
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst 32/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 33/10s | Abnormally fast request rate — automated scanning | +35 | |
| Foreign referer | Referer from unrelated external domain | +10 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 172.59.42.159.
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.
172.59.42.159 has been assigned a threat score of 80/100 (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:
IP address 172.59.42.159 has been traced to El Paso, United States, operating on the network of T-Mobile USA, Inc.. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Over a period of 5 days, this IP generated 364 malicious requests, averaging approximately 72.8 requests per day. This is a mobile network IP. While mobile addresses are typically shared via CGNAT, persistent malicious activity from this specific address suggests automated abuse. Rate-based attacks from this IP aim to overwhelm server resources through high-volume request flooding. United States currently accounts for 107 blocked IPs in our database, making it a significant source of malicious traffic. The score of 80/100 indicates a confirmed malicious actor. Network-level blocking is appropriate.
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.
Signature-based detection matches known attack patterns but misses novel threats. Behavioral analysis identifies anomalies in request patterns, timing, and volume, catching zero-day attacks that signatures cannot recognize.