IP Threat Report
74.234.98.46
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: 16 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 528 | Medium-risk: admin panels, config files | +60 | |
| Burst: 72 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 200 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger strong hits: 8 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger medium hits: 264 | Medium-risk: admin panels, config files | +60 | |
| Burst: 51 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 176 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 74.234.98.46 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
🤖 Bot Detection
Address UA spoofing from 74.234.98.46: maintain blocklist of known malicious UA strings, require consistent UA across sessions, implement TLS fingerprinting.
🌊 Flood / DDoS Mitigation
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 74.234.98.46.
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
74.234.98.46 has been assigned a threat score of 245/100 (Critical). A score this high marks a critical threat actor. This address has demonstrated persistent, aggressive malicious behavior across multiple detection vectors.
The following attack categories were identified:
📊 Threat Analysis
Network traffic from 74.234.98.46, located in Dublin, Ireland, operating on the network of Microsoft Corporation, has been classified as malicious by our automated threat scoring engine. Over a period of 1 days, this IP generated 2 malicious requests, averaging approximately 2 requests per day. Operating from datacenter infrastructure, this IP is typical of addresses used in organized attack operations. Cloud and VPS providers are commonly exploited as launching platforms for automated scanning. Two attack patterns were identified (User-Agent Anomaly and Request Flooding), suggesting a semi-automated campaign that targets multiple vulnerabilities. Ireland currently accounts for 101 blocked IPs in our database, making it a significant source of malicious traffic. With a threat score of 245/100, 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
💡 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.
💡 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.