IP Threat Report
161.115.233.33
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger medium hits: 4 | Medium-risk: admin panels, config files | +40 | |
| Burst: 58 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 59 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Foreign referer seen | Referer from unrelated external domain | +10 | |
| Burst: 43 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 58 req / 10s | Abnormally fast request rate — automated scanning | +35 |
Observed Activity
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Timeline
Network Provider
Recommendations
Actions taken & recommended
- IP 161.115.233.33 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Other malicious IPs detected in the same /24 subnet — consider blocking 161.115.233.0/24
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🌊 Traffic Flood Defense
IP 161.115.233.33 is generating excessive traffic. Limit connections per source IP. Enable geographic blocking if traffic from this region is unexpected.
Neighbors in 161.115.233.0/24
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
Open Ports & Services
Network reconnaissance data from Shodan. Open ports may indicate running services, misconfigurations, or potential attack surfaces.
| Port | Service | Risk | Description |
|---|---|---|---|
| 22 | SSH | Low | Secure Shell — common brute force target for remote access |
| 6060 | Unknown | Low | Service on port 6060 |
| 12345 | Unknown | Low | Service on port 12345 |
Data source: Shodan InternetDB. Scanned independently of abuse.mom.
Blacklist Status (DNSBL)
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.
Threat Analysis
161.115.233.33 has been assigned a threat score of 120/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
The following attack categories were identified:
📊 Threat Analysis
Our monitoring infrastructure has identified 161.115.233.33, geolocated to Los Angeles, United States, operating on the network of Server Mania Inc, as a source of suspicious network activity. Over a period of 24 days, this IP generated 2 malicious requests, averaging approximately 0.1 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 is engaged in request flooding, sending traffic at rates designed to exhaust server capacity. Our records show 183 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. At 120/100, this is an extremely high-risk address. All traffic should be considered hostile.
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.
Related Threats
Security Intelligence
💡 File Upload Vulnerabilities
Insecure file upload functionality allows attackers to upload web shells, malware, or scripts that execute on the server. Proper validation must check file content, not just extensions, and uploaded files should be stored outside the web root.
💡 Machine Learning in Threat Detection
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.