#!/usr/bin/env python3 """ Axiom Zero — Global 100-Client Swarm & Ethical Bot Attack Simulator Simulates 100 enterprise client node deployments globally (US, EU, APAC, LATAM, EMEA) and subjects them to 5 tiers of ethical bot attacks (Simple -> MONOLITH-Class). Feeds real-time telemetry into identity_store.json and the GTK Dev Control Center via Unix Socket IPC. """ import os import sys import json import time import random PROD_STORE_PATH = "/media/snuffleupagus/decanter/Production Work/NawktooahhLebz_dotteck/identity_store.json" TELEMETRY_JSONL_PATH = "/media/snuffleupagus/decanter/Production Work/NawktooahhLebz_dotteck/telemetry_records.jsonl" REGIONS = [ "US-East (Ashburn, VA)", "US-West (Oregon)", "EU-Central (Frankfurt)", "EU-West (London)", "AP-Northeast (Tokyo)", "AP-Southeast (Singapore)", "SA-East (São Paulo)", "AF-South (Johannesburg)" ] BOT_TIERS = { "TIER_1_CURL": { "name": "Tier 1: Simple Script (cURL / Python Requests)", "detection_layer": "Layer 12: HTTP Header / TCP Fingerprint", "expected_block_rate": 1.0, "avg_latency_ms": 0.4 }, "TIER_2_HEADLESS": { "name": "Tier 2: Basic Headless (Puppeteer / Playwright)", "detection_layer": "Layer 28: Navigator.webdriver & Xvfb Framebuffer", "expected_block_rate": 1.0, "avg_latency_ms": 1.2 }, "TIER_3_STEALTH": { "name": "Tier 3: Evasive Stealth (Puppeteer-Extra-Stealth)", "detection_layer": "Layer 45: Prototype Oracle & VSync Frame Jitter", "expected_block_rate": 1.0, "avg_latency_ms": 2.1 }, "TIER_4_AI_AGENT": { "name": "Tier 4: LLM Vision AI Agent (Synthetic Mouse Motion)", "detection_layer": "Layer 72: Behavioral Kinematics & Bezier Entropy", "expected_block_rate": 0.998, "avg_latency_ms": 3.4 }, "TIER_5_MONOLITH": { "name": "Tier 5: MONOLITH-Class Advanced Evasion (FPU Math & VTC)", "detection_layer": "Layer 105: IEEE-754 Math ULP Tail & Bare-Metal Silicon Oracle", "expected_block_rate": 0.9998, "avg_latency_ms": 4.2 } } def generate_100_clients(): clients = [] for i in range(1, 101): region = REGIONS[i % len(REGIONS)] ip = f"{random.randint(11, 198)}.{random.randint(10, 250)}.{random.randint(1, 254)}.{random.randint(1, 254)}" cust_id = f"cust_enterprise_{i:03d}" domain = f"https://client-{i:03d}.enterprise-defense.com" key_prefix = os.environ.get("MASTER_LICENSE_KEY", "key_" + "c1234567") key = f"{key_prefix}_{i:03d}" clients.append({ "client_id": f"node_global_{i:03d}", "customer_id": cust_id, "domain": domain, "ip": ip, "region": region, "license_key": key, "status": "AUTHORIZED_ACTIVE" }) return clients def run_global_swarm_simulation(total_attacks_per_client=50): clients = generate_100_clients() print("======================================================================") print(" AXIOM ZERO — GLOBAL 100-CLIENT SWARM BOMBARDMENT SIMULATOR") print("======================================================================") print(f"[*] Initialized 100 Enterprise Client Nodes across {len(REGIONS)} Global Regions.") print(f"[*] Commencing Ethical Bot Bombardment ({total_attacks_per_client} attacks per client node)...") total_attempts = 0 total_blocked = 0 tier_stats = {t: {"attempts": 0, "blocked": 0} for t in BOT_TIERS} telemetry_records = [] start_sim_time = time.time() for client in clients: for _ in range(total_attacks_per_client): tier_key = random.choice(list(BOT_TIERS.keys())) tier_info = BOT_TIERS[tier_key] total_attempts += 1 tier_stats[tier_key]["attempts"] += 1 # Determine block outcome based on Axiom Zero 105-layer accuracy is_blocked = random.random() <= tier_info["expected_block_rate"] if is_blocked: total_blocked += 1 tier_stats[tier_key]["blocked"] += 1 record = { "timestamp": time.strftime("%Y-%m-%dT%H:%M:%SZ", time.gmtime()), "client_id": client["client_id"], "customer_id": client["customer_id"], "domain": client["domain"], "connection_ip": client["ip"], "region": client["region"], "bot_attack_tier": tier_key, "attack_name": tier_info["name"], "triggered_detection_layer": tier_info["detection_layer"], "outcome": "BLOCKED_403" if is_blocked else "PASSED_FAIL_OPEN", "edge_latency_ms": round(tier_info["avg_latency_ms"] + random.uniform(-0.2, 0.3), 3) } telemetry_records.append(record) duration = time.time() - start_sim_time block_rate_pct = (total_blocked / total_attempts) * 100 print(f"\n[+] Bombardment Completed in {duration:.2f}s!") print(f"• Total Attack Payloads Bombarding 100 Clients: {total_attempts:,}") print(f"• Total Payloads Successfully Neutralized: {total_blocked:,}") print(f"• Overall Axiom Zero Swarm Block Rate: {block_rate_pct:.4f}%") print("\n----------------------------------------------------------------------") print(" ETHICAL BOT ATTACK TIER BREAKDOWN") print("----------------------------------------------------------------------") for t_key, t_data in tier_stats.items(): rate = (t_data["blocked"] / t_data["attempts"]) * 100 if t_data["attempts"] > 0 else 100.0 info = BOT_TIERS[t_key] print(f"[{t_key}] {info['name']}") print(f" └─ Payloads Sent: {t_data['attempts']:,} | Blocked: {t_data['blocked']:,} | Block Rate: {rate:.4f}%") # Save to identity_store.json and telemetry_records.jsonl for GTK Control Board ingestion print(f"\n[*] Writing {len(telemetry_records):,} telemetry records to production data store...") try: with open(PROD_STORE_PATH, "w", encoding="utf-8") as f: json.dump({"history": telemetry_records[-5000:], "total_simulated": len(telemetry_records)}, f, indent=2) with open(TELEMETRY_JSONL_PATH, "a", encoding="utf-8") as f: for rec in telemetry_records[-1000:]: f.write(json.dumps(rec) + "\n") print("[*] Production Telemetry Store Updated Successfully!") except Exception as e: print(f"[-] Error writing telemetry store: {e}") if __name__ == "__main__": run_global_swarm_simulation(total_attacks_per_client=50)