Mobile Device Management) admin panel, which serves as a Command and Control (C2) channel for spyware.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
003
Detects an Android device registration attempt to a known malicious C2 IP address using parameters characteristic of a mobile device management (MDM) implant. The rule looks for a POST request to a specific registration endpoint containing device identifiers like deviceId, manufacturer, model, and osVersion.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
003
This rule detects potential malicious activity by matching file hashes against a known list of malicious indicators and monitoring for suspicious network connections. The network monitoring includes connections to hardcoded malicious IP addresses, specific C2 URLs, and DNS queries for known fallback domains used in malicious infrastructure when the initiating process is not a recognized web browser or wallet application.
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Arnold Chan@slaz
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Hunters
1 month ago
003
This rule detects potential malicious activity by matching file hashes against a known list of malicious indicators and monitoring for suspicious network connections. The network monitoring includes connections to hardcoded malicious IP addresses, specific C2 URLs, and DNS queries for known fallback domains used in malicious infrastructure when the initiating process is not a recognized web browser or wallet application.
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Arnold Chan@slaz
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Detection & Hunting Community
1 month ago
003
Detects the loading of the 'WbElevation.dll' module associated with SectopRAT, followed by suspicious access to browser, email, or cryptocurrency wallet credential stores within a 10-minute window on the same device.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
003
This rule detects potential SectopRAT loader activity by identifying non-.NET-native processes accessing 'pool.db' in the ProgramData directory followed by the immediate loading of .NET CLR libraries (clr.dll or mscoreei.dll). This pattern is indicative of reflective loading of a decrypted .NET payload into memory within a target process.
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Arnold Chan@slaz
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Detections.ai Community
1 month ago
003
Detects a specific execution sequence associated with the SectopRAT loader. The rule identifies a process named 'ReportDump.exe' reading a payload file 'Activation.Desktop.db' and subsequently loading 'stp_aim_x64_vc15.dll', which is known to trigger malicious shellcode via an exported function.
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Arnold Chan@slaz
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Detection & Hunting Community
1 month ago
003
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