bloom vs cuckoo

gistfile1.txt

Use Bloom Filters when:Distributed threat intelligence

Multiple sensors collect malicious IPs/domains independently
Merge all filters at central point → unified blocklist
Classic use case: collaborative malware detection across organizations
Threat feed aggregation

Combine feeds from different vendors
Each vendor provides a Bloom filter of known bad hashes/URLs
OR them together for comprehensive coverage
Log aggregation & correlation

Different security tools (IDS, firewall, proxy) maintain filters
Periodically merge to detect patterns across systems
Example: "Has this IP been seen by ANY security device?"
Use Cuckoo Filters when:Dynamic blocklists/allowlists

Need to remove indicators when they're no longer malicious
Example: IP temporarily compromised, then cleaned
False positive remediation without rebuilding
Real-time packet filtering

Router/firewall edge devices with strict memory constraints
Better space efficiency for single-node filtering
Fast lookups with bounded worst-case time
Short-lived threat data

IOCs (Indicators of Compromise) with expiration
Can delete old entries as they age out
DDoS mitigation where attacker IPs rotate

Comparison: RADIOACTIVE vs Modern Antivirus

Feature	Traditional AV	RADIOACTIVE (Current)	RADIOACTIVE (With ML)
Signature detection	✅ Malware hashes	✅ Model fingerprints	✅ Model fingerprints
Heuristic analysis	✅ Behavioral rules	❌ None	✅ File structure rules
ML detection	✅ Random Forest, Deep Learning	❌ None	✅ Model-like file classifier
Behavioral monitoring	✅ Process/network monitoring	❌ None	✅ File+network correlation
Cloud reputation	✅ Crowd-sourced threat intel	❌ None	✅ Continuous learning
Incremental scanning	✅ Background daemon	❌ Manual scans	✅ Filesystem watching
Zero-day detection	✅ (via ML)	❌ (signature only)	✅ (via ML + heuristics)