IP Threat Report
158.23.144.198
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: 12 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 435 | Medium-risk: admin panels, config files | +60 | |
| Burst: 18 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 64 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 16 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 52 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 18 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 252 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 9 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 290 | Medium-risk: admin panels, config files | +60 | |
| Burst: 17 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 61 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 62 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 63 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 6 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 55 | Medium-risk: admin panels, config files | +60 | |
| Burst: 48 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 24 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 378 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 3 | High-risk paths: shells, RCE vectors, exploits | +75 | |
| Danger medium hits: 233 | Medium-risk: admin panels, config files | +60 | |
| Burst: 15 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 49 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 66 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 60 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Probe pattern 302->404 same path | Behavioral anomaly detected by automated analysis | +20 | |
| Danger medium hits: 429 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 286 | Medium-risk: admin panels, config files | +60 | |
| Burst: 65 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 59 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 283 | Medium-risk: admin panels, config files | +60 | |
| Burst: 54 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 284 | Medium-risk: admin panels, config files | +60 | |
| Burst: 57 req / 10s | Abnormally fast request rate — automated scanning | +35 |
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 158.23.144.198 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 158.23.144.198 shows suspicious UA behavior. Block empty User-Agent requests. Implement JavaScript-based bot detection for sensitive endpoints.
🌊 Flood / DDoS Mitigation
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 158.23.144.198.
🔎 Path Enumeration Protection
Block scanning from 158.23.144.198: rate-limit 404 responses per IP, deploy a honeypot 404 page, ensure no backup files are web-accessible.
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
158.23.144.198 has been assigned a threat score of 280/100 (Critical). This places it in the critical threat category. Immediate blocking is strongly advised across all network perimeters.
The following attack categories were identified:
📊 Threat Analysis
158.23.144.198 is registered in Querétaro City, Mexico, operating on the network of Microsoft Corporation. This IP first appeared in our threat feeds after triggering multiple behavioral detection signatures. The address has been active for 1 days in our monitoring system, producing 26 flagged requests at a rate of ~26/day. This address belongs to a datacenter or cloud hosting provider. Hosting IPs are frequently leveraged by threat actors who rent cheap VPS instances specifically for conducting attacks. The diversity of 3 separate attack methods suggests a comprehensive attack toolkit — likely an automated scanner that tests for vulnerabilities across multiple categories. With 122 flagged addresses, Mexico represents a significant presence in our threat database. With a threat score of 280/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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.