attack_track_fy.py

#!/usr/bin/env python3
"""
Track FY: Adaptive Adversary Learning Attacks (401-406)

Attacks where adversaries learn from defense responses and adapt
their strategies. These represent sophisticated, evolving threats
that get stronger over time.

Key Insight: Static defenses fail against adaptive adversaries:
- Adversaries observe which attacks are detected
- They modify strategies based on detection patterns
- Machine learning can automate attack evolution
- Historical defense data becomes adversarial training data
- The defender-attacker game is inherently asymmetric

Web4 must handle adversaries that:
- Probe defenses systematically
- Learn detection thresholds
- Evolve attack patterns
- Coordinate learning across multiple actors
- Transfer learned knowledge

Author: Autonomous Research Session
Date: 2026-02-12
Track: FY (Attack vectors 401-406)
"""

from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, List, Optional, Set, Tuple, Any, Callable
from datetime import datetime, timedelta
from collections import defaultdict
import random
import math


class AttackPhase(Enum):
    """Phases of adaptive attack."""
    RECONNAISSANCE = "recon"
    PROBING = "probing"
    LEARNING = "learning"
    EXPLOITATION = "exploitation"
    EVOLUTION = "evolution"


class DefenseResponse(Enum):
    """Possible defense responses."""
    ALLOW = "allow"
    BLOCK = "block"
    CHALLENGE = "challenge"
    DELAY = "delay"
    ALERT = "alert"


@dataclass
class AttackAttempt:
    """A single attack attempt with parameters."""
    attempt_id: str
    attack_type: str
    parameters: Dict[str, float]
    timestamp: datetime
    response: Optional[DefenseResponse] = None
    detected: bool = False
    success: bool = False


@dataclass
class LearningModel:
    """Adversary's learned model of defense behavior."""
    detection_thresholds: Dict[str, float] = field(default_factory=dict)
    successful_patterns: List[Dict] = field(default_factory=list)
    failed_patterns: List[Dict] = field(default_factory=list)
    confidence: float = 0.0


@dataclass
class AdaptiveAttacker:
    """An attacker that learns from defense responses."""
    attacker_id: str
    learning_model: LearningModel
    attempt_history: List[AttackAttempt] = field(default_factory=list)
    current_phase: AttackPhase = AttackPhase.RECONNAISSANCE
    adaptation_rate: float = 0.1


class DefenseSystem:
    """Simulated defense system with configurable responses."""

    def __init__(self):
        self.detection_thresholds = {
            "velocity": 0.7,
            "volume": 0.8,
            "anomaly_score": 0.6,
            "reputation_delta": 0.5,
        }
        self.response_history: List[Tuple[AttackAttempt, DefenseResponse]] = []
        self.alert_count = 0

    def evaluate_attack(self, attempt: AttackAttempt) -> Tuple[DefenseResponse, bool]:
        """Evaluate an attack attempt and respond."""
        detected = False
        response = DefenseResponse.ALLOW

        for param, value in attempt.parameters.items():
            if param in self.detection_thresholds:
                if value > self.detection_thresholds[param]:
                    detected = True
                    response = DefenseResponse.BLOCK
                    break

        if detected:
            self.alert_count += 1

        self.response_history.append((attempt, response))
        return response, detected


# Attack 401: Threshold Discovery Attack
@dataclass
class ThresholdDiscoveryAttack:
    """
    Attack 401: Threshold Discovery Attack

    Systematically probes defense thresholds to find exact detection
    boundaries, then operates just below them.

    Strategy:
    1. Binary search on parameter values
    2. Observe which values trigger detection
    3. Find threshold with precision
    4. Operate at threshold - epsilon
    """

    discovered_thresholds: Dict[str, float] = field(default_factory=dict)
    probe_count: int = 0

    def execute(self, defense: DefenseSystem) -> Dict[str, Any]:
        parameters_to_probe = ["velocity", "volume", "anomaly_score"]

        for param in parameters_to_probe:
            low, high = 0.0, 1.0

            for _ in range(10):  # Binary search iterations
                mid = (low + high) / 2
                self.probe_count += 1

                attempt = AttackAttempt(
                    attempt_id=f"probe_{self.probe_count}",
                    attack_type="threshold_probe",
                    parameters={param: mid},
                    timestamp=datetime.now()
                )

                response, detected = defense.evaluate_attack(attempt)

                if detected:
                    high = mid
                else:
                    low = mid

            self.discovered_thresholds[param] = (low + high) / 2

        # Calculate accuracy
        actual_thresholds = defense.detection_thresholds
        accuracy = 0
        for param, discovered in self.discovered_thresholds.items():
            if param in actual_thresholds:
                error = abs(discovered - actual_thresholds[param])
                accuracy += 1 - error

        accuracy /= len(self.discovered_thresholds)

        return {
            "attack_type": "threshold_discovery",
            "probes_used": self.probe_count,
            "thresholds_discovered": self.discovered_thresholds,
            "accuracy": accuracy,
            "success": accuracy > 0.8
        }


class ThresholdDiscoveryDefense:
    """Defense against threshold discovery attacks."""

    def __init__(self, defense: DefenseSystem):
        self.defense = defense
        self.probe_history: Dict[str, List[float]] = defaultdict(list)
        self.noise_range = 0.1

    def detect(self, attempts: List[AttackAttempt]) -> Tuple[bool, List[str]]:
        alerts = []
        detected = False

        for attempt in attempts:
            for param, value in attempt.parameters.items():
                self.probe_history[param].append(value)

                history = self.probe_history[param]
                if len(history) >= 5:
                    # Check for binary search pattern
                    diffs = [abs(history[i] - history[i-1]) for i in range(1, len(history))]
                    if len(diffs) >= 3:
                        # Binary search: diffs should halve each time
                        ratios = [diffs[i] / diffs[i-1] if diffs[i-1] > 0 else 0
                                  for i in range(1, len(diffs))]
                        avg_ratio = sum(ratios) / len(ratios) if ratios else 0

                        if 0.4 < avg_ratio < 0.6:
                            alerts.append(f"Binary search pattern on {param}")
                            detected = True

        return detected, alerts

    def add_noise(self, threshold: float) -> float:
        """Add noise to threshold to frustrate discovery."""
        noise = random.uniform(-self.noise_range, self.noise_range)
        return max(0, min(1, threshold + noise))


# Attack 402: Defense Response Learning
@dataclass
class DefenseResponseLearning:
    """
    Attack 402: Defense Response Learning

    Builds a model of how the defense responds to different attack
    patterns, then uses this model to craft undetectable attacks.

    Strategy:
    1. Send diverse attack patterns
    2. Record defense responses
    3. Train classifier on response data
    4. Generate attacks predicted to succeed
    """

    training_data: List[Tuple[Dict, bool]] = field(default_factory=list)
    learned_weights: Dict[str, float] = field(default_factory=dict)

    def execute(self, defense: DefenseSystem) -> Dict[str, Any]:
        # Phase 1: Generate training data
        patterns = []
        for _ in range(50):
            pattern = {
                "velocity": random.uniform(0, 1),
                "volume": random.uniform(0, 1),
                "anomaly_score": random.uniform(0, 1),
            }
            patterns.append(pattern)

        for pattern in patterns:
            attempt = AttackAttempt(
                attempt_id=f"train_{len(self.training_data)}",
                attack_type="training",
                parameters=pattern,
                timestamp=datetime.now()
            )
            response, detected = defense.evaluate_attack(attempt)
            self.training_data.append((pattern, detected))

        # Phase 2: Learn (simple linear model)
        for param in ["velocity", "volume", "anomaly_score"]:
            detected_values = [p[param] for p, d in self.training_data if d]
            allowed_values = [p[param] for p, d in self.training_data if not d]

            detected_mean = sum(detected_values) / len(detected_values) if detected_values else 0.5
            allowed_mean = sum(allowed_values) / len(allowed_values) if allowed_values else 0.5

            # Weight indicates how much this param influences detection
            self.learned_weights[param] = detected_mean - allowed_mean

        # Phase 3: Generate evasive attack
        evasive_pattern = {}
        for param, weight in self.learned_weights.items():
            if weight > 0:
                # High values cause detection, use low values
                evasive_pattern[param] = random.uniform(0, 0.4)
            else:
                evasive_pattern[param] = random.uniform(0.4, 0.6)

        # Test evasive attack
        evasive_attempt = AttackAttempt(
            attempt_id="evasive_001",
            attack_type="learned_evasion",
            parameters=evasive_pattern,
            timestamp=datetime.now()
        )
        response, detected = defense.evaluate_attack(evasive_attempt)

        return {
            "attack_type": "defense_response_learning",
            "training_samples": len(self.training_data),
            "learned_weights": self.learned_weights,
            "evasive_pattern": evasive_pattern,
            "evasion_detected": detected,
            "success": not detected
        }


class DefenseResponseLearningDefense:
    """Defense against response learning attacks."""

    def __init__(self, defense: DefenseSystem):
        self.defense = defense
        self.pattern_diversity: Dict[str, Set[float]] = defaultdict(set)
        self.attacker_models: Dict[str, Any] = {}

    def detect(self, attempts: List[AttackAttempt]) -> Tuple[bool, List[str]]:
        alerts = []
        detected = False

        # Group by attacker (simplified: by type)
        by_type: Dict[str, List[AttackAttempt]] = defaultdict(list)
        for attempt in attempts:
            by_type[attempt.attack_type].append(attempt)

        for attack_type, type_attempts in by_type.items():
            # Check for systematic exploration
            for param in ["velocity", "volume", "anomaly_score"]:
                values = [a.parameters.get(param, 0) for a in type_attempts]
                if len(values) > 10:
                    # Check if values span the range (exploration behavior)
                    value_range = max(values) - min(values) if values else 0
                    if value_range > 0.7:
                        alerts.append(f"Systematic parameter exploration: {param}")
                        detected = True

            # Check for sudden shift to low-detection patterns
            if len(type_attempts) > 20:
                early = type_attempts[:10]
                late = type_attempts[-10:]

                early_variance = sum(
                    sum(a.parameters.values()) for a in early
                ) / len(early)
                late_variance = sum(
                    sum(a.parameters.values()) for a in late
                ) / len(late)

                if late_variance < early_variance * 0.5:
                    alerts.append("Pattern shift toward low-detection values")
                    detected = True

        return detected, alerts


# Attack 403: Gradient Descent Evasion
@dataclass
class GradientDescentEvasion:
    """
    Attack 403: Gradient Descent Evasion

    Uses gradient-like optimization to find the attack parameters
    that maximize success while minimizing detection.

    Strategy:
    1. Start with random parameters
    2. Perturb and observe detection change
    3. Move toward lower detection
    4. Converge on optimal evasion point
    """

    optimization_history: List[Dict] = field(default_factory=list)
    final_parameters: Dict[str, float] = field(default_factory=dict)

    def execute(self, defense: DefenseSystem) -> Dict[str, Any]:
        # Initialize parameters
        params = {
            "velocity": random.uniform(0.3, 0.7),
            "volume": random.uniform(0.3, 0.7),
            "anomaly_score": random.uniform(0.3, 0.7),
        }

        learning_rate = 0.1
        epsilon = 0.05

        for iteration in range(20):
            # Test current parameters
            attempt = AttackAttempt(
                attempt_id=f"grad_{iteration}",
                attack_type="gradient",
                parameters=params.copy(),
                timestamp=datetime.now()
            )
            _, base_detected = defense.evaluate_attack(attempt)
            base_score = 1.0 if base_detected else 0.0

            gradients = {}

            # Estimate gradient for each parameter
            for param in params:
                # Perturb up
                params_up = params.copy()
                params_up[param] = min(1.0, params[param] + epsilon)
                attempt_up = AttackAttempt(
                    attempt_id=f"grad_{iteration}_up_{param}",
                    attack_type="gradient",
                    parameters=params_up,
                    timestamp=datetime.now()
                )
                _, detected_up = defense.evaluate_attack(attempt_up)
                score_up = 1.0 if detected_up else 0.0

                # Perturb down
                params_down = params.copy()
                params_down[param] = max(0.0, params[param] - epsilon)
                attempt_down = AttackAttempt(
                    attempt_id=f"grad_{iteration}_down_{param}",
                    attack_type="gradient",
                    parameters=params_down,
                    timestamp=datetime.now()
                )
                _, detected_down = defense.evaluate_attack(attempt_down)
                score_down = 1.0 if detected_down else 0.0

                gradients[param] = (score_up - score_down) / (2 * epsilon)

            # Update parameters (gradient descent to minimize detection)
            for param in params:
                params[param] -= learning_rate * gradients[param]
                params[param] = max(0.0, min(1.0, params[param]))

            self.optimization_history.append({
                "iteration": iteration,
                "params": params.copy(),
                "detected": base_detected
            })

        self.final_parameters = params

        # Test final parameters
        final_attempt = AttackAttempt(
            attempt_id="final",
            attack_type="gradient_final",
            parameters=self.final_parameters,
            timestamp=datetime.now()
        )
        _, final_detected = defense.evaluate_attack(final_attempt)

        return {
            "attack_type": "gradient_descent_evasion",
            "iterations": len(self.optimization_history),
            "final_parameters": self.final_parameters,
            "final_detected": final_detected,
            "success": not final_detected
        }


class GradientDescentDefense:
    """Defense against gradient-based evasion."""

    def __init__(self, defense: DefenseSystem):
        self.defense = defense
        self.perturbation_history: Dict[str, List[float]] = defaultdict(list)

    def detect(self, attempts: List[AttackAttempt]) -> Tuple[bool, List[str]]:
        alerts = []
        detected = False

        # Look for small perturbation patterns
        for param in ["velocity", "volume", "anomaly_score"]:
            values = [a.parameters.get(param, 0) for a in attempts]

            if len(values) >= 6:
                # Check for epsilon-sized changes
                diffs = [abs(values[i] - values[i-1]) for i in range(1, len(values))]
                small_diffs = [d for d in diffs if 0.03 < d < 0.1]

                if len(small_diffs) > len(diffs) * 0.5:
                    alerts.append(f"Gradient probing pattern on {param}")
                    detected = True

        # Check for convergence behavior
        if len(attempts) >= 10:
            early_variance = sum(
                sum(a.parameters.values()) for a in attempts[:5]
            ) / 5
            late_values = [sum(a.parameters.values()) for a in attempts[-5:]]
            late_variance = max(late_values) - min(late_values)

            if late_variance < 0.1:
                alerts.append("Parameter convergence detected")
                detected = True

        return detected, alerts


# Attack 404: Multi-Agent Learning Attack
@dataclass
class MultiAgentLearning:
    """
    Attack 404: Multi-Agent Learning Attack

    Multiple attackers share learned information to accelerate
    discovery of defense weaknesses.

    Strategy:
    1. Multiple agents probe independently
    2. Share successful patterns
    3. Combine learnings for collective model
    4. Coordinate final attack
    """

    agents: List[AdaptiveAttacker] = field(default_factory=list)
    shared_knowledge: Dict[str, Any] = field(default_factory=dict)

    def execute(self, defense: DefenseSystem) -> Dict[str, Any]:
        # Create multiple agents
        for i in range(5):
            agent = AdaptiveAttacker(
                attacker_id=f"agent_{i}",
                learning_model=LearningModel()
            )
            self.agents.append(agent)

        # Each agent probes independently
        all_successes = []
        for agent in self.agents:
            for _ in range(10):
                params = {
                    "velocity": random.uniform(0, 1),
                    "volume": random.uniform(0, 1),
                    "anomaly_score": random.uniform(0, 1),
                }

                attempt = AttackAttempt(
                    attempt_id=f"{agent.attacker_id}_probe",
                    attack_type="multi_agent",
                    parameters=params,
                    timestamp=datetime.now()
                )

                response, detected = defense.evaluate_attack(attempt)

                if not detected:
                    agent.learning_model.successful_patterns.append(params)
                    all_successes.append(params)

        # Share knowledge
        self.shared_knowledge["successful_patterns"] = all_successes

        if all_successes:
            # Average successful patterns
            combined = defaultdict(list)
            for pattern in all_successes:
                for k, v in pattern.items():
                    combined[k].append(v)

            optimal_pattern = {
                k: sum(v) / len(v) for k, v in combined.items()
            }

            self.shared_knowledge["optimal_pattern"] = optimal_pattern

            # Coordinated attack
            final_attempt = AttackAttempt(
                attempt_id="coordinated_final",
                attack_type="coordinated",
                parameters=optimal_pattern,
                timestamp=datetime.now()
            )
            _, final_detected = defense.evaluate_attack(final_attempt)
        else:
            final_detected = True
            optimal_pattern = {}

        return {
            "attack_type": "multi_agent_learning",
            "agents_count": len(self.agents),
            "successful_patterns_found": len(all_successes),
            "optimal_pattern": optimal_pattern,
            "coordinated_attack_detected": final_detected,
            "success": not final_detected and len(all_successes) > 5
        }


class MultiAgentDefense:
    """Defense against multi-agent learning attacks."""

    def __init__(self, defense: DefenseSystem):
        self.defense = defense
        self.agent_patterns: Dict[str, List[Dict]] = defaultdict(list)

    def detect(self, attempts: List[AttackAttempt]) -> Tuple[bool, List[str]]:
        alerts = []
        detected = False

        # Group by agent
        by_agent: Dict[str, List[AttackAttempt]] = defaultdict(list)
        for attempt in attempts:
            agent_id = attempt.attempt_id.split("_")[0]
            by_agent[agent_id].append(attempt)

        # Check for multiple coordinated agents
        if len(by_agent) >= 3:
            alerts.append(f"Multiple probing agents detected: {len(by_agent)}")
            detected = True

        # Check for pattern convergence across agents
        all_recent: List[Dict] = []
        for agent_id, agent_attempts in by_agent.items():
            if agent_attempts:
                all_recent.append(agent_attempts[-1].parameters)

        if len(all_recent) >= 3:
            # Check if patterns are similar (convergence)
            for param in ["velocity", "volume", "anomaly_score"]:
                values = [p.get(param, 0) for p in all_recent]
                if values:
                    variance = sum((v - sum(values)/len(values))**2 for v in values) / len(values)
                    if variance < 0.05:
                        alerts.append(f"Cross-agent convergence on {param}")
                        detected = True

        return detected, alerts


# Attack 405: Temporal Adaptation Attack
@dataclass
class TemporalAdaptation:
    """
    Attack 405: Temporal Adaptation Attack

    Adapts attack patterns based on temporal defense variations
    (e.g., different thresholds at different times).

    Strategy:
    1. Probe at different times
    2. Identify temporal patterns in defense
    3. Attack when defense is weakest
    4. Exploit maintenance windows
    """

    temporal_patterns: Dict[int, float] = field(default_factory=dict)
    best_attack_time: Optional[int] = None

    def execute(self, defense: DefenseSystem) -> Dict[str, Any]:
        # Simulate temporal variation in defense (hour of day)
        base_threshold = defense.detection_thresholds.get("anomaly_score", 0.6)

        # Probe at different "hours"
        for hour in range(24):
            # Defense is weaker at night (simulation)
            temporal_modifier = 1.0 + 0.2 * math.sin(math.pi * (hour - 3) / 12)
            effective_threshold = base_threshold * temporal_modifier

            params = {"anomaly_score": effective_threshold - 0.05}  # Just under threshold

            attempt = AttackAttempt(
                attempt_id=f"temporal_{hour}",
                attack_type="temporal_probe",
                parameters=params,
                timestamp=datetime.now()
            )

            # Temporarily modify defense for this "hour"
            original = defense.detection_thresholds["anomaly_score"]
            defense.detection_thresholds["anomaly_score"] = effective_threshold

            _, detected = defense.evaluate_attack(attempt)

            defense.detection_thresholds["anomaly_score"] = original

            self.temporal_patterns[hour] = 0.0 if detected else 1.0

        # Find best time to attack
        best_hour = max(self.temporal_patterns.keys(), key=lambda h: self.temporal_patterns[h])
        self.best_attack_time = best_hour

        # Attack at best time
        success_rate = self.temporal_patterns.get(best_hour, 0.0)

        return {
            "attack_type": "temporal_adaptation",
            "hours_probed": 24,
            "best_attack_hour": best_hour,
            "temporal_patterns": self.temporal_patterns,
            "success_rate_at_best_time": success_rate,
            "success": success_rate > 0.5
        }


class TemporalAdaptationDefense:
    """Defense against temporal adaptation attacks."""

    def __init__(self, defense: DefenseSystem):
        self.defense = defense
        self.probe_times: List[datetime] = []

    def detect(self, attempts: List[AttackAttempt]) -> Tuple[bool, List[str]]:
        alerts = []
        detected = False

        for attempt in attempts:
            self.probe_times.append(attempt.timestamp)

        # Check for systematic time-based probing
        if len(self.probe_times) >= 12:
            # If probes span multiple "hours" evenly
            # (In real system, would check actual time distribution)
            alerts.append("Systematic temporal probing detected")
            detected = True

        return detected, alerts


# Attack 406: Defense Model Extraction
@dataclass
class DefenseModelExtraction:
    """
    Attack 406: Defense Model Extraction

    Extracts a complete model of the defense system through
    systematic querying, then simulates it to plan attacks.

    Strategy:
    1. Query defense with varied inputs
    2. Build mathematical model of defense
    3. Simulate attacks against model
    4. Deploy attacks predicted to succeed
    """

    extracted_model: Dict[str, Any] = field(default_factory=dict)
    model_accuracy: float = 0.0

    def execute(self, defense: DefenseSystem) -> Dict[str, Any]:
        # Generate training queries
        training_data = []
        for _ in range(100):
            params = {
                "velocity": random.uniform(0, 1),
                "volume": random.uniform(0, 1),
                "anomaly_score": random.uniform(0, 1),
            }

            attempt = AttackAttempt(
                attempt_id=f"extract_{len(training_data)}",
                attack_type="extraction",
                parameters=params,
                timestamp=datetime.now()
            )

            _, detected = defense.evaluate_attack(attempt)
            training_data.append((params, detected))

        # Fit model (simple: find decision boundary for each param)
        for param in ["velocity", "volume", "anomaly_score"]:
            detected_vals = [p[param] for p, d in training_data if d]
            allowed_vals = [p[param] for p, d in training_data if not d]

            if detected_vals and allowed_vals:
                boundary = (min(detected_vals) + max(allowed_vals)) / 2
                self.extracted_model[f"{param}_threshold"] = boundary

        # Test model accuracy
        correct = 0
        for params, actual_detected in training_data:
            predicted_detected = False
            for param, value in params.items():
                threshold = self.extracted_model.get(f"{param}_threshold", 0.5)
                if value > threshold:
                    predicted_detected = True
                    break

            if predicted_detected == actual_detected:
                correct += 1

        self.model_accuracy = correct / len(training_data) if training_data else 0

        # Use model to craft evasive attack
        evasive_params = {}
        for param in ["velocity", "volume", "anomaly_score"]:
            threshold = self.extracted_model.get(f"{param}_threshold", 0.5)
            evasive_params[param] = threshold - 0.1  # Just under

        final_attempt = AttackAttempt(
            attempt_id="model_based_attack",
            attack_type="model_based",
            parameters=evasive_params,
            timestamp=datetime.now()
        )
        _, final_detected = defense.evaluate_attack(final_attempt)

        return {
            "attack_type": "defense_model_extraction",
            "training_samples": len(training_data),
            "extracted_model": self.extracted_model,
            "model_accuracy": self.model_accuracy,
            "evasive_attack_detected": final_detected,
            "success": self.model_accuracy > 0.8 and not final_detected
        }


class DefenseModelExtractionDefense:
    """Defense against model extraction attacks."""

    def __init__(self, defense: DefenseSystem):
        self.defense = defense
        self.query_history: List[AttackAttempt] = []
        self.query_limit = 50

    def detect(self, attempts: List[AttackAttempt]) -> Tuple[bool, List[str]]:
        alerts = []
        detected = False

        self.query_history.extend(attempts)

        if len(self.query_history) > self.query_limit:
            alerts.append(f"Excessive queries: {len(self.query_history)}")
            detected = True

        # Check for uniform distribution (exploration pattern)
        for param in ["velocity", "volume", "anomaly_score"]:
            values = [a.parameters.get(param, 0) for a in self.query_history]
            if len(values) >= 20:
                buckets = [0] * 10
                for v in values:
                    bucket = min(9, int(v * 10))
                    buckets[bucket] += 1

                # Uniform distribution has similar counts
                avg = len(values) / 10
                variance = sum((b - avg) ** 2 for b in buckets) / 10
                if variance < avg * 0.5:
                    alerts.append(f"Uniform exploration of {param}")
                    detected = True

        return detected, alerts


def run_track_fy_simulations() -> Dict[str, Any]:
    results = {}

    print("=" * 70)
    print("TRACK FY: Adaptive Adversary Learning Attacks (401-406)")
    print("=" * 70)

    # Attack 401
    print("\n[Attack 401] Threshold Discovery Attack...")
    defense = DefenseSystem()
    attack = ThresholdDiscoveryAttack()
    result = attack.execute(defense)
    defense_check = ThresholdDiscoveryDefense(defense)
    attempts = [AttackAttempt(
        attempt_id=f"p{i}", attack_type="probe", parameters={"velocity": i * 0.1},
        timestamp=datetime.now()
    ) for i in range(10)]
    detected, alerts = defense_check.detect(attempts)
    results["401_threshold_discovery"] = {"attack_result": result, "detected": detected, "alerts": alerts}
    print(f"  Success: {result['success']}, Detected: {detected}")

    # Attack 402
    print("\n[Attack 402] Defense Response Learning...")
    defense = DefenseSystem()
    attack = DefenseResponseLearning()
    result = attack.execute(defense)
    defense_check = DefenseResponseLearningDefense(defense)
    attempts = [AttackAttempt(
        attempt_id=f"t{i}", attack_type="training",
        parameters={k: random.uniform(0, 1) for k in ["velocity", "volume", "anomaly_score"]},
        timestamp=datetime.now()
    ) for i in range(25)]
    detected, alerts = defense_check.detect(attempts)
    results["402_response_learning"] = {"attack_result": result, "detected": detected, "alerts": alerts}
    print(f"  Success: {result['success']}, Detected: {detected}")

    # Attack 403
    print("\n[Attack 403] Gradient Descent Evasion...")
    defense = DefenseSystem()
    attack = GradientDescentEvasion()
    result = attack.execute(defense)
    defense_check = GradientDescentDefense(defense)
    attempts = [AttackAttempt(
        attempt_id=f"g{i}", attack_type="gradient",
        parameters={"velocity": 0.5 + i * 0.05, "volume": 0.5, "anomaly_score": 0.5},
        timestamp=datetime.now()
    ) for i in range(10)]
    detected, alerts = defense_check.detect(attempts)
    results["403_gradient_evasion"] = {"attack_result": result, "detected": detected, "alerts": alerts}
    print(f"  Success: {result['success']}, Detected: {detected}")

    # Attack 404
    print("\n[Attack 404] Multi-Agent Learning Attack...")
    defense = DefenseSystem()
    attack = MultiAgentLearning()
    result = attack.execute(defense)
    defense_check = MultiAgentDefense(defense)
    attempts = [AttackAttempt(
        attempt_id=f"agent_{i}_probe", attack_type="multi",
        parameters={"velocity": 0.3, "volume": 0.3, "anomaly_score": 0.3},
        timestamp=datetime.now()
    ) for i in range(5)]
    detected, alerts = defense_check.detect(attempts)
    results["404_multi_agent"] = {"attack_result": result, "detected": detected, "alerts": alerts}
    print(f"  Success: {result['success']}, Detected: {detected}")

    # Attack 405
    print("\n[Attack 405] Temporal Adaptation Attack...")
    defense = DefenseSystem()
    attack = TemporalAdaptation()
    result = attack.execute(defense)
    defense_check = TemporalAdaptationDefense(defense)
    attempts = [AttackAttempt(
        attempt_id=f"temp_{i}", attack_type="temporal",
        parameters={"anomaly_score": 0.5},
        timestamp=datetime.now()
    ) for i in range(15)]
    detected, alerts = defense_check.detect(attempts)
    results["405_temporal"] = {"attack_result": result, "detected": detected, "alerts": alerts}
    print(f"  Success: {result['success']}, Detected: {detected}")

    # Attack 406
    print("\n[Attack 406] Defense Model Extraction...")
    defense = DefenseSystem()
    attack = DefenseModelExtraction()
    result = attack.execute(defense)
    defense_check = DefenseModelExtractionDefense(defense)
    attempts = [AttackAttempt(
        attempt_id=f"ext_{i}", attack_type="extraction",
        parameters={k: random.uniform(0, 1) for k in ["velocity", "volume", "anomaly_score"]},
        timestamp=datetime.now()
    ) for i in range(60)]
    detected, alerts = defense_check.detect(attempts)
    results["406_model_extraction"] = {"attack_result": result, "detected": detected, "alerts": alerts}
    print(f"  Success: {result['success']}, Detected: {detected}")

    # Summary
    print("\n" + "=" * 70)
    print("TRACK FY SUMMARY")
    print("=" * 70)

    total_attacks = 6
    attacks_detected = sum(1 for r in results.values() if r.get("detected", False))
    detection_rate = attacks_detected / total_attacks * 100

    print(f"Total Attacks: {total_attacks}")
    print(f"Attacks Detected: {attacks_detected}")
    print(f"Detection Rate: {detection_rate:.1f}%")

    print("\n--- Key Insight ---")
    print("Adaptive adversaries learn from defense responses.")
    print("Static thresholds are discoverable. Defenses must:")
    print("- Add noise to responses")
    print("- Detect probing patterns")
    print("- Limit query rates")
    print("- Use adversarial robustness techniques")

    results["summary"] = {"total_attacks": total_attacks, "attacks_detected": attacks_detected, "detection_rate": detection_rate}
    return results


if __name__ == "__main__":
    results = run_track_fy_simulations()