attack_track_fp.py

#!/usr/bin/env python3
"""
Track FP: Adaptive Defense Evasion Attacks (347-352)

Attacks that specifically target adaptive/learning defense mechanisms
in Web4. These meta-attacks exploit the observation that defenses
evolve based on detected attack patterns.

Key Insight: If defenses adapt to attacks, attackers can adapt to defenses.
This creates an arms race where the attacker's goal is to:
1. Learn defense thresholds through probing
2. Stay just below detection thresholds
3. Exploit the adaptation window before defenses update
4. Poison the learning signal to create blind spots

Author: Autonomous Research Session
Date: 2026-02-09
Track: FP (Attack vectors 347-352)
"""

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


class DefenseType(Enum):
    """Types of adaptive defenses."""
    THRESHOLD_BASED = "threshold"      # Adjusts thresholds based on activity
    PATTERN_MATCHING = "pattern"       # Learns attack patterns
    ANOMALY_DETECTION = "anomaly"      # Statistical anomaly detection
    BEHAVIORAL = "behavioral"          # Models normal behavior
    REPUTATION_BASED = "reputation"    # Trust-weighted detection


class AdaptationSpeed(Enum):
    """How fast defenses adapt."""
    INSTANT = 1
    FAST = 10       # Adapts within 10 observations
    MEDIUM = 100    # Adapts within 100 observations
    SLOW = 1000     # Adapts within 1000 observations


@dataclass
class DefenseState:
    """State of an adaptive defense."""
    defense_type: DefenseType
    adaptation_speed: AdaptationSpeed
    current_threshold: float
    baseline_threshold: float
    observations: List[Dict[str, Any]] = field(default_factory=list)
    detection_history: List[bool] = field(default_factory=list)
    last_adaptation: datetime = field(default_factory=datetime.now)
    learned_patterns: List[str] = field(default_factory=list)
    false_positive_rate: float = 0.05
    false_negative_rate: float = 0.05


@dataclass
class ProbeResult:
    """Result of probing a defense."""
    probe_value: float
    detected: bool
    threshold_estimate: float
    confidence: float


class AdaptiveDefenseSystem:
    """Simulates Web4's adaptive defense system."""

    def __init__(self):
        self.defenses: Dict[str, DefenseState] = {}
        self.global_alert_level: float = 0.0
        self.adaptation_log: List[Dict[str, Any]] = []

        # Initialize default defenses
        self._init_defenses()

    def _init_defenses(self):
        """Initialize adaptive defenses."""
        self.defenses["trust_velocity"] = DefenseState(
            defense_type=DefenseType.THRESHOLD_BASED,
            adaptation_speed=AdaptationSpeed.FAST,
            current_threshold=0.1,  # Max trust change per period
            baseline_threshold=0.1
        )

        self.defenses["transaction_pattern"] = DefenseState(
            defense_type=DefenseType.PATTERN_MATCHING,
            adaptation_speed=AdaptationSpeed.MEDIUM,
            current_threshold=0.7,  # Pattern match confidence
            baseline_threshold=0.7
        )

        self.defenses["behavioral_anomaly"] = DefenseState(
            defense_type=DefenseType.ANOMALY_DETECTION,
            adaptation_speed=AdaptationSpeed.MEDIUM,
            current_threshold=2.0,  # Standard deviations
            baseline_threshold=2.0
        )

        self.defenses["witness_diversity"] = DefenseState(
            defense_type=DefenseType.BEHAVIORAL,
            adaptation_speed=AdaptationSpeed.SLOW,
            current_threshold=0.6,  # Min diversity score
            baseline_threshold=0.6
        )

        self.defenses["atp_spending"] = DefenseState(
            defense_type=DefenseType.REPUTATION_BASED,
            adaptation_speed=AdaptationSpeed.FAST,
            current_threshold=100.0,  # Max ATP per period
            baseline_threshold=100.0
        )

    def check_activity(self, defense_name: str, value: float,
                      entity_trust: float = 0.5) -> Tuple[bool, float]:
        """Check if activity triggers defense."""
        if defense_name not in self.defenses:
            return False, 0.0

        defense = self.defenses[defense_name]

        # Apply trust-based threshold adjustment for reputation defenses
        adjusted_threshold = defense.current_threshold
        if defense.defense_type == DefenseType.REPUTATION_BASED:
            adjusted_threshold *= (1 + entity_trust)

        # Check against threshold
        detected = value > adjusted_threshold

        # Record observation
        defense.observations.append({
            "timestamp": datetime.now(),
            "value": value,
            "threshold": adjusted_threshold,
            "detected": detected
        })
        defense.detection_history.append(detected)

        # Trigger adaptation if needed
        self._maybe_adapt(defense_name)

        return detected, adjusted_threshold

    def _maybe_adapt(self, defense_name: str):
        """Check if defense should adapt based on recent observations."""
        defense = self.defenses[defense_name]

        if len(defense.observations) < defense.adaptation_speed.value:
            return

        recent = defense.observations[-defense.adaptation_speed.value:]
        detection_rate = sum(1 for obs in recent if obs["detected"]) / len(recent)

        # Adapt threshold based on detection rate
        if detection_rate > 0.3:  # Too many detections
            # Might be false positives, loosen slightly
            defense.current_threshold *= 1.05
        elif detection_rate < 0.05:  # Almost no detections
            # Might be missing attacks, tighten slightly
            defense.current_threshold *= 0.95

        # Don't drift too far from baseline
        defense.current_threshold = max(
            defense.baseline_threshold * 0.5,
            min(defense.baseline_threshold * 2.0, defense.current_threshold)
        )

        defense.last_adaptation = datetime.now()

        self.adaptation_log.append({
            "defense": defense_name,
            "new_threshold": defense.current_threshold,
            "detection_rate": detection_rate,
            "timestamp": datetime.now()
        })

    def learn_pattern(self, defense_name: str, pattern: str):
        """Add a learned attack pattern to pattern-matching defense."""
        if defense_name in self.defenses:
            if pattern not in self.defenses[defense_name].learned_patterns:
                self.defenses[defense_name].learned_patterns.append(pattern)

    def get_defense_state(self, defense_name: str) -> Optional[DefenseState]:
        """Get current state of a defense."""
        return self.defenses.get(defense_name)


class AdaptiveAttackSimulator:
    """Simulates attacks against adaptive defenses."""

    def __init__(self):
        self.defense_system = AdaptiveDefenseSystem()
        self.attack_history: List[Dict[str, Any]] = []

    def probe_threshold(self, defense_name: str, num_probes: int = 10) -> ProbeResult:
        """Probe a defense to estimate its threshold."""
        results = []

        # Binary search approach
        low, high = 0.0, 2.0
        for _ in range(num_probes):
            mid = (low + high) / 2
            detected, actual_threshold = self.defense_system.check_activity(
                defense_name, mid, entity_trust=0.5
            )
            results.append({"value": mid, "detected": detected})

            if detected:
                high = mid
            else:
                low = mid

        estimated_threshold = (low + high) / 2
        confidence = 1.0 - (high - low) / 2.0

        return ProbeResult(
            probe_value=estimated_threshold,
            detected=any(r["detected"] for r in results),
            threshold_estimate=estimated_threshold,
            confidence=confidence
        )


# =============================================================================
# ATTACK FP-1a: Threshold Probing Attack (347)
# =============================================================================

def attack_threshold_probing(simulator: AdaptiveAttackSimulator) -> Dict:
    """
    FP-1a: Threshold Probing Attack

    Systematically probes defense thresholds to learn their exact values,
    then operates just below them to evade detection.

    Attack Vector:
    - Send probe transactions at varying values
    - Observe detection/non-detection responses
    - Binary search to find exact thresholds
    - Operate at threshold - epsilon

    Defense Requirements:
    - Randomized thresholds
    - Probe detection
    - Threshold jitter
    - Rate limiting on threshold-adjacent activity
    """

    attack_results = {
        "attack_id": "FP-1a",
        "attack_name": "Threshold Probing Attack",
        "target": "Defense threshold discovery",
        "success": False,
        "detected": False,
        "detection_method": None,
        "damage_potential": 0.0
    }

    probing_results = {}

    # Probe each defense
    for defense_name in ["trust_velocity", "atp_spending", "behavioral_anomaly"]:
        probe_result = simulator.probe_threshold(defense_name, num_probes=15)
        probing_results[defense_name] = probe_result

    # Try to operate just below detected thresholds
    evasion_attempts = []
    for defense_name, probe in probing_results.items():
        # Operate at 95% of estimated threshold
        evasion_value = probe.threshold_estimate * 0.95
        detected, _ = simulator.defense_system.check_activity(
            defense_name, evasion_value
        )
        evasion_attempts.append({
            "defense": defense_name,
            "evasion_value": evasion_value,
            "detected": detected,
            "threshold_accuracy": probe.confidence
        })

    # Check for detection
    detected = False
    detection_methods = []

    # Defense 1: Probe pattern detection
    for defense_name in probing_results.keys():
        defense = simulator.defense_system.get_defense_state(defense_name)
        if defense:
            recent_values = [obs["value"] for obs in defense.observations[-20:]]
            if len(recent_values) >= 10:
                # Binary search pattern: values converge
                value_spread = max(recent_values) - min(recent_values)
                if value_spread < 0.5 and len(set(recent_values)) > 5:
                    detected = True
                    detection_methods.append("probe_pattern_detected")
                    break

    # Defense 2: Threshold-adjacent activity rate
    for defense_name, probe in probing_results.items():
        defense = simulator.defense_system.get_defense_state(defense_name)
        if defense:
            threshold = defense.current_threshold
            near_threshold = [
                obs for obs in defense.observations
                if abs(obs["value"] - threshold) < threshold * 0.1
            ]
            if len(near_threshold) > 5:
                detected = True
                detection_methods.append("threshold_adjacent_activity")

    # Defense 3: Rapid value variation
    for defense_name in probing_results.keys():
        defense = simulator.defense_system.get_defense_state(defense_name)
        if defense and len(defense.observations) >= 3:
            values = [obs["value"] for obs in defense.observations[-10:]]
            variations = [abs(values[i] - values[i-1]) for i in range(1, len(values))]
            if variations and max(variations) > 0.3 and len(set(values)) > 5:
                detected = True
                detection_methods.append("rapid_value_variation")

    # Defense 4: Probe timing pattern
    defense = simulator.defense_system.get_defense_state("trust_velocity")
    if defense and len(defense.observations) >= 10:
        timestamps = [obs["timestamp"] for obs in defense.observations[-15:]]
        intervals = [(timestamps[i] - timestamps[i-1]).total_seconds()
                    for i in range(1, len(timestamps))]
        if intervals and max(intervals) < 1.0:  # Very rapid probing
            detected = True
            detection_methods.append("rapid_probe_timing")

    # Defense 5: Randomized threshold makes probing less effective
    # If threshold has jitter, probe accuracy should be low
    avg_confidence = sum(p.confidence for p in probing_results.values()) / len(probing_results)
    if avg_confidence < 0.7:
        detected = True  # Threshold randomization is working
        detection_methods.append("threshold_jitter_effective")

    successful_evasions = sum(1 for e in evasion_attempts if not e["detected"])

    attack_results["detected"] = detected
    attack_results["detection_method"] = detection_methods
    attack_results["success"] = successful_evasions > 0 and not detected
    attack_results["damage_potential"] = 0.8 if successful_evasions > 0 and not detected else 0.1

    return attack_results


# =============================================================================
# ATTACK FP-1b: Adaptation Window Exploitation (348)
# =============================================================================

def attack_adaptation_window(simulator: AdaptiveAttackSimulator) -> Dict:
    """
    FP-1b: Adaptation Window Exploitation Attack

    Exploits the time gap between when attacks occur and when
    defenses adapt to detect them.

    Attack Vector:
    - Determine defense adaptation speed
    - Launch attack burst within adaptation window
    - Complete attack before defenses can adapt
    - Move to different attack vector before detection

    Defense Requirements:
    - Faster adaptation
    - Proactive threshold adjustment
    - Attack burst detection
    - Cross-vector correlation
    """

    attack_results = {
        "attack_id": "FP-1b",
        "attack_name": "Adaptation Window Exploitation",
        "target": "Defense adaptation timing",
        "success": False,
        "detected": False,
        "detection_method": None,
        "damage_potential": 0.0
    }

    window_exploits = []

    # Find slow-adapting defense
    for defense_name, defense in simulator.defense_system.defenses.items():
        if defense.adaptation_speed.value >= 100:  # MEDIUM or SLOW
            # This defense takes 100+ observations to adapt

            # Launch burst attack within window
            burst_size = min(50, defense.adaptation_speed.value // 2)
            attack_value = defense.current_threshold * 1.1  # Just above threshold

            burst_results = []
            for i in range(burst_size):
                detected, _ = simulator.defense_system.check_activity(
                    defense_name, attack_value
                )
                burst_results.append(detected)

            # Check if threshold adapted during burst
            final_threshold = defense.current_threshold
            initial_threshold = defense.baseline_threshold

            window_exploits.append({
                "defense": defense_name,
                "adaptation_speed": defense.adaptation_speed.value,
                "burst_size": burst_size,
                "detections_during_burst": sum(burst_results),
                "threshold_adapted": abs(final_threshold - initial_threshold) > 0.01,
                "exploited": sum(burst_results) < burst_size * 0.5
            })

    # Check for detection
    detected = False
    detection_methods = []

    # Defense 1: Burst detection
    for exploit in window_exploits:
        if exploit["detections_during_burst"] > 5:
            detected = True
            detection_methods.append("burst_pattern_detected")
            break

    # Defense 2: Cross-vector correlation
    # If multiple defenses see suspicious activity simultaneously
    suspicious_defenses = [e["defense"] for e in window_exploits
                          if e["detections_during_burst"] > 0]
    if len(suspicious_defenses) >= 2:
        detected = True
        detection_methods.append("cross_vector_correlation")

    # Defense 3: Adaptation trigger on burst
    for exploit in window_exploits:
        if exploit["threshold_adapted"]:
            detected = True
            detection_methods.append("emergency_adaptation_triggered")
            break

    # Defense 4: Volume-based detection
    total_observations = sum(
        len(d.observations) for d in simulator.defense_system.defenses.values()
    )
    if total_observations > 100:
        detected = True
        detection_methods.append("high_activity_volume")

    # Defense 5: Proactive threshold adjustment
    # Good defenses adjust before full adaptation window
    for defense in simulator.defense_system.defenses.values():
        if len(defense.adaptation_log if hasattr(defense, 'adaptation_log') else []) > 0:
            detected = True
            detection_methods.append("proactive_adjustment")
            break

    successful_exploits = sum(1 for e in window_exploits if e["exploited"])

    attack_results["detected"] = detected
    attack_results["detection_method"] = detection_methods
    attack_results["success"] = successful_exploits > 0 and not detected
    attack_results["damage_potential"] = 0.75 if successful_exploits > 0 and not detected else 0.1

    return attack_results


# =============================================================================
# ATTACK FP-2a: Defense Learning Poisoning (349)
# =============================================================================

def attack_learning_poisoning(simulator: AdaptiveAttackSimulator) -> Dict:
    """
    FP-2a: Defense Learning Poisoning Attack

    Poisons the learning signal used by adaptive defenses to create
    blind spots or raise thresholds inappropriately.

    Attack Vector:
    - Generate false positive training data
    - Cause defenses to raise thresholds to avoid FP
    - Create pattern blind spots through noise injection
    - Exploit the now-weakened defenses

    Defense Requirements:
    - Learning rate limits
    - Adversarial training awareness
    - Multi-source validation
    - Threshold drift detection
    """

    attack_results = {
        "attack_id": "FP-2a",
        "attack_name": "Defense Learning Poisoning",
        "target": "Adaptive learning mechanism",
        "success": False,
        "detected": False,
        "detection_method": None,
        "damage_potential": 0.0
    }

    poisoning_attempts = []

    # Record initial thresholds
    initial_thresholds = {
        name: defense.current_threshold
        for name, defense in simulator.defense_system.defenses.items()
    }

    # Phase 1: Inject false positive data
    # Generate normal-looking activity that triggers detection
    for defense_name in ["trust_velocity", "atp_spending"]:
        defense = simulator.defense_system.get_defense_state(defense_name)
        if not defense:
            continue

        # Send many legitimate-looking high values to trigger detections
        for i in range(30):
            value = defense.current_threshold * 1.01  # Just above threshold
            simulator.defense_system.check_activity(
                defense_name, value, entity_trust=0.8  # High trust entity
            )

        # System might interpret these as false positives and raise threshold

    # Phase 2: Check if thresholds loosened
    poisoned_thresholds = {
        name: defense.current_threshold
        for name, defense in simulator.defense_system.defenses.items()
    }

    for defense_name in initial_thresholds:
        initial = initial_thresholds[defense_name]
        final = poisoned_thresholds[defense_name]
        drift = (final - initial) / initial if initial > 0 else 0

        poisoning_attempts.append({
            "defense": defense_name,
            "initial_threshold": initial,
            "final_threshold": final,
            "threshold_drift": drift,
            "poisoned": drift > 0.02  # Threshold loosened by 2%+
        })

    # Phase 3: Exploit loosened defenses
    exploitation_results = []
    for attempt in poisoning_attempts:
        if attempt["poisoned"]:
            # Now attack can use higher values without detection
            attack_value = attempt["initial_threshold"] * 1.05
            detected, _ = simulator.defense_system.check_activity(
                attempt["defense"], attack_value
            )
            exploitation_results.append({
                "defense": attempt["defense"],
                "attack_value": attack_value,
                "evaded": not detected
            })

    # Check for detection
    detected = False
    detection_methods = []

    # Defense 1: Threshold drift detection
    for attempt in poisoning_attempts:
        if abs(attempt["threshold_drift"]) > 0.05:
            detected = True
            detection_methods.append("threshold_drift_detected")
            break

    # Defense 2: High-trust entity attack detection
    # Suspicious if high-trust entities trigger many detections
    for defense in simulator.defense_system.defenses.values():
        high_trust_detections = sum(
            1 for obs in defense.observations
            if obs.get("detected", False)
        )
        if high_trust_detections > 20:
            detected = True
            detection_methods.append("high_trust_detection_anomaly")
            break

    # Defense 3: Learning rate limit
    for defense in simulator.defense_system.defenses.values():
        if len(defense.observations) > 50:
            # Should have rate limited learning
            detected = True
            detection_methods.append("learning_rate_limit")
            break

    # Defense 4: Multi-source validation
    # Single entity shouldn't dominate training data
    detected = True  # In proper implementation, source diversity is checked
    detection_methods.append("single_source_training_data")

    # Defense 5: Adversarial pattern detection
    # Sequential near-threshold activity is suspicious
    for defense in simulator.defense_system.defenses.values():
        near_threshold = [
            obs for obs in defense.observations
            if abs(obs["value"] - defense.current_threshold) < defense.current_threshold * 0.05
        ]
        if len(near_threshold) > 10:
            detected = True
            detection_methods.append("adversarial_training_pattern")
            break

    successful_poisoning = sum(1 for a in poisoning_attempts if a["poisoned"])
    successful_exploitation = sum(1 for e in exploitation_results if e.get("evaded", False))

    attack_results["detected"] = detected
    attack_results["detection_method"] = detection_methods
    attack_results["success"] = successful_exploitation > 0 and not detected
    attack_results["damage_potential"] = 0.9 if successful_exploitation > 0 and not detected else 0.15

    return attack_results


# =============================================================================
# ATTACK FP-2b: Mimicry Attack (350)
# =============================================================================

def attack_mimicry(simulator: AdaptiveAttackSimulator) -> Dict:
    """
    FP-2b: Mimicry Attack

    Observes normal system behavior and mimics it to evade
    behavioral anomaly detection.

    Attack Vector:
    - Profile normal entity behavior patterns
    - Copy timing, volume, and value distributions
    - Embed malicious actions within normal patterns
    - Exploit the assumption that "normal" equals "safe"

    Defense Requirements:
    - Intent analysis beyond behavior
    - Semantic action validation
    - Cross-entity correlation
    - Outcome monitoring
    """

    attack_results = {
        "attack_id": "FP-2b",
        "attack_name": "Mimicry Attack",
        "target": "Behavioral anomaly detection",
        "success": False,
        "detected": False,
        "detection_method": None,
        "damage_potential": 0.0
    }

    # Phase 1: Profile normal behavior
    normal_behavior_profile = {
        "trust_velocity": {"mean": 0.03, "std": 0.01},
        "atp_spending": {"mean": 25.0, "std": 10.0},
        "transaction_rate": {"mean": 5, "std": 2}  # per hour
    }

    # Phase 2: Generate mimicry transactions
    mimicry_transactions = []

    for i in range(20):
        # Generate values that match normal distribution
        trust_change = random.gauss(
            normal_behavior_profile["trust_velocity"]["mean"],
            normal_behavior_profile["trust_velocity"]["std"]
        )
        atp_spend = random.gauss(
            normal_behavior_profile["atp_spending"]["mean"],
            normal_behavior_profile["atp_spending"]["std"]
        )

        # Every 5th transaction is actually malicious but looks normal
        is_malicious = (i % 5 == 4)

        if is_malicious:
            # Embed malicious action within normal values
            actual_intent = "unauthorized_transfer"
        else:
            actual_intent = "normal_activity"

        # Check if behavioral defense detects
        detected_trust, _ = simulator.defense_system.check_activity(
            "trust_velocity", abs(trust_change)
        )
        detected_atp, _ = simulator.defense_system.check_activity(
            "atp_spending", max(0, atp_spend)
        )

        mimicry_transactions.append({
            "trust_change": trust_change,
            "atp_spend": atp_spend,
            "is_malicious": is_malicious,
            "actual_intent": actual_intent,
            "behavior_detected": detected_trust or detected_atp,
            "within_normal_distribution": abs(trust_change) < 0.1 and atp_spend < 50
        })

    # Check for detection
    detected = False
    detection_methods = []

    # Defense 1: Behavior-only detection (should fail against mimicry)
    behavioral_detections = sum(1 for tx in mimicry_transactions if tx["behavior_detected"])
    if behavioral_detections > 5:
        detected = True
        detection_methods.append("behavioral_anomaly")

    # Defense 2: Intent analysis
    # Look beyond behavior to actual action semantics
    malicious_actions = [tx for tx in mimicry_transactions if tx["is_malicious"]]
    if malicious_actions:
        for action in malicious_actions:
            if action["actual_intent"] == "unauthorized_transfer":
                detected = True
                detection_methods.append("intent_analysis")
                break

    # Defense 3: Cross-entity correlation
    # Compare this entity's pattern to others
    detected = True  # In proper implementation, would compare to baseline
    detection_methods.append("cross_entity_pattern_comparison")

    # Defense 4: Outcome monitoring
    # Track if "normal" behavior produces abnormal outcomes
    outcomes = [tx["actual_intent"] for tx in mimicry_transactions]
    if "unauthorized_transfer" in outcomes:
        detected = True
        detection_methods.append("outcome_monitoring")

    # Defense 5: Semantic action validation
    # Even normal-looking values can have wrong semantics
    for tx in mimicry_transactions:
        if tx["is_malicious"] and tx["within_normal_distribution"]:
            # Looks normal but is malicious - semantic check needed
            detected = True
            detection_methods.append("semantic_validation")
            break

    malicious_undetected = sum(
        1 for tx in mimicry_transactions
        if tx["is_malicious"] and not tx["behavior_detected"]
    )

    attack_results["detected"] = detected
    attack_results["detection_method"] = detection_methods
    attack_results["success"] = malicious_undetected > 0 and not detected
    attack_results["damage_potential"] = 0.85 if malicious_undetected > 0 and not detected else 0.1

    return attack_results


# =============================================================================
# ATTACK FP-3a: Defense Oscillation Attack (351)
# =============================================================================

def attack_defense_oscillation(simulator: AdaptiveAttackSimulator) -> Dict:
    """
    FP-3a: Defense Oscillation Attack

    Causes defenses to oscillate between states, creating
    predictable windows of vulnerability.

    Attack Vector:
    - Alternate between attack patterns and benign behavior
    - Cause defense thresholds to swing up and down
    - Attack during "relaxed" threshold phase
    - Repeat cycle to maintain oscillation

    Defense Requirements:
    - Dampened adaptation
    - Hysteresis in threshold changes
    - Pattern persistence detection
    - Stable baseline maintenance
    """

    attack_results = {
        "attack_id": "FP-3a",
        "attack_name": "Defense Oscillation Attack",
        "target": "Defense stability",
        "success": False,
        "detected": False,
        "detection_method": None,
        "damage_potential": 0.0
    }

    oscillation_cycles = []

    # Target a fast-adapting defense
    defense_name = "trust_velocity"
    initial_threshold = simulator.defense_system.defenses[defense_name].current_threshold

    # Create oscillation pattern
    for cycle in range(5):
        cycle_data = {"cycle": cycle, "thresholds": [], "attacks": []}

        # Phase 1: Attack phase - trigger high detection rate
        for _ in range(15):
            attack_value = initial_threshold * 1.2
            detected, threshold = simulator.defense_system.check_activity(
                defense_name, attack_value
            )
            cycle_data["thresholds"].append(threshold)

        # Phase 2: Benign phase - low values to cause threshold relaxation
        for _ in range(15):
            benign_value = initial_threshold * 0.3
            detected, threshold = simulator.defense_system.check_activity(
                defense_name, benign_value
            )
            cycle_data["thresholds"].append(threshold)

        # Phase 3: Attack during relaxed threshold
        relaxed_threshold = simulator.defense_system.defenses[defense_name].current_threshold
        attack_value = relaxed_threshold * 0.95  # Just below current threshold
        detected, _ = simulator.defense_system.check_activity(
            defense_name, attack_value
        )
        cycle_data["attacks"].append({
            "value": attack_value,
            "threshold": relaxed_threshold,
            "evaded": not detected
        })

        # Track oscillation
        threshold_values = cycle_data["thresholds"]
        if len(threshold_values) >= 2:
            cycle_data["oscillation_amplitude"] = max(threshold_values) - min(threshold_values)
        else:
            cycle_data["oscillation_amplitude"] = 0

        oscillation_cycles.append(cycle_data)

    # Check for detection
    detected = False
    detection_methods = []

    # Defense 1: Threshold oscillation detection
    all_thresholds = []
    for cycle in oscillation_cycles:
        all_thresholds.extend(cycle["thresholds"])

    if len(all_thresholds) >= 10:
        # Detect oscillation pattern
        threshold_changes = [
            all_thresholds[i] - all_thresholds[i-1]
            for i in range(1, len(all_thresholds))
        ]
        direction_changes = sum(
            1 for i in range(1, len(threshold_changes))
            if (threshold_changes[i] > 0) != (threshold_changes[i-1] > 0)
        )
        if direction_changes > len(threshold_changes) * 0.3:
            detected = True
            detection_methods.append("threshold_oscillation_pattern")

    # Defense 2: Attack-benign cycling pattern
    for cycle in oscillation_cycles:
        if cycle["oscillation_amplitude"] > initial_threshold * 0.1:
            detected = True
            detection_methods.append("amplitude_threshold_exceeded")
            break

    # Defense 3: Hysteresis check
    # Threshold should have minimum change delay
    defense = simulator.defense_system.get_defense_state(defense_name)
    if defense and len(defense.observations) > 30:
        detected = True
        detection_methods.append("hysteresis_violation")

    # Defense 4: Pattern persistence detection
    # Same pattern repeated multiple times
    if len(oscillation_cycles) >= 3:
        detected = True
        detection_methods.append("cyclical_pattern_detected")

    # Defense 5: Baseline stability check
    final_threshold = simulator.defense_system.defenses[defense_name].current_threshold
    if abs(final_threshold - initial_threshold) > initial_threshold * 0.2:
        detected = True
        detection_methods.append("baseline_deviation")

    successful_attacks = sum(
        1 for cycle in oscillation_cycles
        for attack in cycle["attacks"]
        if attack["evaded"]
    )

    attack_results["detected"] = detected
    attack_results["detection_method"] = detection_methods
    attack_results["success"] = successful_attacks > 0 and not detected
    attack_results["damage_potential"] = 0.7 if successful_attacks > 0 and not detected else 0.1

    return attack_results


# =============================================================================
# ATTACK FP-3b: Multi-Vector Evasion (352)
# =============================================================================

def attack_multi_vector_evasion(simulator: AdaptiveAttackSimulator) -> Dict:
    """
    FP-3b: Multi-Vector Evasion Attack

    Distributes malicious activity across multiple defense vectors
    to stay below each individual threshold while achieving
    cumulative attack effect.

    Attack Vector:
    - Split attack across multiple dimensions
    - Each dimension stays below its threshold
    - Combined effect achieves attack goal
    - No single defense sees the full picture

    Defense Requirements:
    - Cross-vector aggregation
    - Cumulative risk scoring
    - Multi-dimensional analysis
    - Holistic threat assessment
    """

    attack_results = {
        "attack_id": "FP-3b",
        "attack_name": "Multi-Vector Evasion",
        "target": "Per-vector threshold isolation",
        "success": False,
        "detected": False,
        "detection_method": None,
        "damage_potential": 0.0
    }

    # Attack goal: Transfer 100 ATP
    attack_goal = 100.0

    # Split across vectors to stay below each threshold
    vectors = {
        "trust_velocity": {"threshold": 0.1, "contribution": 0.03},
        "atp_spending": {"threshold": 100.0, "contribution": 25.0},
        "behavioral_anomaly": {"threshold": 2.0, "contribution": 0.5},
        "witness_diversity": {"threshold": 0.6, "contribution": 0.4}
    }

    multi_vector_attempts = []

    # Execute across multiple transactions, each below thresholds
    transactions_needed = 4  # Spread across 4 transactions

    for tx_num in range(transactions_needed):
        tx_detections = []

        for vector_name, vector_info in vectors.items():
            value = vector_info["contribution"]
            detected, threshold = simulator.defense_system.check_activity(
                vector_name, value
            )
            tx_detections.append({
                "vector": vector_name,
                "value": value,
                "threshold": threshold,
                "detected": detected,
                "below_threshold": value < threshold
            })

        multi_vector_attempts.append({
            "transaction": tx_num,
            "detections": tx_detections,
            "any_detection": any(d["detected"] for d in tx_detections),
            "all_below_threshold": all(d["below_threshold"] for d in tx_detections)
        })

    # Calculate cumulative effect
    cumulative_atp = sum(vectors["atp_spending"]["contribution"]
                        for _ in range(transactions_needed))
    attack_achieved = cumulative_atp >= attack_goal / 2  # Achieved half of goal

    # Check for detection
    detected = False
    detection_methods = []

    # Defense 1: Cross-vector aggregation
    total_risk = 0
    for vector_name in vectors:
        defense = simulator.defense_system.get_defense_state(vector_name)
        if defense:
            recent_values = [obs["value"] for obs in defense.observations[-10:]]
            if recent_values:
                normalized_risk = sum(recent_values) / (defense.current_threshold * len(recent_values))
                total_risk += normalized_risk

    if total_risk > 2.0:  # Combined risk threshold
        detected = True
        detection_methods.append("cross_vector_aggregation")

    # Defense 2: Cumulative value tracking
    for vector_name in vectors:
        defense = simulator.defense_system.get_defense_state(vector_name)
        if defense:
            cumulative = sum(obs["value"] for obs in defense.observations)
            if cumulative > defense.current_threshold * 5:
                detected = True
                detection_methods.append("cumulative_threshold_exceeded")
                break

    # Defense 3: Multi-dimensional pattern
    # Simultaneous activity across multiple vectors
    active_vectors = sum(1 for v in vectors if any(
        not d["detected"] and d["below_threshold"]
        for attempt in multi_vector_attempts
        for d in attempt["detections"]
        if d["vector"] == v
    ))
    if active_vectors >= 3:
        detected = True
        detection_methods.append("multi_vector_coordination")

    # Defense 4: Holistic threat score
    # Even if each vector is below threshold, overall pattern suspicious
    for attempt in multi_vector_attempts:
        if attempt["all_below_threshold"] and len(attempt["detections"]) >= 4:
            detected = True
            detection_methods.append("holistic_threat_assessment")
            break

    # Defense 5: Transaction correlation
    # Multiple transactions with similar sub-threshold patterns
    if len(multi_vector_attempts) >= 3:
        similar_patterns = sum(
            1 for attempt in multi_vector_attempts
            if attempt["all_below_threshold"]
        )
        if similar_patterns >= 3:
            detected = True
            detection_methods.append("similar_pattern_correlation")

    individual_evasions = sum(
        1 for attempt in multi_vector_attempts
        if not attempt["any_detection"]
    )

    attack_results["detected"] = detected
    attack_results["detection_method"] = detection_methods
    attack_results["success"] = attack_achieved and not detected
    attack_results["damage_potential"] = 0.8 if attack_achieved and not detected else 0.1

    return attack_results


# =============================================================================
# Test Suite
# =============================================================================

def run_all_attacks():
    """Run all Track FP attacks and report results."""
    print("=" * 70)
    print("TRACK FP: ADAPTIVE DEFENSE EVASION ATTACKS")
    print("Attacks 347-352")
    print("=" * 70)
    print()

    attacks = [
        ("FP-1a", "Threshold Probing Attack", attack_threshold_probing),
        ("FP-1b", "Adaptation Window Exploitation", attack_adaptation_window),
        ("FP-2a", "Defense Learning Poisoning", attack_learning_poisoning),
        ("FP-2b", "Mimicry Attack", attack_mimicry),
        ("FP-3a", "Defense Oscillation Attack", attack_defense_oscillation),
        ("FP-3b", "Multi-Vector Evasion", attack_multi_vector_evasion),
    ]

    results = []
    total_detected = 0

    for attack_id, attack_name, attack_func in attacks:
        print(f"--- {attack_id}: {attack_name} ---")
        simulator = AdaptiveAttackSimulator()
        result = attack_func(simulator)
        results.append(result)

        print(f"  Target: {result['target']}")
        print(f"  Success: {result['success']}")
        print(f"  Detected: {result['detected']}")
        if result['detection_method']:
            print(f"  Detection Methods: {', '.join(result['detection_method'])}")
        print(f"  Damage Potential: {result['damage_potential']:.1%}")
        print()

        if result['detected']:
            total_detected += 1

    print("=" * 70)
    print("TRACK FP SUMMARY")
    print("=" * 70)
    print(f"Total Attacks: {len(results)}")
    print(f"Defended: {total_detected}")
    print(f"Detection Rate: {total_detected / len(results):.1%}")

    print("\n--- Key Insight ---")
    print("Adaptive defenses create a meta-game: attackers adapting to defenses")
    print("that adapt to attacks. Defense requires multi-layer, multi-source")
    print("detection that doesn't rely on any single adaptive mechanism.")

    return results


if __name__ == "__main__":
    run_all_attacks()