IP Threat Report
192.42.116.20
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Burst: 12 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 6 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 8 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 | |
| Foreign referer | Referer from unrelated external domain | +10 | |
| Foreign referer seen | Referer from unrelated external domain | +10 | |
| UA bot: Go-http-client | Known bot/crawler User-Agent detected | +40 |
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 192.42.116.20 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 192.42.116.0/24
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🔎 Directory Scan Defense
IP 192.42.116.20 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
🌊 Flood / DDoS Mitigation
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 192.42.116.20.
🤖 Bot Detection
Address UA spoofing from 192.42.116.20: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
Neighbors in 192.42.116.0/24
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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
192.42.116.20 has been assigned a threat score of 80/100 (Critical). This is a critical-level threat. Systems administrators should treat this IP as hostile and block all inbound connections without exception.
The following attack categories were identified:
📊 Threat Analysis
Threat intelligence analysis has linked 192.42.116.20 to malicious activity originating from Amsterdam, Netherlands, operating on the network of Church of Cyberology. The address has been under observation since its initial detection. Over a period of 19 days, this IP generated 201 malicious requests, averaging approximately 10.6 requests per day. This IP is identified as a VPN or proxy endpoint, commonly used to mask the true origin of attack traffic and bypass geographic or reputation-based blocking. The combination of 3 distinct attack vectors indicates a sophisticated, multi-pronged threat actor deploying automated tools that probe multiple attack surfaces simultaneously. Our records show 112 malicious IPs originating from Netherlands, positioning it as a significant contributor to global threat activity. The score of 80/100 indicates a confirmed malicious actor. Network-level blocking is appropriate.
This IP is associated with a VPN or proxy service. Attackers frequently route their traffic through anonymizing services to obscure their true location. This makes attribution more challenging but the malicious behavior patterns remain detectable.
Related Threats
🇳🇱 Top threats from Netherlands
185.184.192.251 (338)45.88.138.44 (320)45.82.64.236 (315)5.181.169.192 (313)157.22.73.141 (313)View all →🏢 Same network: AS215125
192.42.116.110 (80)192.42.116.64 (80)192.42.116.118 (80)192.42.116.68 (80)192.42.116.21 (65)View all →Security Intelligence
💡 DDoS Mitigation Approaches
Distributed denial of service attacks overwhelm infrastructure with traffic volume. Effective mitigation combines always-on traffic scrubbing, anycast network distribution, rate limiting, and the ability to quickly scale absorption capacity during attacks.
💡 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.