
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst 13/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 14/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 16/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 18/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 40/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 46/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 5/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 50/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 53/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 55/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 64/10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 40 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 48 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 969 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 121 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 126 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| Danger strong hits: 372 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| UA changed | Multiple User-Agents — bot rotation technique | +25 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
We have detected 1605 malicious IPs from this autonomous system.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 192.253.209.90.
IP 192.253.209.90 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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.
192.253.209.90 has been assigned a threat score of 255/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
The following attack categories were identified:
Our monitoring infrastructure has identified 192.253.209.90, geolocated to Jersey City, United States, operating on the network of GSL Networks Pty LTD, as a source of suspicious network activity. During its 23-day observation window, we recorded 1,359 hostile requests from this IP — roughly 59.1 per day on average. 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. Our records show 36,718 malicious IPs originating from United States, positioning it as a major contributor to global threat activity. At 255/100, this is an extremely high-risk address. All traffic should be considered hostile.
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.
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.
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.