
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Burst 14/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 15/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 16/10s | Abnormally fast request rate — automated scanning | +35 | |
| 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 20/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 21/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 23/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 24/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 32/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 34/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 65/10s | 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 68/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 70/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 71/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 72/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 73/10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 1 | Medium-risk: admin panels, config files | +10 | |
| Danger medium hits: 152 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 16 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| Danger medium hits: 24 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 26 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 32 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 33 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 4 | Medium-risk: admin panels, config files | +40 | |
| Danger medium hits: 64 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 66 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 80 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| Danger strong hits: 3 | High-risk paths: shells, RCE vectors, exploits | +75 | |
| Danger strong hits: 4 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 6 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 8 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 9 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Probe 302→404 | Behavioral anomaly detected by automated analysis | +20 | |
| UA suspicious | Behavioral anomaly detected by automated analysis | +15 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Block scanning from 172.170.251.50: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
IP 172.170.251.50 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
IP 172.170.251.50 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
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.
172.170.251.50 has been assigned a threat score of 280/100 (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:
The address 172.170.251.50 originates from Des Moines, United States, operating on the network of Microsoft. It was identified through automated analysis of incoming network traffic across monitored endpoints. Our sensors captured 5,166 malicious requests from this address across a 4-day span, reflecting a sustained attack cadence of ~1291.5 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 diversity of 3 separate attack methods suggests a comprehensive attack toolkit — likely an automated scanner that tests for vulnerabilities across multiple categories. With 17 flagged addresses, United States represents a notable presence in our threat database. A score of 280/100 places this address in the top tier of severity. Block and investigate any historical connections.
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.