As a large language model, I cannot provide real-time intelligence or access to private databases or proprietary tools to generate specific intelligence briefings. However, I can guide you on how to approach creating a threat intelligence narrative for an IP address in a general sense. Hereβs a framework you might follow:
Intelligence Briefing for IP 202.69.169.162/32
#### Executive Summary
- Objective: To analyze the threat potential and historical activity associated with IP address 202.69.169.162/32.
- Scope: Network activity, historical behavior, relationships, and neighborhood data.
#### Network Activity
- Current Status: Determine if the IP is actively used for services or if itβs potentially a honeypot or a decommissioned address.
- Traffic Patterns: Use network monitoring tools to identify any unusual traffic patterns or spikes in activity, which could indicate malicious usage.
#### Historical Behavior
- Blacklist Checks: Review if the IP has been blacklisted by security organizations or appears on known threat intelligence platforms.
- Past Incidents: Investigate any past incidents or reports of malicious activity associated with this IP, such as malware distribution or botnet participation.
#### Relationships
- Domain Associations: Identify any domains hosted on this IP and assess their reputations.
- Known Threat Actors: Check if the IP has been linked to known threat actors or groups.
#### Neighborhood Data
- Subnet Analysis: Examine the surrounding IP addresses and subnets for any patterns of malicious activity.
- Geolocation: Determine the physical location associated with the IP to assess potential geopolitical risks.
#### Threat Assessment
- Risk Level: Based on the gathered data, assess the threat level (e.g., low, moderate, high) and provide recommendations for further monitoring or mitigation actions.
#### Recommendations
- Monitoring: Suggest continuous monitoring of traffic from/to this IP.
- Blocking/Whitelisting: Depending on the assessment, consider blocking the IP or whitelisting if itβs deemed safe.
- Further Investigation: Recommend deeper forensic analysis if suspicious activity is detected.
#### Conclusion
Summarize the findings and emphasize the importance of continuous monitoring and updating of threat intelligence to adapt to evolving threats.
Note
For accurate and specific intelligence, use tools like WHOIS lookups, threat intelligence platforms (e.g., VirusTotal, AlienVault OTX), and network monitoring solutions. Always ensure compliance with legal and organizational policies when conducting such analyses.
This summary was generated by AI and may contain inaccuracies. Verify critical details independently.
π’ Ownership & Registration
| Organization | Engelbert de Guzman |
| ASN | AS17639 |
| Network Name | LLC-KOHEI-CLARKFIELD |
| CIDR Block | 202.69.169.160/27 |
| RIR | APNIC |
| Country | PH |
| Abuse Contact | β |
π DNS Intelligence
| PTR | 162.169.69.202.-rev.convergeict.com |
| Forward Confirmed | No β PTR hostname does not resolve back to this IP (weak signal) |
| Forward Hostnames | 162.169.69.202.-rev.convergeict.com |
π DNS Hygiene
| Hygiene Score | 60% (Good) |
| SPF | Present |
| DMARC | Present |
| FCrDNS | Not verified |
| DNSSEC | Valid |
| CAA | Not configured |
βοΈ Network Classification
| Infrastructure | Unknown |
| Service Purpose | Web Server |
| Network Tier | Tier 3 β Basic operator with some routing infrastructure |
π Services & Open Ports
| Port | Service | Protocol | Banner |
|---|---|---|---|
| 80 | http | tcp | β |
| 443 | https | tcp | β |
| 22 | ssh | tcp | |
| 8080 | http-alt | tcp | β |
| 8443 | https-alt | tcp | β |
| Closed Ports | 25, 3389 (5 open / 7 scanned) | ||
| Server | nginx |
| HTTP Title | β |
| SSH Version | SSH-2.0-OpenSSH_8.8 |
π TLS Certificate
| SANs | None |
| Valid From | 2018-06-07T14:01:51+00:00 |
| Valid Until | 2118-05-14T14:01:51+00:00 |
| TLS Protocol | Tls12 |
| Cipher Suite | TLS_ECDHE_RSA_WITH_AES_256_GCM_SHA384 |
| Signature Algorithm | sha256RSA |
| Validity Period | 36500 days |
| Serial Number | 0756801C |
| Thumbprint | DC3F86994BD58D41E2EE4CF60BE77615B92E4ED0 |
π― Confidence Breakdown
Per-dimension confidence scores based on source diversity and data freshness
| Dimension | Score | Sources | Observations |
|---|---|---|---|
| threat | 21% | 2 | 4 |
| routing | 29% | 4 | 5 |
| services | 23% | 2 | 4 |
| ownership | 22% | 3 | 3 |
| reputation | 21% | 1 | 3 |
| geolocation | 26% | 2 | 3 |
| Overall | 24% | 14 | 22 |
| Data Coherence | Mixed Signals (68%) β 2 contradiction(s) |
| Attribution | Moderate (65%) |
| OwnershipFCrDNSGeo ConsensusGeo PlausibleIRR MatchRPKI Valid |
β TLS certificate claims XX but primary geo says PH
π Observation Timeline π Live
| First Seen | 2026-05-07 23:04:09 UTC |
| Last Seen | 2026-06-26 18:12:22 UTC |
| Profile Built | 2026-06-27 11:10:21 UTC |
| Data Freshness | Live |
| Signal Types | 32 |
| Total Observations | 60 |
Full dossier details are available via our API.