
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 | |
| UA bot: python | Known bot/crawler User-Agent detected | +40 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 36.150.136.138 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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.
36.150.136.138 has been assigned a threat score of 65/100 (High). The IP is rated as a high-level threat. Network administrators should implement blocking rules and monitor for any connections from this address.
The following attack categories were identified:
IP address 36.150.136.138 has been traced to Nanjing, China, operating on the network of China Mobile Communications Corporation. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Over a period of 24 days, this IP generated 242 malicious requests, averaging approximately 10.1 requests per day. The address belongs to a mobile carrier network. The sustained pattern of malicious requests indicates either a compromised device or deliberate abuse. Detected suspicious User-Agent anomalies including empty, forged, or rapidly rotating UA strings — characteristic of automated scanning tools. With 106 flagged addresses, China represents a significant presence in our threat database. The score of 65/100 warrants active monitoring and rate-limiting. Full blocking is advisable for sensitive systems.
Vulnerability scanning is the automated process of probing web applications for known weaknesses. Attackers use tools like Nuclei, Nikto, and ZAP to test thousands of hosts per hour, looking for exposed configuration files, outdated software, and default credentials.
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.