
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Burst 17/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 18/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 19/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 56/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 58/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 61/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 62/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 64/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 66/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 67/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 17 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 18 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 19 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 56 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 58 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 61 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 62 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 64 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 66 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 67 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 87 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 8 | High-risk paths: shells, RCE vectors, exploits | +100 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 74.249.233.75 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.
74.249.233.75 has been assigned a threat score of 230/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
The following attack categories were identified:
IP address 74.249.233.75 has been traced to Des Moines, United States, operating on the network of Microsoft Corporation. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 1,946 malicious requests from this address across a 6-day span, reflecting a sustained attack cadence of ~324.3 requests per day. The IP is classified as hosting/datacenter infrastructure, commonly associated with rented servers used for automated attack campaigns, botnet command-and-control, or vulnerability scanning at scale. The IP is engaged in request flooding, sending traffic at rates designed to exhaust server capacity. United States currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. At 230/100, 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.
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.