
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA suspicious (short/empty) | Behavioral anomaly detected by automated analysis | +15 | |
| Danger strong hits: 8 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 94 | Medium-risk: admin panels, config files | +60 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Burst: 33 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 93 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 14 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 188 | Medium-risk: admin panels, config files | +60 | |
| Burst: 42 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 96 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 12 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 92 | Medium-risk: admin panels, config files | +60 | |
| Burst: 62 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 125 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 51 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 110 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 6 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Burst: 46 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 94 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 25 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 74 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 9 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Burst: 100 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 32 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 81 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 61 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 141 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 66 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 34 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 90 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 48 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 124 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 72 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 3 | High-risk paths: shells, RCE vectors, exploits | +75 | |
| Danger medium hits: 47 | Medium-risk: admin panels, config files | +60 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Burst: 47 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 47 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 18 | Medium-risk: admin panels, config files | +60 | |
| Burst: 27 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 27 req / 10s | Abnormally fast request rate — automated scanning | +35 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Address UA spoofing from 20.240.242.60: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
IP 20.240.242.60 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 20.240.242.60.
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.
20.240.242.60 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 20.240.242.60 originates from Gävle, Sweden, operating on the network of Microsoft Corporation. It was identified through automated analysis of incoming network traffic across monitored endpoints. Over a period of 1 days, this IP generated 16 malicious requests, averaging approximately 16 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. Sweden currently accounts for 102 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.
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
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.