#!/usr/bin/env python3
"""
Attack Simulation Demo Script

This script demonstrates the extended threat model attack simulations
with detailed output and analysis.
"""

import random
from typing import Any

import trio


class AttackSimulationDemo:
    """Demo class for running attack simulations"""

    def __init__(self):
        self.results: dict[str, Any] = {}

    async def demo_invalid_block_attack(self) -> dict[str, Any]:
        """Demonstrate invalid block propagation attack"""
        print("INVALID BLOCK PROPAGATION ATTACK")
        print("=" * 50)
        print("Target: Light clients accepting authentic but invalid blocks")
        print("Insight: Authenticity != Integrity")
        print()

        # Simulate attack parameters
        num_light_clients = 10
        num_full_nodes = 5
        num_validators = 2

        print("Network Composition:")
        print(f"   Light clients: {num_light_clients}")
        print(f"   Full nodes: {num_full_nodes}")
        print(f"   Malicious validators: {num_validators}")
        print()

        # Simulate attack execution
        print("Executing attack...")
        await trio.sleep(0.2)

        # Simulate results
        light_client_acceptance = random.uniform(0.6, 0.9)
        full_node_acceptance = random.uniform(0.1, 0.4)
        vulnerability_gap = light_client_acceptance - full_node_acceptance

        print("Attack Results:")
        print(f"   Light client acceptance: {light_client_acceptance:.1%}")
        print(f"   Full node acceptance: {full_node_acceptance:.1%}")
        print(f"   Vulnerability gap: {vulnerability_gap:.1%}")
        print()

        # Generate insights
        if vulnerability_gap > 0.4:
            print("CRITICAL: High vulnerability gap detected!")
            print("   Light clients are significantly more vulnerable")
        elif vulnerability_gap > 0.2:
            print("WARNING: Moderate vulnerability gap")
        else:
            print("Good: Low vulnerability gap")

        print()

        return {
            "attack_type": "invalid_block_propagation",
            "light_client_acceptance": light_client_acceptance,
            "full_node_acceptance": full_node_acceptance,
            "vulnerability_gap": vulnerability_gap,
            "severity": "critical"
            if vulnerability_gap > 0.4
            else "moderate"
            if vulnerability_gap > 0.2
            else "low",
        }

    async def demo_bootnode_poisoning_attack(self) -> dict[str, Any]:
        """Demonstrate bootnode poisoning attack"""
        print("BOOTNODE POISONING ATTACK")
        print("=" * 50)
        print("Target: Node discovery through compromised bootnodes")
        print("Insight: All bootnodes compromised -> permanent isolation")
        print()

        # Simulate attack parameters
        num_honest_peers = 20
        num_malicious_bootnodes = 3
        num_fallback_peers = 5

        print("Network Composition:")
        print(f"   Honest peers: {num_honest_peers}")
        print(f"   Malicious bootnodes: {num_malicious_bootnodes}")
        print(f"   Fallback peers: {num_fallback_peers}")
        print()

        # Simulate attack execution
        print("Executing attack...")
        await trio.sleep(0.2)

        # Simulate results
        isolation_rate = random.uniform(0.3, 0.8)
        recovery_rate = random.uniform(0.2, 0.7)
        permanent_isolation = isolation_rate * 0.6

        print("Attack Results:")
        print(f"   Isolation rate: {isolation_rate:.1%}")
        print(f"   Recovery rate: {recovery_rate:.1%}")
        print(f"   Permanent isolation: {permanent_isolation:.1%}")
        print()

        # Generate insights
        if isolation_rate > 0.6:
            print("CRITICAL: High isolation rate!")
            print("   Many peers are isolated from honest network")
        elif isolation_rate > 0.3:
            print("WARNING: Moderate isolation rate")
        else:
            print("Good: Low isolation rate")

        if recovery_rate < 0.3:
            print("CRITICAL: Low recovery rate!")
            print("   Isolated peers struggle to reconnect")

        print()

        return {
            "attack_type": "bootnode_poisoning",
            "isolation_rate": isolation_rate,
            "recovery_rate": recovery_rate,
            "permanent_isolation_rate": permanent_isolation,
            "severity": "critical"
            if isolation_rate > 0.6
            else "moderate"
            if isolation_rate > 0.3
            else "low",
        }

    async def demo_finality_stall_attack(self) -> dict[str, Any]:
        """Demonstrate finality stall attack"""
        print("FINALITY STALL ATTACK")
        print("=" * 50)
        print("Target: Light client memory exhaustion during finality stalls")
        print("Insight: Halted finality -> unbounded memory growth")
        print()

        # Simulate attack parameters
        num_light_clients = 8
        num_attackers = 2
        stall_duration = 4.0

        print("Network Composition:")
        print(f"   Light clients: {num_light_clients}")
        print(f"   Attackers: {num_attackers}")
        print(f"   Stall duration: {stall_duration}s")
        print()

        # Simulate attack execution
        print("Executing attack...")
        await trio.sleep(0.2)

        # Simulate results
        exhaustion_rate = random.uniform(0.2, 0.7)
        peak_memory = random.uniform(500, 2000)
        timeout_detection = random.uniform(0.4, 0.9)

        print("Attack Results:")
        print(f"   Memory exhaustion rate: {exhaustion_rate:.1%}")
        print(f"   Peak memory usage: {peak_memory:.1f} MB")
        print(f"   Timeout detection rate: {timeout_detection:.1%}")
        print()

        # Generate insights
        if exhaustion_rate > 0.5:
            print("CRITICAL: High memory exhaustion!")
            print("   Many light clients exhausted memory")
        elif exhaustion_rate > 0.3:
            print("WARNING: Moderate memory exhaustion")
        else:
            print("Good: Low memory exhaustion")

        if timeout_detection < 0.7:
            print("WARNING: Low timeout detection rate")
            print("   Light clients struggle to detect stalls")

        print()

        return {
            "attack_type": "finality_stall",
            "exhaustion_rate": exhaustion_rate,
            "peak_memory_mb": peak_memory,
            "timeout_detection_rate": timeout_detection,
            "severity": "critical"
            if exhaustion_rate > 0.5
            else "moderate"
            if exhaustion_rate > 0.3
            else "low",
        }

    async def demo_long_range_fork_attack(self) -> dict[str, Any]:
        """Demonstrate long-range fork attack"""
        print("LONG-RANGE FORK ATTACK")
        print("=" * 50)
        print("Target: Offline nodes accepting stale chain views")
        print("Insight: Long offline periods -> fork replay vulnerability")
        print()

        # Simulate attack parameters
        num_online_peers = 15
        num_offline_peers = 8
        num_fork_attackers = 2

        print("Network Composition:")
        print(f"   Online peers: {num_online_peers}")
        print(f"   Offline peers: {num_offline_peers}")
        print(f"   Fork attackers: {num_fork_attackers}")
        print()

        # Simulate attack execution
        print("Executing attack...")
        await trio.sleep(0.2)

        # Simulate results
        replay_success = random.uniform(0.1, 0.6)
        detection_rate = random.uniform(0.5, 0.9)
        resync_success = random.uniform(0.3, 0.8)

        print("Attack Results:")
        print(f"   Fork replay success: {replay_success:.1%}")
        print(f"   Detection rate: {detection_rate:.1%}")
        print(f"   Resync success: {resync_success:.1%}")
        print()

        # Generate insights
        if replay_success > 0.4:
            print("CRITICAL: High fork replay success!")
            print("   Many offline peers accept stale forks")
        elif replay_success > 0.2:
            print("WARNING: Moderate fork replay success")
        else:
            print("Good: Low fork replay success")

        if detection_rate < 0.7:
            print("WARNING: Low detection rate")
            print("   Network struggles to detect stale forks")

        print()

        return {
            "attack_type": "long_range_fork",
            "replay_success_rate": replay_success,
            "detection_rate": detection_rate,
            "resync_success_rate": resync_success,
            "severity": "critical"
            if replay_success > 0.4
            else "moderate"
            if replay_success > 0.2
            else "low",
        }

    async def run_all_demos(self):
        """Run all attack simulation demos"""
        print("EXTENDED THREAT MODEL DEMONSTRATION")
        print("=" * 60)
        print("Polkadot/Smoldot-inspired security research")
        print("Testing network resilience under adversarial conditions")
        print()

        # Run all attack demos
        self.results["invalid_block"] = await self.demo_invalid_block_attack()
        self.results["bootnode_poisoning"] = await self.demo_bootnode_poisoning_attack()
        self.results["finality_stall"] = await self.demo_finality_stall_attack()
        self.results["long_range_fork"] = await self.demo_long_range_fork_attack()

        # Generate comprehensive summary
        self.generate_summary()

    def generate_summary(self):
        """Generate comprehensive attack summary"""
        print("COMPREHENSIVE ATTACK ANALYSIS")
        print("=" * 60)

        # Count severity levels
        severity_counts = {"critical": 0, "moderate": 0, "low": 0}
        for result in self.results.values():
            severity_counts[result["severity"]] += 1

        print("Attack Severity Distribution:")
        print(f"   Critical: {severity_counts['critical']}")
        print(f"   Moderate: {severity_counts['moderate']}")
        print(f"   Low: {severity_counts['low']}")
        print()

        # Detailed results
        print("Detailed Attack Results:")
        for attack_name, result in self.results.items():
            attack_display = attack_name.replace("_", " ").title()
            severity_label = {
                "critical": "CRITICAL",
                "moderate": "MODERATE",
                "low": "LOW",
            }
            [result["severity"]]

            print(f"   [{severity_label}] {attack_display}:")

            # Show key metrics based on attack type
            if "vulnerability_gap" in result:
                print(f"      Vulnerability gap: {result['vulnerability_gap']:.1%}")
            if "isolation_rate" in result:
                print(f"      Isolation rate: {result['isolation_rate']:.1%}")
            if "exhaustion_rate" in result:
                print(f"      Memory exhaustion: {result['exhaustion_rate']:.1%}")
            if "replay_success_rate" in result:
                print(f"      Fork replay success: {result['replay_success_rate']:.1%}")

        print()

        # Overall assessment
        if severity_counts["critical"] > 0:
            print("OVERALL ASSESSMENT: CRITICAL VULNERABILITIES DETECTED")
            print("   Immediate attention required for network security")
        elif severity_counts["moderate"] > 0:
            print("OVERALL ASSESSMENT: MODERATE VULNERABILITIES DETECTED")
            print("   Proactive measures recommended")
        else:
            print("OVERALL ASSESSMENT: GOOD SECURITY POSTURE")
            print("   Network shows resilience against tested attacks")


async def main():
    """Main demo function"""
    demo = AttackSimulationDemo()
    await demo.run_all_demos()


if __name__ == "__main__":
    # Run the demo using trio
    trio.run(main)