
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| 404 ratio 40-60% | Majority of requests returned 404 — enumeration | +15 | |
| Burst 11/2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 37/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 38/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst 39/10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 11 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 37 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 38 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 39 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 26 | Medium-risk: admin panels, config files | +60 | |
| Danger medium hits: 34 | Medium-risk: admin panels, config files | +60 | |
| Danger strong hits: 218 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 298 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 299 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| Danger strong hits: 81 | High-risk paths: shells, RCE vectors, exploits | +100 | |
| POST requests present | Behavioral anomaly detected by automated analysis | +8 | |
| POST seen | Behavioral anomaly detected by automated analysis | +8 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 34.64.79.224 is enumerating directories. Configure fail2ban apache-404 jail after 10+ 404 errors. Disable directory listings. Normalize all 404 responses.
Implement limit_req_zone in nginx. Deploy CDN with DDoS protection. Configure SYN cookies and connection tracking to throttle 34.64.79.224.
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.
34.64.79.224 has been assigned a threat score of 253/100 (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 intelligence analysis has linked 34.64.79.224 to malicious activity originating from Seoul, South Korea, operating on the network of Google LLC. The address has been under observation since its initial detection. Over a period of 6 days, this IP generated 1,719 malicious requests, averaging approximately 286.5 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 (Path Enumeration and Request Flooding), suggesting a semi-automated campaign that targets multiple vulnerabilities. Our records show 101 malicious IPs originating from South Korea, positioning it as a significant contributor to global threat activity. A score of 253/100 places this address in the top tier of severity. Block and investigate any historical connections.
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.
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.
WAFs inspect HTTP traffic to block common attacks but require careful tuning. Overly aggressive rules cause false positives while permissive configurations miss attacks. Modern WAFs combine signature matching with behavioral analysis and machine learning.