IP Threat Report
15.135.91.30
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst 11/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 6/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 8/2s | Abnormally fast request rate — automated scanning | +35 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 | |
| UA bot: Go-http-client | Known bot/crawler User-Agent detected | +40 | |
| 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
We have detected 3967 malicious IPs from this autonomous system.
Recommendations
Actions taken & recommended
- IP 15.135.91.30 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
🌊 Flood / DDoS Mitigation
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 15.135.91.30.
🔎 Path Enumeration Protection
Block scanning from 15.135.91.30: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
🤖 User-Agent Anomaly Defense
IP 15.135.91.30 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
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
15.135.91.30 has been assigned a threat score of 145/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
The following attack categories were identified:
📊 Threat Analysis
IP address 15.135.91.30 has been traced to Sydney, Australia, operating on the network of Amazon.com, Inc.. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 27 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~27 requests per day. This address belongs to a datacenter or cloud hosting provider. Hosting IPs are frequently leveraged by threat actors who rent cheap VPS instances specifically for conducting attacks. The combination of 3 distinct attack vectors indicates a sophisticated, multi-pronged threat actor deploying automated tools that probe multiple attack surfaces simultaneously. Our records show 1,269 malicious IPs originating from Australia, positioning it as a major contributor to global threat activity. At 145/100, this is an extremely high-risk address. All traffic should be considered hostile.
This IP belongs to a hosting or data center provider. Malicious traffic from hosting infrastructure often originates from compromised VPS instances, rented servers used for scanning campaigns, or abused free-tier cloud accounts. Hosting providers typically respond to abuse reports within 24-72 hours.
Related Threats
🇦🇺 Top threats from Australia
137.23.3.14 (310)20.92.77.159 (310)3.26.255.172 (308)3.26.98.5 (308)35.189.4.156 (298)View all →Security Intelligence
💡 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.
💡 Privacy-Preserving Threat Detection
Advanced techniques enable threat detection while minimizing privacy impact. Encrypted DNS, differential privacy in analytics, and federated learning for threat models allow effective security monitoring without unnecessary surveillance of legitimate user behavior.