IP Threat Report
154.81.155.37
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| UA bot: crawler | Known bot/crawler User-Agent detected | +40 | |
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 |
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 154.81.155.37 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🤖 User-Agent Anomaly Defense
IP 154.81.155.37 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
🔎 Directory Scan Defense
IP 154.81.155.37 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
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 |
| 2332 | Unknown | Low | Service on port 2332 |
| 5555 | Unknown | Low | Service on port 5555 |
| 6000 | Unknown | Low | Service on port 6000 |
| 6002 | Unknown | Low | Service on port 6002 |
| 8885 | Unknown | Low | Service on port 8885 |
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
154.81.155.37 has been assigned a threat score of 90/100 (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
154.81.155.37 is registered in Singapore, Singapore, operating on the network of liasail global hongkong limited. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. During its 1-day observation window, we recorded 1 hostile requests from this IP — roughly 1 per day on average. The address is classified as residential, meaning it likely belongs to an end-user ISP connection. Malicious activity from residential IPs typically indicates device compromise or botnet membership. The dual attack vectors of User-Agent Anomaly combined with Path Enumeration indicate a coordinated assault rather than opportunistic scanning. Singapore currently accounts for 105 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 90/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
This IP is classified as residential, suggesting it may belong to a compromised home device, IoT botnet member, or an infected personal computer. Residential IPs involved in attacks often indicate malware infection without the owner's knowledge.
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💡 TLS Fingerprinting (JA3/JA4)
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.
💡 Web Application Firewall Strategies
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.