
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst 11/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 13/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 14/10s | 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.
IP 45.207.176.7 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
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.
45.207.176.7 has been assigned a threat score of 80/100 (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:
IP address 45.207.176.7 has been traced to Alexandria, United States, operating on the network of Cox Communications Inc. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 289 malicious requests from this address across a 2-day span, reflecting a sustained attack cadence of ~144.5 requests per day. This is a residential IP address, suggesting a compromised home device such as a router, smart appliance, or infected workstation participating in a botnet. The IP is engaged in request flooding, sending traffic at rates designed to exhaust server capacity. United States currently accounts for 173 blocked IPs in our database, making it a significant source of malicious traffic. At 80/100, this IP warrants immediate defensive action.
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.