IP Threat Report
178.239.198.61
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 | |
| Danger strong hits: 362 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 959 | Medium-risk: admin panels, config files | +60 | |
| Burst: 23 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 77 req / 10s | 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 178.239.198.61 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Other malicious IPs detected in the same /24 subnet — consider blocking 178.239.198.0/24
- 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 178.239.198.61: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
🌊 Flood / DDoS Mitigation
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 178.239.198.61.
Neighbors in 178.239.198.0/24
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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
178.239.198.61 has been assigned a threat score of 255/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
The following attack categories were identified:
📊 Threat Analysis
Our monitoring infrastructure has identified 178.239.198.61, geolocated to London, United Kingdom, operating on the network of UK Dedicated Servers Limited, as a source of suspicious network activity. The address has been active for 1 days in our monitoring system, producing 1 flagged requests at a rate of ~1/day. The address operates as a VPN/proxy exit node. Attackers route traffic through anonymizing services to obscure their real location and evade IP-based security controls. Two attack patterns were identified (User-Agent Anomaly and Request Flooding), suggesting a semi-automated campaign that targets multiple vulnerabilities. Our records show 171 malicious IPs originating from United Kingdom, positioning it as a significant contributor to global threat activity. With a threat score of 255/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
This IP is associated with a VPN or proxy service. Attackers frequently route their traffic through anonymizing services to obscure their true location. This makes attribution more challenging but the malicious behavior patterns remain detectable.
Related Threats
🇬🇧 Top threats from United Kingdom
104.28.240.60 (340)104.28.208.60 (340)91.230.225.3 (305)45.86.203.224 (305)45.86.203.35 (305)View all →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.
💡 Automated Incident Response
Automated response systems can block threats in milliseconds, far faster than human analysts. However, automation requires careful safeguards — rate limits on blocking actions, automatic expiration, and human review queues prevent automated systems from causing self-inflicted outages.