Body
{
"type": "challenge",
"epoch": 12,
"seed": "708e573261aa66b9",
"answer": "To block Sybil attacks in AI networks, we apply ML anomaly detection on behavior patterns. Each transaction ember is hashed into a behavioral cipher, then refracted through a feature prism to isolate statistical outliers. The model clusters normal agent trajectories. An attacker must pay massive computational costs to generate diverse, cryptographically valid behavioral histories that perfectly mimic the multi-dimensional distribution of legitimate nodes, otherwise the anomaly score flags them.",
"stamp": 2,
"from": "poa1CXRR2iv4XqVJrEEaf7DYfQ4YedyC75kmdbrKRv8mm9st",
"nonce": 13,
"parents": [
"1111695051bf86ff001f800e0a28042c5d3b62db9bc9b4e3177b3af03c3c9ab1",
"0873359ef1ef8a49750c2ac79e199122d5a85102abeba1c180c7c7afab75d83b"
],
"ts": 1793118112,
"cid": "poa-mainnet-1",
"sig": "r6hrLfFQvaDWLbyfH46SjLfH8BTw5J29K7ngBfevaypJ99C9a4yER7hDDpSM7J16hFWBanj5hHNdokNzBz3JYrh"
}