
ABUSE.MOM — BEHAVE OR GET EXPOSED
| Signature | Description | Points | Severity |
|---|---|---|---|
| Danger strong hits: 1 | High-risk paths: shells, RCE vectors, exploits | +25 | |
| Danger medium hits: 19 | Medium-risk: admin panels, config files | +60 | |
| 404 ratio >= 60% | Majority of requests returned 404 — enumeration | +25 | |
| Burst: 8 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 20 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 11 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 21 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 19 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 13 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 17 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 9 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 16 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 20 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 18 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 15 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Danger medium hits: 2 | Medium-risk: admin panels, config files | +20 | |
| Burst: 18 req / 10s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 10 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 14 req / 2s | Abnormally fast request rate — automated scanning | +35 | |
| Burst: 12 req / 2s | Abnormally fast request rate — automated scanning | +35 |
Reconstructed HTTP requests from server access logs. Target domains redacted for security.
* Typical request patterns for detected signatures. Actual target domains are redacted.
IP 98.94.108.79 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 98.94.108.79.
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.
98.94.108.79 has been assigned a threat score of 180/100 (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:
Our monitoring infrastructure has identified 98.94.108.79, geolocated to Ashburn, United States, operating on the network of Amazon.com, as a source of suspicious network activity. During its 1-day observation window, we recorded 45 hostile requests from this IP — roughly 45 per day on average. 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. United States currently accounts for 247 blocked IPs in our database, making it a significant source of malicious traffic. At 180/100, this is an extremely high-risk address. All traffic should be considered hostile.
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.
Credential stuffing uses stolen username-password pairs from data breaches to attempt logins across many websites. Since users frequently reuse passwords, these automated attacks achieve success rates of 0.1-2%, which translates to thousands of compromised accounts from millions of attempts.
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.