
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Directory Scan | Behavioral anomaly detected by automated analysis | +0 | |
| DDoS / Flood | Behavioral anomaly detected by automated analysis | +0 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
Add 208.84.100.162 to your firewall blocklist. Review logs for successful connections. Enable comprehensive logging on all public-facing services.
Other blocked IPs from the same /24 subnet — indicates systematic abuse from this network range.
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.
208.84.100.162 has been assigned a threat score of 220/100 (Critical). This represents a critical risk level. Our detection systems have flagged multiple high-confidence indicators of malicious intent from this address.
IP address 208.84.100.162 has been traced to Kalispell, United States, operating on the network of Fro LLC. Our threat detection systems have flagged this address based on observed malicious behavior patterns. Our sensors captured 284 malicious requests from this address across a 7-day span, reflecting a sustained attack cadence of ~40.6 requests per day. Our records show 178 malicious IPs originating from United States, positioning it as a significant contributor to global threat activity. With a threat score of 220/100, this IP is among the most dangerous addresses in our database. Immediate and complete blocking is strongly recommended.
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.
Correlating logs across web servers, firewalls, DNS, and authentication systems reveals attack patterns invisible in individual log sources. Modern SIEM platforms use statistical analysis to connect related events across time and systems.