
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Burst 112/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 127/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 140/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 32/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 35/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 42/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 47/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 48/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 49/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 68/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 90/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 94/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 96/10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 48 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 94 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 96 | Medium-risk: admin panels, config files | +60 | |
| 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 | |
| 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 74.248.32.128: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 74.248.32.128.
IP 74.248.32.128 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.
74.248.32.128 has been assigned a threat score of 280/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
The following attack categories were identified:
The address 74.248.32.128 originates from Warsaw, Poland, operating on the network of Microsoft Corporation. It was identified through automated analysis of incoming network traffic across monitored endpoints. The address has been active for 3 days in our monitoring system, producing 780 flagged requests at a rate of ~260/day. This address belongs to a datacenter or cloud hosting provider. Hosting IPs are frequently leveraged by threat actors who rent cheap VPS instances specifically for conducting attacks. The combination of 3 distinct attack vectors indicates a sophisticated, multi-pronged threat actor deploying automated tools that probe multiple attack surfaces simultaneously. At 280/100, 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.