
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 3 | High-risk paths: shells, RCE vectors, exploits | +75 | |
| Danger medium hits: 38 | Medium-risk: admin panels, config files | +60 | |
| Burst: 11 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 37 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 14 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 41 | Medium-risk: admin panels, config files | +60 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Burst: 39 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 38 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 13 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 28 | Medium-risk: admin panels, config files | +60 | |
| Burst: 36 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 71 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 9 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 33 | Medium-risk: admin panels, config files | +60 | |
| Burst: 10 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 33 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 10 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 46 | Medium-risk: admin panels, config files | +60 | |
| Burst: 35 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 24 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 51 | Medium-risk: admin panels, config files | +60 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 4.196.164.90 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
Block scanning from 4.196.164.90: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
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.
4.196.164.90 has been assigned a threat score of 255/100 (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:
The address 4.196.164.90 originates from The Rocks, Australia, operating on the network of Microsoft Corporation. It was identified through automated analysis of incoming network traffic across monitored endpoints. Our sensors captured 24 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~24 requests per 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 dual attack vectors of Request Flooding combined with Path Enumeration indicate a coordinated assault rather than opportunistic scanning. With 101 flagged addresses, Australia represents a significant presence in our threat database. A score of 255/100 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.
XSS attacks inject malicious scripts into web pages viewed by other users. Reflected XSS uses crafted URLs, while stored XSS persists in databases. Both types can steal session cookies, redirect users, or deface websites.
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.