
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA suspicious (short/empty) | Behavioral anomaly detected by automated analysis | +15 | |
| Danger strong hits: 2 | High-risk paths: shells, RCE vectors, exploits | +50 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 |
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 142.93.201.234: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
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.
142.93.201.234 has been assigned a threat score of 85/100 (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:
Network traffic from 142.93.201.234, located in North Bergen, United States, operating on the network of DigitalOcean, LLC, has been classified as malicious by our automated threat scoring engine. Over a period of 1 days, this IP generated 1 malicious requests, averaging approximately 1 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 IP exhibits User-Agent manipulation, switching between different browser identities or sending empty headers. Our records show 128 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. The score of 85/100 indicates a confirmed malicious actor. Network-level blocking is appropriate.
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.
TLS fingerprinting creates unique identifiers based on how clients negotiate encrypted connections. The JA3 and JA4 methods generate hashes from TLS ClientHello parameters, enabling identification of specific tools and malware regardless of IP address changes.
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.