
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Burst 118/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 160/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 163/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 183/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 38/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 41/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 52/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 54/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 94/10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 1080 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 263 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 526 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 147 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 32 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 40 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 64 | 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 20.91.198.61: 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 20.91.198.61.
IP 20.91.198.61 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.
20.91.198.61 has been assigned a threat score of 280/100 (Critical). With this rating, the IP falls into the critical severity bracket — among the most dangerous addresses in our monitoring database.
The following attack categories were identified:
The address 20.91.198.61 originates from Gävle, Sweden, 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 6 days in our monitoring system, producing 1,011 flagged requests at a rate of ~168.5/day. Classified as a hosting IP, this address likely runs on a rented server or cloud instance. Attackers prefer datacenter IPs for their high bandwidth and disposable nature. With 3 different attack patterns detected, this IP exhibits behavior characteristic of advanced automated scanning frameworks. Sweden currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. 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.
False positives erode trust in security systems and waste analyst resources. Effective management requires feedback loops, allowlisting mechanisms, contextual analysis, and regular tuning of detection rules based on operational experience.