As a large language model, I cannot conduct real-time internet searches or access external databases to gather current intelligence on specific IP addresses or entities. However, I can guide you on how to approach generating a threat intelligence narrative for an IP address like 202.165.15.88/32 using standard procedures and tools. Hereβs a general outline for such a task:
Intelligence Briefing for IP 202.165.15.88/32
#### Overview:
- IP Address: 202.165.15.88/32
- Geolocation: [Use a geo-IP service to determine the geographic location]
- Organization/ASN: [Use an ASN lookup service to identify the owning organization and Autonomous System Number (ASN)]
- Domain: [Use reverse DNS lookup to find associated domains]
#### Profile:
- Type of Entity: [Determine if it's a known organization, data center, or another entity]
- Industry: [If known, specify the industry the organization operates in]
#### Observation History:
- Malware Associations: [Use threat intelligence platforms like VirusTotal, AlienVault OTX, or IBM X-Force to check historical associations with malware]
- Phishing Campaigns: [Review phishing reports or databases like PhishTank or the Anti-Phishing Working Group (APWG) for any links to phishing activities]
- Known Threat Actors: [Check for any known affiliations with threat actors or groups using databases like MITRE ATT&CK or Recorded Future]
#### Relationships:
- Network Connections: [Analyze network traffic logs or use a network intelligence tool to identify other IPs frequently communicating with this address]
- C2 Infrastructure: [Determine if the IP has been used as a Command and Control server for malware or botnets]
#### Neighborhood Data:
- Neighboring IPs: [Use subnet analysis tools to examine IP addresses in the same /32 network]
- Traffic Patterns: [Assess traffic patterns using tools like Wireshark or Splunk for unusual activity]
#### Actionable Intelligence Narrative:
- Risk Assessment: Based on historical data and current observations, assess the risk level associated with this IP. Is it frequently associated with malicious activities?
- Mitigation Recommendations: Suggest measures for monitoring and blocking this IP if deemed a threat. Consider implementing network segmentation, enhanced logging, or deploying intrusion detection systems.
- Further Investigation: Recommend areas for further investigation, such as deep packet inspection or collaboration with threat intelligence communities for more insights.
#### Tools and Resources:
- Geo-IP services (e.g., MaxMind, IPinfo)
- ASN lookups (e.g., bgp.he.net, IANA)
- Threat intelligence platforms (e.g., AlienVault OTX, IBM X-Force)
- Malware databases (e.g., VirusTotal)
- Phishing databases (e.g., PhishTank, APWG)
This structured approach can help you create a comprehensive and actionable threat intelligence report. For real-time analysis, ensure compliance with legal and organizational policies regarding data privacy and security.
This summary was generated by AI and may contain inaccuracies. Verify critical details independently.
π’ Ownership & Registration
| Organization | Mohd Sobri Salamon |
| ASN | AS18206 |
| Network Name | β |
| CIDR Block | 202.165.14.0/23 |
| RIR | APNIC |
| Country | β |
| Abuse Contact | Available via RDAP |
π DNS Intelligence
| PTR Record | No PTR |
| Forward Confirmed | No β PTR hostname does not resolve back to this IP (weak signal) |
π DNS Hygiene
| Hygiene Score | 40% (Fair) |
| SPF | Not configured |
| DMARC | Not configured |
| FCrDNS | Not verified |
| DNSSEC | Valid |
| CAA | Present |
βοΈ Network Classification
| Infrastructure | Unknown |
| Service Purpose | Firewalled / No Services |
| Network Tier | Tier 2 β Moderate operator sophistication with routing hygiene |
π Services & Open Ports
| Port | Service | Protocol | Banner |
|---|---|---|---|
| No open ports detected | |||
| Closed Ports | 22, 25, 80, 443, 3389, 8080, 8443 (0 open / 7 scanned) | ||
| Server | Apache/2.4.52 |
| HTTP Title | β |
π TLS Certificate
| SANs | None |
| Valid From | β |
| Valid Until | β |
π― Confidence Breakdown
Per-dimension confidence scores based on source diversity and data freshness
| Dimension | Score | Sources | Observations |
|---|---|---|---|
| threat | 36% | 2 | 5 |
| routing | 27% | 4 | 5 |
| services | 24% | 2 | 4 |
| ownership | 27% | 3 | 4 |
| reputation | 24% | 1 | 3 |
| geolocation | 32% | 2 | 3 |
| Overall | 28% | 14 | 24 |
| Data Coherence | Mixed Signals (53%) β 3 contradiction(s) |
| Attribution | Moderate (65%) |
| OwnershipFCrDNSGeo ConsensusGeo PlausibleIRR MatchRPKI Valid |
β Geo sources disagree on country: MY, US
β TLS certificate claims US but primary geo says MY
π Observation Timeline π Live
| First Seen | 2026-05-07 23:04:09 UTC |
| Last Seen | 2026-06-26 18:11:04 UTC |
| Profile Built | 2026-06-23 06:13:54 UTC |
| Data Freshness | Live |
| Signal Types | 35 |
| Total Observations | 37 |
Full dossier details are available via our API.