IP Threat Report
70.179.29.73
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| Directory Scan | Behavioral anomaly detected by automated analysis | +0 |
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
Recommendations
Actions taken & recommended
- IP 70.179.29.73 is blocked at application level (HTTP 403)
- Consider blocking at firewall level (iptables/CSF) to reduce server load
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ Defensive Recommendations
Block 70.179.29.73 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
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
70.179.29.73 has been assigned a threat score of 60/100 (High). The IP is rated as a high-level threat. Network administrators should implement blocking rules and monitor for any connections from this address.
📊 Threat Analysis
70.179.29.73 is registered in San Diego, United States, operating on the network of Cox Communications Inc.. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. Over a period of 17 days, this IP generated 281 malicious requests, averaging approximately 16.5 requests per day. With 101 flagged addresses, United States represents a significant presence in our threat database. The score of 60/100 warrants active monitoring and rate-limiting. Full blocking is advisable for sensitive systems.
Related Threats
🇺🇸 Top threats from United States
104.28.246.115 (350)103.215.74.213 (340)103.168.66.237 (340)34.187.158.4 (340)34.186.187.208 (340)View all →Security Intelligence
💡 Content Delivery Network Risks
CDNs can inadvertently mask the true origin of malicious traffic, making attribution difficult. Attackers abuse CDN services to proxy their attacks, leverage cached content for amplification, and exploit misconfigurations in CDN-to-origin connections.
💡 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.