
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst: 12 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 13 req / 10s | Abnormally fast request rate — automated scanning | +35 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 88.218.173.32 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
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.
88.218.173.32 has been assigned a threat score of 70/100 (High). The IP is rated as a high-level threat. Network administrators should implement blocking rules and monitor for any connections from this address.
The following attack categories were identified:
Network traffic from 88.218.173.32, located in New York, United States, operating on the network of GTT Communications Inc., has been classified as malicious by our automated threat scoring engine. Our sensors captured 1 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~1 requests per day. This residential IP is likely a compromised consumer device. Home routers and IoT equipment with default credentials are prime targets for botnet operators. The IP is engaged in request flooding, sending traffic at rates designed to exhaust server capacity. Our records show 21 malicious IPs originating from United States, positioning it as a notable contributor to global threat activity. A threat score of 70/100 places this IP in the high-risk category. Blocking at the firewall level is recommended.
This IP is classified as residential, suggesting it may belong to a compromised home device, IoT botnet member, or an infected personal computer. Residential IPs involved in attacks often indicate malware infection without the owner's knowledge.
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 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.