IP Threat Report
45.134.20.127
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst 18/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 19/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 20/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 6/2s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 122 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 798 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 969 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 342 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 372 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 375 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 38 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| UA changed | Multiple User-Agents — bot rotation technique | +25 |
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 45.134.20.127 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 45.134.20.0/24
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🌊 Flood / DDoS Mitigation
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 45.134.20.127.
🤖 Bot Detection
Address UA spoofing from 45.134.20.127: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
Neighbors in 45.134.20.0/24
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
45.134.20.127 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
Threat intelligence analysis has linked 45.134.20.127 to malicious activity originating from Sydney, Australia, operating on the network of VPN. The address has been under observation since its initial detection. Our sensors captured 197 malicious requests from this address across a 7-day span, reflecting a sustained attack cadence of ~28.1 requests per 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. The dual attack vectors of Request Flooding combined with User-Agent Anomaly indicate a coordinated assault rather than opportunistic scanning. With 87 flagged addresses, Australia represents a notable presence in our threat database. A score of 255/100 places this address in the top tier of severity. Block and investigate any historical connections.
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 Australia
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💡 DDoS Mitigation Approaches
Distributed denial of service attacks overwhelm infrastructure with traffic volume. Effective mitigation combines always-on traffic scrubbing, anycast network distribution, rate limiting, and the ability to quickly scale absorption capacity during attacks.
💡 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.