ThreatFox: Mirai IOCs

Hunt Hypothesis

This detection identifies potential Mirai botnet infections by monitoring network traffic against four specific Indicators of Compromise (IOCs) known to be utilized by this IoT-targeting malware. A proactive hunt is essential within Azure Sentinel to rapidly isolate compromised devices before they can launch large-scale Distributed Denial of Service (DDoS) attacks or pivot laterally across the enterprise network.

IOC Summary

Malware Family: Mirai Total IOCs: 4 IOC Types: domain, sha256_hash, ip:port, url

KQL: Ip Hunt

// Hunt for network connections to known malicious IPs
// Source: ThreatFox - Mirai
let malicious_ips = dynamic(["95.155.151.113"]);
CommonSecurityLog
| where DestinationIP in (malicious_ips) or SourceIP in (malicious_ips)
| project TimeGenerated, SourceIP, DestinationIP, DestinationPort, DeviceAction, Activity
| order by TimeGenerated desc

KQL: Ip Hunt Device

// Hunt in Defender for Endpoint network events
let malicious_ips = dynamic(["95.155.151.113"]);
DeviceNetworkEvents
| where RemoteIP in (malicious_ips)
| project Timestamp, DeviceName, RemoteIP, RemotePort, InitiatingProcessFileName, ActionType
| order by Timestamp desc

KQL: Domain Hunt

// Hunt for DNS queries to known malicious domains
// Source: ThreatFox - Mirai
let malicious_domains = dynamic(["wqok85qtq.net"]);
DnsEvents
| where Name has_any (malicious_domains)
| project TimeGenerated, Computer, Name, IPAddresses, QueryType
| order by TimeGenerated desc

KQL: Url Hunt

// Hunt for access to known malicious URLs
// Source: ThreatFox - Mirai
let malicious_urls = dynamic(["http://95.155.151.113/d/xd.mips"]);
UrlClickEvents
| where Url has_any (malicious_urls)
| project Timestamp, AccountUpn, Url, ActionType, IsClickedThrough
| order by Timestamp desc

KQL: Hash Hunt

// Hunt for files matching known malicious hashes
// Source: ThreatFox - Mirai
let malicious_hashes = dynamic(["bd216916031c63a5f3174bc27d64babf31294363f1b8274e4ab7d8802c46d91c"]);
DeviceFileEvents
| where SHA256 in (malicious_hashes) or SHA1 in (malicious_hashes) or MD5 in (malicious_hashes)
| project Timestamp, DeviceName, FileName, FolderPath, SHA256, InitiatingProcessFileName
| order by Timestamp desc

Required Data Sources

References

• ThreatFox — Mirai

False Positive Guidance

Here are specific false positive scenarios and corresponding filters for the ThreatFox: Mirai IOCs detection rule in an enterprise environment:

• Scenario: IoT Device Firmware Updates via Scheduled Tasks

  • Context: Enterprise-managed IoT devices (e.g., Cisco IP phones, Aruba access points) often run scheduled firmware updates that connect to vendor update servers. These connections may match Mirai’s known outbound traffic patterns or specific hash signatures used in the hunt package.
  • Filter/Exclusion: Create a whitelist exclusion for the specific source IP ranges of managed IoT subnets (e.g., 10.50.x.x) and filter out connections to known vendor update domains (e.g., *.cisco.com, *.arubanetworks.com). Additionally, exclude traffic occurring strictly during the maintenance window defined in the SCCM or Ansible job schedule (e.g., 02:00–04:00 UTC).

• Scenario: Legacy Backup Agents Scanning External Storage

  • Context: Older versions of backup agents, such as Veeam Backup & Replication or Commvault, may initiate network scans to external cloud storage buckets (AWS S3, Azure Blob) using ports and protocols that overlap with Mirai’s command-and-control signatures. The agent’s heartbeat mechanism might trigger the IOC match.
  • Filter/Exclusion: Exclude traffic originating from specific backup server hostnames or IPs (e.g., srv-backup-01, srv-backup-02). Implement a filter to ignore alerts where the process name matches known backup executables like vrb.exe (Veeam) or commvault_agent.exe.

• Scenario: Security Tool Health Checks and Telemetry

  • Context: Endpoint Detection and Response (EDR) solutions like **Crow