IP Threat Report
173.239.218.160
ABUSE.MOM — BEHAVE OR GET EXPOSED
Geolocation & Classification
Detection Signatures
| Signature | Description | Points | Severity |
|---|---|---|---|
| DDoS / Flood | 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 173.239.218.160 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 173.239.218.0/24
- Ensure sensitive files (.env, .git, backups) are not accessible from the web
⚙️ Defensive Recommendations
Block 173.239.218.160 at the network perimeter. Implement defense-in-depth combining IP blocking with application-layer protections.
Neighbors in 173.239.218.0/24
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
173.239.218.160 has been assigned a threat score of 255/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
📊 Threat Analysis
The address 173.239.218.160 originates from Los Angeles, United States, operating on the network of LogicWeb Inc. It was identified through automated analysis of incoming network traffic across monitored endpoints. During its 12-day observation window, we recorded 186 hostile requests from this IP — roughly 15.5 per day on average. With 151 flagged addresses, United States represents a significant presence in our threat database. With a threat score of 255/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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💡 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.