IP Threat Report
195.101.104.189
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA changed for same IP | Multiple User-Agents — bot rotation technique | +25 | |
| Burst: 7 req / 2s | Abnormally fast request rate — automated scanning | +35 |
Observed Activity
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Timeline
Network Provider
Recommendations
Actions taken & recommended
- IP 195.101.104.189 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🤖 Bot Detection
Address UA spoofing from 195.101.104.189: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
🌊 Traffic Flood Defense
IP 195.101.104.189 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
Open Ports & Services
Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.
| Port | Service | Risk | Description |
|---|---|---|---|
| 22 | SSH | Low | Secure Shell — common brute force target for remote access |
| 8443 | HTTPS-Alt | Low | Service on port 8443 |
| 10001 | Unknown | Low | Service on port 10001 |
| 10004 | Unknown | Low | Service on port 10004 |
| 10007 | Unknown | Low | Service on port 10007 |
| 10009 | Unknown | Low | Service on port 10009 |
| 10011 | Unknown | Low | Service on port 10011 |
| 10012 | Unknown | Low | Service on port 10012 |
| 10013 | Unknown | Low | Service on port 10013 |
| 10015 | Unknown | Low | Service on port 10015 |
| 10016 | Unknown | Low | Service on port 10016 |
| 10017 | Unknown | Low | Service on port 10017 |
| 10018 | Unknown | Low | Service on port 10018 |
| 10020 | Unknown | Low | Service on port 10020 |
| 10022 | Unknown | Low | Service on port 10022 |
| 10023 | Unknown | Low | Service on port 10023 |
| 10024 | Unknown | Low | Service on port 10024 |
| 10026 | Unknown | Low | Service on port 10026 |
| 11001 | Unknown | Low | Service on port 11001 |
Data source: Shodan InternetDB. Scanned independently of abuse.mom.
Blacklist Status (DNSBL)
This IP was checked against major DNS-based blacklists used by mail servers and firewalls worldwide.
Checked: Spamhaus, SpamCop, Barracuda, SORBS, CBL, UCEProtect. Results may change over time.
Threat Analysis
195.101.104.189 has been assigned a threat score of 60/100 (High). At this threat level, the IP is considered high risk. Firewall rules should be updated to deny traffic from this source.
The following attack categories were identified:
📊 Threat Analysis
IP address 195.101.104.189 has been traced to Lille, France, operating on the network of Orange S.A.. Our threat detection systems have flagged this address based on observed malicious behavior patterns. During its 1-day observation window, we recorded 1 hostile requests from this IP — roughly 1 per day on average. The address is classified as residential, meaning it likely belongs to an end-user ISP connection. Malicious activity from residential IPs typically indicates device compromise or botnet membership. The dual attack vectors of User-Agent Anomaly combined with Request Flooding indicate a coordinated assault rather than opportunistic scanning. With 135 flagged addresses, France represents a significant presence in our threat database. At 60/100, this IP presents a meaningful threat. Implement rate limiting with escalation to blocking.
This IP is classified as residential, suggesting it may belong to a compromised home device, IoT botnet member, or an infected personal computer. Residential IPs involved in attacks often indicate malware infection without the owner's knowledge.
Related Threats
Security Intelligence
💡 HTTP Header Analysis for Threat Detection
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
💡 Machine Learning in Threat Detection
Machine learning models analyze vast amounts of network traffic to identify attack patterns invisible to rule-based systems. Supervised models classify known attack types while unsupervised models detect anomalies that may indicate novel threats.