IP Threat Report
159.203.46.12
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| 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 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 |
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 159.203.46.12 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 159.203.46.0/24
- Report abuse to the network provider via their abuse contact
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
🤖 Bot Detection
Address UA spoofing from 159.203.46.12: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
🔎 Path Enumeration Protection
Block scanning from 159.203.46.12: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
Neighbors in 159.203.46.0/24
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 |
| 80 | HTTP | Low | HTTP web server — standard web traffic |
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
159.203.46.12 has been assigned a threat score of 120/100 (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:
📊 Threat Analysis
Our monitoring infrastructure has identified 159.203.46.12, geolocated to Toronto, Canada, operating on the network of DigitalOcean, LLC, as a source of suspicious network activity. Our sensors captured 1 malicious requests from this address across a 1-day span, reflecting a sustained attack cadence of ~1 requests per day. Classified as a hosting IP, this address likely runs on a rented server or cloud instance. Attackers prefer datacenter IPs for their high bandwidth and disposable nature. The dual attack vectors of User-Agent Anomaly combined with Path Enumeration indicate a coordinated assault rather than opportunistic scanning. With 108 flagged addresses, Canada represents a significant presence in our threat database. 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
💡 HTTP Header Analysis for Threat Detection
Examining HTTP headers beyond User-Agent reveals attack tools and automated scripts. Missing standard headers, unusual ordering, non-standard values, and inconsistencies with claimed client identity all serve as reliable detection signals.
💡 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.