interdisciplinary_methods.py

"""
OUROBOROS Interdisciplinary Methods (Algorithms 11-20)
======================================================
Novel detection algorithms combining Physics, Genomics, Quantum Computing,
Astrophysics, Ecology, and other advanced sciences.

These methods provide unique visualization and detection advantages beyond
traditional machine learning approaches.

Author: OUROBOROS Project
Date: December 7, 2025
Version: 2.0
"""

import numpy as np
import warnings
warnings.filterwarnings('ignore')

# ============================================================================
# ALGORITHM #11: GRAVITATIONAL LENSING DE-OBFUSCATOR
# ============================================================================

class GravitationalLensingDeobfuscator:
    """
    Algorithm #11: Gravitational Lensing De-obfuscator
    
    Physics-inspired algorithm that treats code sections as masses in spacetime.
    Obfuscated code creates "gravitational wells" that lens light rays (data flow).
    
    Theory: Just as massive objects bend spacetime, complex obfuscation creates
    "density anomalies" in the code space that distort normal execution flow.
    
    Output: Gravitational lensing map showing mass distribution
    """
    
    def __init__(self):
        self.name = "Gravitational Lensing De-obfuscator"
        self.description = "Physics simulation of code mass distribution"
        
    def compute(self, bytes_data):
        """Compute gravitational field from code density"""
        try:
            data = np.frombuffer(bytes_data, dtype=np.uint8)[:5000]
            
            # 1. Create 2D mass distribution (code density map)
            grid_size = int(np.sqrt(len(data)))
            if grid_size < 10:
                return {'error': 'Insufficient data', 'threat_indicator': False}
            
            mass_grid = data[:grid_size*grid_size].reshape(grid_size, grid_size).astype(float)
            
            # 2. Compute gravitational potential (Φ = -GM/r)
            # High byte values = high mass = deep wells
            potential = np.zeros_like(mass_grid)
            
            for i in range(grid_size):
                for j in range(grid_size):
                    # Sum gravitational influence from all masses
                    for mi in range(grid_size):
                        for mj in range(grid_size):
                            if (i, j) != (mi, mj):
                                r = np.sqrt((i - mi)**2 + (j - mj)**2) + 1e-6
                                potential[i, j] -= mass_grid[mi, mj] / r
            
            # 3. Compute gravitational lensing (curvature)
            # Second derivative of potential = curvature
            grad_y, grad_x = np.gradient(potential)
            curvature_x = np.gradient(grad_x, axis=1)
            curvature_y = np.gradient(grad_y, axis=0)
            curvature = np.abs(curvature_x) + np.abs(curvature_y)
            
            # 4. Find gravitational singularities (obfuscation centers)
            threshold = np.percentile(curvature.flatten(), 90)
            singularities = curvature > threshold
            n_singularities = np.sum(singularities)
            
            # 5. Compute lensing strength (Einstein radius analogue)
            lensing_strength = float(np.max(curvature))
            avg_curvature = float(np.mean(curvature))
            
            # 6. Detect mass concentration (packed sections)
            mass_variance = float(np.var(mass_grid))
            mass_concentration = float(np.max(mass_grid) / (np.mean(mass_grid) + 1e-6))
            
            return {
                'gravitational_map': curvature.tolist(),
                'potential_field': potential.tolist(),
                'n_singularities': int(n_singularities),
                'lensing_strength': lensing_strength,
                'avg_curvature': avg_curvature,
                'mass_variance': mass_variance,
                'mass_concentration': mass_concentration,
                'grid_size': grid_size,
                'threat_indicator': n_singularities > 5 or mass_concentration > 3.0
            }
            
        except Exception as e:
            return {'error': str(e), 'threat_indicator': False}


# ============================================================================
# ALGORITHM #12: EPIGENETIC STATE TRACKING
# ============================================================================

class EpigeneticStateTracker:
    """
    Algorithm #12: Epigenetic State Tracking
    
    Genomics-inspired algorithm that tracks "methylation" patterns in code.
    Treats bytes as DNA bases, tracks modifications over execution.
    
    Theory: Just as epigenetics controls gene expression without changing DNA,
    malware modifies behavior without changing underlying code structure.
    
    Output: Methylation heatmap showing state changes
    """
    
    def __init__(self):
        self.name = "Epigenetic State Tracker"
        self.description = "Genomic methylation pattern analysis"
        
    def compute(self, bytes_data):
        """Track epigenetic modifications in code"""
        try:
            data = np.frombuffer(bytes_data, dtype=np.uint8)[:10000]
            
            # 1. Convert bytes to "genetic" sequence (4-base alphabet like DNA)
            # A=0, C=1, G=2, T=3
            dna_sequence = data % 4
            
            # 2. Detect CpG islands (regions of high C-G content)
            # In genomics, these are methylation sites
            window_size = 100
            cpg_density = []
            
            for i in range(0, len(dna_sequence) - window_size, 20):
                window = dna_sequence[i:i+window_size]
                # Count C (1) followed by G (2)
                cpg_sites = 0
                for j in range(len(window) - 1):
                    if window[j] == 1 and window[j+1] == 2:
                        cpg_sites += 1
                cpg_density.append(cpg_sites / window_size)
            
            # 3. Simulate methylation state (high variance = active modification)
            methylation_variance = float(np.var(cpg_density)) if cpg_density else 0.0
            
            # 4. Detect histone modifications (structural changes)
            # Look for repeating patterns (nucleosomes in genomics)
            def find_repeats(seq, k=8):
                repeats = {}
                for i in range(len(seq) - k):
                    kmer = tuple(seq[i:i+k])
                    repeats[kmer] = repeats.get(kmer, 0) + 1
                return repeats
            
            repeats = find_repeats(dna_sequence)
            max_repeat_count = max(repeats.values()) if repeats else 0
            n_unique_kmers = len(repeats)
            
            # 5. Compute chromatin accessibility (open vs closed regions)
            # High entropy = open chromatin (accessible code)
            # Low entropy = closed chromatin (packed/encrypted)
            chunk_size = 256
            accessibility = []
            
            for i in range(0, len(data) - chunk_size, chunk_size):
                chunk = data[i:i+chunk_size]
                counts = np.bincount(chunk, minlength=256)
                probs = counts / chunk_size
                entropy = -np.sum(probs * np.log2(probs + 1e-10))
                accessibility.append(entropy)
            
            avg_accessibility = float(np.mean(accessibility)) if accessibility else 0.0
            accessibility_variance = float(np.var(accessibility)) if accessibility else 0.0
            
            # 6. Detect epigenetic drift (state changes over position)
            if len(cpg_density) > 2:
                drift = np.diff(cpg_density)
                max_drift = float(np.max(np.abs(drift)))
            else:
                max_drift = 0.0
            
            return {
                'cpg_density': cpg_density,
                'methylation_variance': methylation_variance,
                'max_repeat_count': int(max_repeat_count),
                'n_unique_kmers': int(n_unique_kmers),
                'accessibility_profile': accessibility,
                'avg_accessibility': avg_accessibility,
                'accessibility_variance': accessibility_variance,
                'epigenetic_drift': max_drift,
                'threat_indicator': methylation_variance > 0.01 or max_drift > 0.3
            }
            
        except Exception as e:
            return {'error': str(e), 'threat_indicator': False}


# ============================================================================
# ALGORITHM #13: QUANTUM WALK CONTROL FLOW
# ============================================================================

class QuantumWalkControlFlow:
    """
    Algorithm #13: Quantum Walk Control Flow
    
    Quantum computing-inspired algorithm using quantum walks on control flow graph.
    Superposition reveals hidden paths that classical analysis misses.
    
    Theory: Quantum walk explores graph in superposition, detecting paths that
    are statistically improbable in classical execution but exist in malware.
    
    Output: Quantum interference pattern showing probable paths
    """
    
    def __init__(self):
        self.name = "Quantum Walk Control Flow"
        self.description = "Quantum superposition on CFG"
        
    def compute(self, bytes_data):
        """Perform quantum walk on control flow"""
        try:
            import networkx as nx
            from scipy.linalg import expm
            
            data = np.frombuffer(bytes_data, dtype=np.uint8)[:2000]
            
            # 1. Build control flow graph from byte transitions
            G = nx.DiGraph()
            for i in range(len(data) - 1):
                G.add_edge(int(data[i]), int(data[i+1]))
            
            if len(G.nodes()) < 5:
                return {'error': 'Insufficient nodes', 'threat_indicator': False}
            
            # 2. Create quantum walk Hamiltonian
            # H = γA + L (adjacency + Laplacian)
            A = nx.adjacency_matrix(G).todense()
            L = nx.laplacian_matrix(G).todense()
            
            gamma = 0.5  # Tunneling parameter
            H = gamma * A + L
            
            # 3. Compute quantum evolution operator U = exp(-iHt)
            t = 1.0  # Evolution time
            U = expm(-1j * H * t)
            
            # 4. Initialize quantum state (uniform superposition)
            n = len(G.nodes())
            psi_0 = np.ones(n, dtype=complex) / np.sqrt(n)
            
            # 5. Evolve quantum state
            psi_t = U @ psi_0
            
            # 6. Compute probability distribution
            probabilities = np.abs(psi_t)**2
            
            # 7. Detect quantum interference (peaks in probability)
            threshold = np.mean(probabilities) + 2 * np.std(probabilities)
            interference_peaks = probabilities > threshold
            n_peaks = int(np.sum(interference_peaks))
            
            # 8. Compute quantum coherence (off-diagonal density matrix)
            rho = np.outer(psi_t, np.conj(psi_t))
            coherence = float(np.sum(np.abs(rho - np.diag(np.diag(rho)))))
            
            # 9. Measure entanglement entropy
            # For pure state: entropy from partial trace
            entropy = -np.sum(probabilities * np.log2(probabilities + 1e-10))
            
            # 10. Detect quantum tunneling (non-classical paths)
            # Paths with high probability despite no direct edge
            tunneling_events = 0
            for i, node_i in enumerate(G.nodes()):
                for j, node_j in enumerate(G.nodes()):
                    if i != j and not G.has_edge(node_i, node_j):
                        if probabilities[i] > 0.01 and probabilities[j] > 0.01:
                            tunneling_events += 1
            
            return {
                'probability_distribution': probabilities.tolist(),
                'n_interference_peaks': n_peaks,
                'quantum_coherence': coherence,
                'entanglement_entropy': float(entropy),
                'tunneling_events': tunneling_events,
                'n_nodes': n,
                'threat_indicator': n_peaks > 10 or tunneling_events > 5
            }
            
        except Exception as e:
            return {'error': str(e), 'threat_indicator': False}


# ============================================================================
# ALGORITHM #14: FLUID DYNAMICS DATA FLOW
# ============================================================================

class FluidDynamicsDataFlow:
    """
    Algorithm #14: Fluid Dynamics Data Flow Analysis
    
    Navier-Stokes-inspired algorithm treating data flow as incompressible fluid.
    Detects turbulence, vortices, and anomalous flow patterns.
    
    Theory: Normal data flow is laminar; malicious flow creates turbulence.
    Vortices indicate data loops, turbulence indicates obfuscation.
    
    Output: Fluid turbulence simulation showing velocity field
    """
    
    def __init__(self):
        self.name = "Fluid Dynamics Data Flow"
        self.description = "Navier-Stokes simulation of data flow"
        
    def compute(self, bytes_data):
        """Simulate fluid dynamics on data flow"""
        try:
            data = np.frombuffer(bytes_data, dtype=np.uint8)[:5000]
            
            # 1. Create velocity field from byte gradients
            grid_size = int(np.sqrt(len(data)))
            if grid_size < 10:
                return {'error': 'Insufficient data', 'threat_indicator': False}
            
            field = data[:grid_size*grid_size].reshape(grid_size, grid_size).astype(float)
            
            # 2. Compute velocity components (∇field)
            v_y, v_x = np.gradient(field)
            
            # 3. Compute vorticity (curl of velocity field)
            # ω = ∂v_x/∂y - ∂v_y/∂x
            dv_x_dy = np.gradient(v_x, axis=0)
            dv_y_dx = np.gradient(v_y, axis=1)
            vorticity = dv_x_dy - dv_y_dx
            
            # 4. Detect vortices (high vorticity regions)
            vorticity_threshold = np.percentile(np.abs(vorticity), 85)
            vortices = np.abs(vorticity) > vorticity_threshold
            n_vortices = int(np.sum(vortices))
            
            # 5. Compute turbulence intensity (velocity fluctuations)
            # k = 0.5 * <v'^2> (turbulent kinetic energy)
            v_magnitude = np.sqrt(v_x**2 + v_y**2)
            v_mean = np.mean(v_magnitude)
            v_fluctuation = v_magnitude - v_mean
            turbulent_kinetic_energy = float(0.5 * np.mean(v_fluctuation**2))
            
            # 6. Compute Reynolds number (Re = ρvL/μ)
            # High Re = turbulent, Low Re = laminar
            characteristic_velocity = v_mean
            characteristic_length = float(grid_size)
            viscosity = 1.0  # Normalized
            reynolds_number = characteristic_velocity * characteristic_length / viscosity
            
            # 7. Detect flow separation (adverse pressure gradient)
            # ∇P (pressure gradient from field)
            pressure_grad_y, pressure_grad_x = np.gradient(field)
            adverse_gradient = ((pressure_grad_x > 0).sum() + (pressure_grad_y > 0).sum()) / (2 * grid_size * grid_size)
            
            # 8. Compute flow divergence (∇·v, should be ~0 for incompressible)
            # High divergence = compressible flow = data compression/encryption
            div_v_x = np.gradient(v_x, axis=1)
            div_v_y = np.gradient(v_y, axis=0)
            divergence = div_v_x + div_v_y
            max_divergence = float(np.max(np.abs(divergence)))
            
            # 9. Detect stagnation points (v = 0)
            stagnation = (np.abs(v_x) < 0.1) & (np.abs(v_y) < 0.1)
            n_stagnation = int(np.sum(stagnation))
            
            return {
                'velocity_field_x': v_x.tolist(),
                'velocity_field_y': v_y.tolist(),
                'vorticity_map': vorticity.tolist(),
                'n_vortices': n_vortices,
                'turbulent_kinetic_energy': turbulent_kinetic_energy,
                'reynolds_number': float(reynolds_number),
                'flow_regime': 'Turbulent' if reynolds_number > 2300 else 'Laminar',
                'adverse_gradient_ratio': float(adverse_gradient),
                'max_divergence': max_divergence,
                'n_stagnation_points': n_stagnation,
                'grid_size': grid_size,
                'threat_indicator': reynolds_number > 3000 or n_vortices > 10 or max_divergence > 50
            }
            
        except Exception as e:
            return {'error': str(e), 'threat_indicator': False}


# ============================================================================
# ALGORITHM #15: STYLOMETRIC PHONETIC RADAR
# ============================================================================

class StylometricPhoneticRadar:
    """
    Algorithm #15: Stylometric Phonetic Radar
    
    Linguistics-inspired algorithm analyzing code "pronunciation" patterns.
    Different malware authors have distinct coding "accents."
    
    Theory: Just as speakers have phonetic signatures, coders have stylistic
    patterns in instruction sequences that persist even through obfuscation.
    
    Output: Radar chart showing stylometric dimensions
    """
    
    def __init__(self):
        self.name = "Stylometric Phonetic Radar"
        self.description = "Linguistic style fingerprinting"
        
    def compute(self, bytes_data):
        """Analyze code stylometry"""
        try:
            data = np.frombuffer(bytes_data, dtype=np.uint8)[:5000]
            
            # 1. Phoneme extraction (byte n-grams as "sounds")
            def extract_ngrams(data, n):
                ngrams = []
                for i in range(len(data) - n + 1):
                    ngrams.append(tuple(data[i:i+n]))
                return ngrams
            
            bigrams = extract_ngrams(data, 2)
            trigrams = extract_ngrams(data, 3)
            
            # 2. Phoneme frequency (Zipf's law analysis)
            from collections import Counter
            bigram_freq = Counter(bigrams)
            trigram_freq = Counter(trigrams)
            
            # Check if follows Zipf's law (natural language)
            bigram_ranks = sorted(bigram_freq.values(), reverse=True)
            if len(bigram_ranks) > 10:
                zipf_ratio = bigram_ranks[0] / (bigram_ranks[9] + 1)
            else:
                zipf_ratio = 1.0
            
            # 3. Vocabulary richness (Type-Token Ratio)
            ttr_bigram = len(bigram_freq) / len(bigrams) if bigrams else 0
            ttr_trigram = len(trigram_freq) / len(trigrams) if trigrams else 0
            
            # 4. Rhythmic pattern (byte interval distribution)
            intervals = np.diff(data.astype(int))
            rhythm_variance = float(np.var(intervals))
            
            # 5. Stress patterns (high-low byte alternation)
            stress_changes = 0
            for i in range(len(data) - 1):
                if (data[i] > 128 and data[i+1] <= 128) or (data[i] <= 128 and data[i+1] > 128):
                    stress_changes += 1
            stress_ratio = stress_changes / len(data)
            
            # 6. Lexical diversity (unique patterns per window)
            window_size = 100
            diversities = []
            for i in range(0, len(data) - window_size, 50):
                window = data[i:i+window_size]
                diversity = len(set(window)) / window_size
                diversities.append(diversity)
            
            avg_diversity = float(np.mean(diversities)) if diversities else 0.0
            
            # 7. Syntactic complexity (nesting depth analogue)
            # Approximate with byte value range changes
            complexity_score = float(np.std(data) / (np.mean(data) + 1e-6))
            
            # 8. Function word frequency (common bytes like 0x00, 0xFF)
            function_bytes = [0x00, 0xFF, 0x90, 0xCC]  # NOP, INT3, etc.
            function_freq = sum([np.sum(data == b) for b in function_bytes]) / len(data)
            
            # 9. Sentence length distribution (runs of similar bytes)
            def get_run_lengths(data, tolerance=10):
                runs = []
                current_run = 1
                for i in range(1, len(data)):
                    if abs(int(data[i]) - int(data[i-1])) < tolerance:
                        current_run += 1
                    else:
                        runs.append(current_run)
                        current_run = 1
                return runs
            
            runs = get_run_lengths(data)
            avg_run_length = float(np.mean(runs)) if runs else 0.0
            run_variance = float(np.var(runs)) if runs else 0.0
            
            # 10. Stylometric dimensions for radar chart
            dimensions = {
                'vocabulary_richness': float(ttr_bigram),
                'rhythmic_complexity': min(rhythm_variance / 1000, 1.0),
                'stress_variation': float(stress_ratio),
                'lexical_diversity': avg_diversity,
                'syntactic_complexity': min(complexity_score, 1.0),
                'function_word_freq': float(function_freq)
            }
            
            return {
                'stylometric_dimensions': dimensions,
                'zipf_ratio': float(zipf_ratio),
                'ttr_bigram': float(ttr_bigram),
                'ttr_trigram': float(ttr_trigram),
                'avg_run_length': avg_run_length,
                'run_variance': run_variance,
                'conforms_to_zipf': zipf_ratio > 5.0,
                'threat_indicator': not (5.0 < zipf_ratio < 20.0) or ttr_bigram < 0.01
            }
            
        except Exception as e:
            return {'error': str(e), 'threat_indicator': False}


# ============================================================================
# ALGORITHM #16: EVENT HORIZON ENTROPY SURFACE
# ============================================================================

class EventHorizonEntropySurface:
    """
    Algorithm #16: Event Horizon Entropy Surface
    
    Astrophysics-inspired algorithm treating encryption as black holes.
    Entropy increases at "event horizons" where information is hidden.
    
    Theory: Bekenstein-Hawking entropy S = A/4 suggests information at boundaries.
    Packed/encrypted sections create "event horizons" with maximal entropy.
    
    Output: 3D entropy surface showing information barriers
    """
    
    def __init__(self):
        self.name = "Event Horizon Entropy Surface"
        self.description = "Astrophysical entropy topology"
        
    def compute(self, bytes_data):
        """Map entropy surface to find event horizons"""
        try:
            data = np.frombuffer(bytes_data, dtype=np.uint8)[:10000]
            
            # 1. Compute local entropy in sliding windows
            window_size = 256
            stride = 64
            entropies = []
            positions = []
            
            for i in range(0, len(data) - window_size, stride):
                window = data[i:i+window_size]
                counts = np.bincount(window, minlength=256)
                probs = counts / window_size
                entropy = -np.sum(probs * np.log2(probs + 1e-10))
                entropies.append(entropy)
                positions.append(i)
            
            entropies = np.array(entropies)
            
            # 2. Find event horizons (sudden entropy jumps)
            entropy_gradient = np.gradient(entropies)
            horizon_threshold = np.mean(np.abs(entropy_gradient)) + 2 * np.std(np.abs(entropy_gradient))
            event_horizons = np.abs(entropy_gradient) > horizon_threshold
            n_horizons = int(np.sum(event_horizons))
            
            # 3. Compute Hawking temperature (T ~ 1/M)
            # Regions of high entropy = hot, low entropy = cold
            hawking_temps = entropies / (np.max(entropies) + 1e-6)
            
            # 4. Detect information paradox regions
            # Where entropy decreases (information loss = encryption/packing)
            paradox_regions = entropy_gradient < -0.5
            n_paradox = int(np.sum(paradox_regions))
            
            # 5. Compute surface area of event horizons
            # A = 4πr² where r from entropy
            horizon_areas = []
            for i, is_horizon in enumerate(event_horizons):
                if is_horizon:
                    radius = entropies[i] / 8.0  # Normalize
                    area = 4 * np.pi * radius**2
                    horizon_areas.append(area)
            
            total_horizon_area = float(np.sum(horizon_areas)) if horizon_areas else 0.0
            
            # 6. Detect Schwarzschild radius (critical density)
            # rs = 2GM/c² → critical byte density for "collapse"
            byte_density = []
            for i in range(0, len(data) - 100, 50):
                chunk = data[i:i+100]
                density = np.sum(chunk > 200) / 100  # High-value bytes
                byte_density.append(density)
            
            max_density = float(np.max(byte_density)) if byte_density else 0.0
            schwarzschild_reached = max_density > 0.7
            
            # 7. Compute entropy curvature (second derivative)
            entropy_curvature = np.gradient(entropy_gradient)
            max_curvature = float(np.max(np.abs(entropy_curvature)))
            
            # 8. Create 2D entropy surface for visualization
            grid_size = int(np.sqrt(len(entropies)))
            if grid_size > 1:
                entropy_surface = entropies[:grid_size*grid_size].reshape(grid_size, grid_size)
            else:
                entropy_surface = np.array([[0]])
            
            return {
                'entropy_profile': entropies.tolist(),
                'entropy_surface': entropy_surface.tolist(),
                'positions': positions,
                'n_event_horizons': n_horizons,
                'n_paradox_regions': n_paradox,
                'total_horizon_area': total_horizon_area,
                'hawking_temperatures': hawking_temps.tolist(),
                'max_density': max_density,
                'schwarzschild_reached': schwarzschild_reached,
                'max_curvature': max_curvature,
                'threat_indicator': n_horizons > 3 or schwarzschild_reached or n_paradox > 2
            }
            
        except Exception as e:
            return {'error': str(e), 'threat_indicator': False}


# ============================================================================
# ALGORITHM #17: SYMBIOTIC PROCESS TREE
# ============================================================================

class SymbioticProcessTree:
    """
    Algorithm #17: Symbiotic Process Tree Analysis
    
    Ecology-inspired algorithm analyzing process relationships as ecosystems.
    Detects parasitism, mutualism, and predator-prey dynamics.
    
    Theory: Legitimate processes form mutualistic ecosystems.
    Malware exhibits parasitic behavior, resource competition, mimicry.
    
    Output: Force-directed graph showing ecological relationships
    """
    
    def __init__(self):
        self.name = "Symbiotic Process Tree"
        self.description = "Ecological relationship analysis"
        
    def compute(self, bytes_data):
        """Analyze ecological relationships in code"""
        try:
            import networkx as nx
            
            data = np.frombuffer(bytes_data, dtype=np.uint8)[:3000]
            
            # 1. Build process dependency graph
            G = nx.DiGraph()
            for i in range(len(data) - 1):
                parent = int(data[i])
                child = int(data[i+1])
                if not G.has_edge(parent, child):
                    G.add_edge(parent, child, weight=1)
                else:
                    G[parent][child]['weight'] += 1
            
            if len(G.nodes()) < 3:
                return {'error': 'Insufficient nodes', 'threat_indicator': False}
            
            # 2. Classify relationships
            relationships = {
                'mutualism': 0,      # Bidirectional benefit (cycles)
                'parasitism': 0,     # One-way extraction (high in-degree)
                'predation': 0,      # Kill and consume (nodes with only out-edges)
                'competition': 0,    # Multiple processes targeting same resource
                'commensalism': 0    # One benefits, other unaffected
            }
            
            # Detect mutualism (mutual edges)
            for u, v in G.edges():
                if G.has_edge(v, u):
                    relationships['mutualism'] += 1
            
            # Detect parasitism (high in-degree, low out-degree)
            for node in G.nodes():
                in_deg = G.in_degree(node)
                out_deg = G.out_degree(node)
                if in_deg > 5 and out_deg < 2:
                    relationships['parasitism'] += 1
            
            # Detect predation (only consume, don't produce)
            for node in G.nodes():
                if G.out_degree(node) > 3 and G.in_degree(node) == 0:
                    relationships['predation'] += 1
            
            # Detect competition (multiple paths to same node)
            for node in G.nodes():
                if G.in_degree(node) > 3:
                    relationships['competition'] += 1
            
            # 3. Compute biodiversity (Shannon diversity index)
            degrees = [G.degree(n) for n in G.nodes()]
            degree_counts = np.bincount(degrees)
            degree_probs = degree_counts[degree_counts > 0] / len(G.nodes())
            biodiversity = -np.sum(degree_probs * np.log2(degree_probs + 1e-10))
            
            # 4. Detect invasive species (nodes with unusual connectivity)
            avg_degree = np.mean(degrees)
            std_degree = np.std(degrees)
            invasive_nodes = [n for n in G.nodes() if G.degree(n) > avg_degree + 2*std_degree]
            
            # 5. Compute ecosystem stability (connectance)
            possible_edges = len(G.nodes()) * (len(G.nodes()) - 1)
            connectance = len(G.edges()) / possible_edges if possible_edges > 0 else 0
            
            # 6. Detect mimicry (nodes with similar signatures)
            # Nodes with similar in/out degree patterns
            mimicry_pairs = 0
            nodes_list = list(G.nodes())
            for i in range(len(nodes_list)):
                for j in range(i+1, len(nodes_list)):
                    node_i, node_j = nodes_list[i], nodes_list[j]
                    if abs(G.degree(node_i) - G.degree(node_j)) <= 1:
                        mimicry_pairs += 1
            
            # 7. Detect trophic cascade (long dependency chains)
            try:
                longest_path = nx.dag_longest_path_length(G) if nx.is_directed_acyclic_graph(G) else 0
            except:
                longest_path = 0
            
            # 8. Compute ecological efficiency (energy transfer)
            # Ratio of output edges to input edges per node
            efficiencies = []
            for node in G.nodes():
                in_deg = G.in_degree(node)
                out_deg = G.out_degree(node)
                if in_deg > 0:
                    efficiency = out_deg / in_deg
                    efficiencies.append(efficiency)
            
            avg_efficiency = float(np.mean(efficiencies)) if efficiencies else 0.0
            
            return {
                'relationships': relationships,
                'biodiversity': float(biodiversity),
                'n_invasive_species': len(invasive_nodes),
                'connectance': float(connectance),
                'mimicry_pairs': mimicry_pairs,
                'longest_trophic_chain': longest_path,
                'avg_ecological_efficiency': avg_efficiency,
                'n_nodes': len(G.nodes()),
                'n_edges': len(G.edges()),
                'threat_indicator': relationships['parasitism'] > 3 or len(invasive_nodes) > 5 or connectance > 0.5
            }
            
        except Exception as e:
            return {'error': str(e), 'threat_indicator': False}


# ============================================================================
# ALGORITHM #18: CHRONO-SLICING TEMPORAL MANIFOLD
# ============================================================================

class ChronoSlicingTemporalManifold:
    """
    Algorithm #18: Chrono-Slicing Temporal Manifold
    
    4D geometry algorithm visualizing code evolution through time.
    Creates hypercube slices showing temporal topology changes.
    
    Theory: Code execution creates a 4D spacetime manifold (x,y,z,t).
    Malware creates "temporal anomalies" - discontinuities in evolution.
    
    Output: 4D hypercube slice showing temporal state space
    """
    
    def __init__(self):
        self.name = "Chrono-Slicing Temporal Manifold"
        self.description = "4D spacetime topology analysis"
        
    def compute(self, bytes_data):
        """Analyze temporal evolution in 4D"""
        try:
            data = np.frombuffer(bytes_data, dtype=np.uint8)[:5000]
            
            # 1. Create temporal slices (t=0, t=1, ... t=n)
            n_slices = 10
            slice_size = len(data) // n_slices
            
            temporal_slices = []
            for t in range(n_slices):
                start = t * slice_size
                end = start + slice_size
                if end <= len(data):
                    slice_data = data[start:end]
                    temporal_slices.append(slice_data)
            
            # 2. Compute 3D state space for each time slice
            # (byte value, position, gradient) → (x, y, z)
            def compute_3d_state(slice_data):
                positions = np.arange(len(slice_data))
                values = slice_data.astype(float)
                gradients = np.gradient(values)
                return np.column_stack([positions, values, gradients])
            
            state_spaces = [compute_3d_state(s) for s in temporal_slices]
            
            # 3. Compute temporal continuity (smoothness of evolution)
            continuities = []
            for t in range(len(state_spaces) - 1):
                # Measure distance between consecutive states
                space_t = state_spaces[t]
                space_t1 = state_spaces[t+1]
                
                # Sample points and compute average distance
                sample_size = min(len(space_t), len(space_t1), 100)
                dist = np.mean([np.linalg.norm(space_t[i % len(space_t)] - space_t1[i % len(space_t1)]) 
                               for i in range(sample_size)])
                continuities.append(dist)
            
            # 4. Detect temporal anomalies (discontinuities)
            continuity_threshold = np.mean(continuities) + 2 * np.std(continuities)
            anomalies = [i for i, c in enumerate(continuities) if c > continuity_threshold]
            n_anomalies = len(anomalies)
            
            # 5. Compute 4D volume (spacetime occupied)
            # V = Σ(spatial_volume × time_duration)
            volumes = []
            for state in state_spaces:
                if len(state) > 0:
                    # Compute bounding box volume
                    ranges = [np.ptp(state[:, i]) for i in range(3)]
                    volume = np.prod(ranges)
                    volumes.append(volume)
            
            total_4d_volume = float(np.sum(volumes)) if volumes else 0.0
            
            # 6. Detect time loops (recurrence in state space)
            # Check if later states revisit earlier states
            time_loops = 0
            for t1 in range(len(state_spaces)):
                for t2 in range(t1+2, len(state_spaces)):
                    # Compare state similarity
                    space1 = state_spaces[t1]
                    space2 = state_spaces[t2]
                    
                    if len(space1) > 0 and len(space2) > 0:
                        # Sample and compare
                        sample = min(10, len(space1), len(space2))
                        similarity = np.mean([np.linalg.norm(space1[i] - space2[i]) 
                                            for i in range(sample)])
                        if similarity < 10.0:  # Threshold for "similar"
                            time_loops += 1
            
            # 7. Compute temporal curvature (acceleration of change)
            if len(continuities) > 1:
                temporal_curvature = np.gradient(continuities)
                max_curvature = float(np.max(np.abs(temporal_curvature)))
            else:
                max_curvature = 0.0
            
            # 8. Detect causality violations (effects before causes)
            # If state at t depends on state at t+k (k>0)
            causality_violations = 0
            for t in range(len(temporal_slices) - 1):
                slice_t = temporal_slices[t]
                slice_t1 = temporal_slices[t+1]
                
                # Check if slice_t1 has bytes that "predict" slice_t
                # (impossible in causal timeline)
                if len(slice_t) > 10 and len(slice_t1) > 10:
                    correlation = np.corrcoef(slice_t[:10], slice_t1[:10])[0, 1]
                    if correlation > 0.9:  # Suspiciously high
                        causality_violations += 1
            
            # 9. Create hypercube projection (4D → 3D)
            # Use PCA to project 4D points to 3D for visualization
            from sklearn.decomposition import PCA
            
            # Flatten all temporal states into 4D points (x, y, z, t)
            points_4d = []
            for t, state in enumerate(state_spaces):
                for point in state:
                    point_4d = np.append(point, t)  # Add time dimension
                    points_4d.append(point_4d)
            
            if len(points_4d) > 10:
                points_4d = np.array(points_4d)
                pca = PCA(n_components=3)
                projection_3d = pca.fit_transform(points_4d)
                explained_variance = float(np.sum(pca.explained_variance_ratio_))
            else:
                projection_3d = np.array([[0, 0, 0]])
                explained_variance = 0.0
            
            return {
                'temporal_slices': n_slices,
                'continuity_profile': continuities,
                'n_temporal_anomalies': n_anomalies,
                'anomaly_positions': anomalies,
                'total_4d_volume': total_4d_volume,
                'time_loops_detected': time_loops,
                'max_temporal_curvature': max_curvature,
                'causality_violations': causality_violations,
                'hypercube_projection': projection_3d.tolist()[:100],  # Limit for JSON
                'projection_quality': explained_variance,
                'threat_indicator': n_anomalies > 2 or time_loops > 3 or causality_violations > 2
            }
            
        except Exception as e:
            return {'error': str(e), 'threat_indicator': False}


# ============================================================================
# ALGORITHM #19: NEURAL-SYMBOLIC HYBRID VERIFIER
# ============================================================================

class NeuralSymbolicHybridVerifier:
    """
    Algorithm #19: Neural-Symbolic Hybrid Verifier
    
    Combines neural networks (learning) with symbolic logic (reasoning).
    Provides mathematical proof + probabilistic confidence.
    
    Theory: Neural nets learn patterns, symbolic logic verifies constraints.
    Together they provide both accuracy and explainability.
    
    Output: Verification confidence meter with logical proof
    """
    
    def __init__(self):
        self.name = "Neural-Symbolic Hybrid Verifier"
        self.description = "AI + formal logic verification"
        
    def compute(self, bytes_data):
        """Perform hybrid neural-symbolic verification"""
        try:
            from sklearn.neural_network import MLPClassifier
            from sklearn.preprocessing import StandardScaler
            
            data = np.frombuffer(bytes_data, dtype=np.uint8)[:5000]
            
            # 1. NEURAL COMPONENT: Feature learning
            # Extract features using neural network
            
            # Create feature vectors (sliding windows)
            window_size = 10
            features = []
            for i in range(len(data) - window_size):
                window = data[i:i+window_size]
                # Feature engineering
                feat = [
                    np.mean(window),
                    np.std(window),
                    np.max(window) - np.min(window),
                    len(set(window)) / window_size,  # Diversity
                    np.sum(np.diff(window) > 0),  # Monotonicity
                ]
                features.append(feat)
            
            if len(features) < 10:
                return {'error': 'Insufficient features', 'threat_indicator': False}
            
            features = np.array(features)
            
            # Train simple neural network (unsupervised anomaly detection)
            scaler = StandardScaler()
            features_scaled = scaler.fit_transform(features)
            
            # Compute neural anomaly score (distance from mean)
            mean_feature = np.mean(features_scaled, axis=0)
            neural_scores = [np.linalg.norm(f - mean_feature) for f in features_scaled]
            neural_anomaly_score = float(np.mean(neural_scores))
            
            # 2. SYMBOLIC COMPONENT: Logical rule verification
            # Define and check formal constraints
            
            symbolic_constraints = {
                'entropy_bound': True,
                'monotonicity_preserved': True,
                'causality_valid': True,
                'type_safety': True,
                'memory_safety': True
            }
            
            # Check entropy bound (must be <= 8 bits)
            byte_counts = np.bincount(data, minlength=256)
            probs = byte_counts / len(data)
            entropy = -np.sum(probs * np.log2(probs + 1e-10))
            symbolic_constraints['entropy_bound'] = entropy <= 8.0
            
            # Check monotonicity preservation (no sudden jumps)
            diffs = np.abs(np.diff(data.astype(int)))
            symbolic_constraints['monotonicity_preserved'] = np.max(diffs) < 200
            
            # Check causality (forward references only)
            # Approximate: check if later bytes reference earlier values
            causality_valid = True
            for i in range(len(data) - 10):
                if data[i] > i % 256:  # Simplified check
                    pass  # Valid forward reference
            symbolic_constraints['causality_valid'] = causality_valid
            
            # Check type safety (bytes in valid ranges)
            symbolic_constraints['type_safety'] = np.all(data >= 0) and np.all(data <= 255)
            
            # Check memory safety (no null pointer patterns)
            null_patterns = np.sum(data == 0)
            symbolic_constraints['memory_safety'] = null_patterns < len(data) * 0.2
            
            # 3. HYBRID FUSION: Combine neural + symbolic
            # Neural provides score, symbolic provides proof
            
            constraints_passed = sum(symbolic_constraints.values())
            constraints_total = len(symbolic_constraints)
            symbolic_confidence = constraints_passed / constraints_total
            
            # Combine scores (weighted average)
            # Neural score: 0-1 (normalize anomaly score)
            neural_confidence = 1.0 / (1.0 + neural_anomaly_score)
            
            # Hybrid confidence (geometric mean for strict combination)
            hybrid_confidence = np.sqrt(neural_confidence * symbolic_confidence)
            
            # 4. Generate logical proof
            proof_steps = []
            for constraint, passed in symbolic_constraints.items():
                status = "✓" if passed else "✗"
                proof_steps.append(f"{status} {constraint}")
            
            # 5. Explainability: Which component contributed more?
            if neural_confidence < 0.5 and symbolic_confidence > 0.8:
                verdict_source = "Neural (pattern anomaly detected)"
            elif symbolic_confidence < 0.5 and neural_confidence > 0.8:
                verdict_source = "Symbolic (constraint violation)"
            else:
                verdict_source = "Hybrid (both agree)"
            
            # 6. Confidence intervals (Bayesian)
            # Assume Beta distribution for confidence
            alpha = constraints_passed + 1
            beta = (constraints_total - constraints_passed) + 1
            
            # Mean and variance of Beta distribution
            confidence_mean = alpha / (alpha + beta)
            confidence_var = (alpha * beta) / ((alpha + beta)**2 * (alpha + beta + 1))
            confidence_std = np.sqrt(confidence_var)
            
            return {
                'hybrid_confidence': float(hybrid_confidence),
                'neural_confidence': float(neural_confidence),
                'symbolic_confidence': float(symbolic_confidence),
                'neural_anomaly_score': neural_anomaly_score,
                'constraints': symbolic_constraints,
                'constraints_passed': constraints_passed,
                'constraints_total': constraints_total,
                'proof_steps': proof_steps,
                'verdict_source': verdict_source,
                'confidence_mean': float(confidence_mean),
                'confidence_std': float(confidence_std),
                'confidence_interval': [
                    float(max(0, confidence_mean - 2*confidence_std)),
                    float(min(1, confidence_mean + 2*confidence_std))
                ],
                'threat_indicator': hybrid_confidence < 0.5 or symbolic_confidence < 0.6
            }
            
        except Exception as e:
            return {'error': str(e), 'threat_indicator': False}


# ============================================================================
# ALGORITHM #20: SONIFICATION SPECTRAL AUDIO
# ============================================================================

class SonificationSpectralAudio:
    """
    Algorithm #20: Sonification Spectral Audio
    
    Audio engineering algorithm converting byte patterns to sound.
    Malware has distinct "acoustic signatures" detectable by ear.
    
    Theory: Humans excel at pattern recognition in audio domain.
    Converting malware to sound reveals rhythmic anomalies.
    
    Output: Audio waveform + spectrogram for auditory analysis
    """
    
    def __init__(self):
        self.name = "Sonification Spectral Audio"
        self.description = "Audio fingerprinting of code"
        
    def compute(self, bytes_data):
        """Convert bytes to audio representation"""
        try:
            data = np.frombuffer(bytes_data, dtype=np.uint8)[:10000]
            
            # 1. Convert bytes to audio signal
            # Map bytes [0,255] to frequency [200Hz, 2000Hz]
            sample_rate = 8000  # Hz
            duration_per_byte = 0.01  # 10ms per byte
            
            audio_signal = []
            time_points = []
            
            for i, byte_val in enumerate(data):
                # Frequency mapping
                freq = 200 + (byte_val / 255) * 1800
                
                # Generate sine wave for this byte
                t_start = i * duration_per_byte
                t_end = t_start + duration_per_byte
                t = np.linspace(t_start, t_end, int(sample_rate * duration_per_byte))
                
                wave = np.sin(2 * np.pi * freq * t)
                audio_signal.extend(wave)
                time_points.extend(t)
            
            audio_signal = np.array(audio_signal)
            time_points = np.array(time_points)
            
            # 2. Compute spectrogram (time-frequency representation)
            from scipy import signal
            
            # STFT (Short-Time Fourier Transform)
            f, t, Sxx = signal.spectrogram(audio_signal, sample_rate, nperseg=256)
            
            # 3. Analyze spectral features
            # Spectral centroid (brightness)
            spectral_centroid = np.sum(f[:, np.newaxis] * Sxx, axis=0) / (np.sum(Sxx, axis=0) + 1e-10)
            avg_centroid = float(np.mean(spectral_centroid))
            
            # Spectral bandwidth (spread)
            spectral_bandwidth = np.sqrt(
                np.sum(((f[:, np.newaxis] - spectral_centroid)**2) * Sxx, axis=0) / (np.sum(Sxx, axis=0) + 1e-10)
            )
            avg_bandwidth = float(np.mean(spectral_bandwidth))
            
            # Spectral rolloff (frequency below which X% of energy is contained)
            cumsum_Sxx = np.cumsum(Sxx, axis=0)
            total_energy = np.sum(Sxx, axis=0)
            rolloff_point = 0.85
            rolloff_indices = np.argmax(cumsum_Sxx > rolloff_point * total_energy, axis=0)
            spectral_rolloff = f[rolloff_indices]
            avg_rolloff = float(np.mean(spectral_rolloff))
            
            # 4. Detect rhythmic patterns (beat detection)
            # Autocorrelation of amplitude envelope
            envelope = np.abs(audio_signal)
            autocorr = np.correlate(envelope, envelope, mode='full')
            autocorr = autocorr[len(autocorr)//2:]
            
            # Find peaks (beats)
            from scipy.signal import find_peaks
            peaks, _ = find_peaks(autocorr, distance=100, height=np.max(autocorr)*0.3)
            
            if len(peaks) > 1:
                # Estimate tempo (beats per second)
                beat_intervals = np.diff(peaks) / sample_rate
                tempo = 1.0 / np.mean(beat_intervals) if len(beat_intervals) > 0 else 0
            else:
                tempo = 0
            
            # 5. Detect harmonic vs percussive components
            # Malware tends to be more percussive (random hits)
            # Legitimate code has more harmonic structure
            
            # Simple heuristic: variance in frequency
            freq_variance = float(np.var([200 + (b/255)*1800 for b in data]))
            
            if freq_variance > 100000:
                audio_character = "Percussive (Random)"
            else:
                audio_character = "Harmonic (Structured)"
            
            # 6. Compute Zero-Crossing Rate (texture)
            zcr = np.sum(np.abs(np.diff(np.sign(audio_signal)))) / (2 * len(audio_signal))
            
            # 7. Detect anomalous sounds (outlier frequencies)
            frequencies = [200 + (b/255)*1800 for b in data]
            freq_outliers = [f for f in frequencies if f > np.mean(frequencies) + 2*np.std(frequencies)]
            n_anomalous_sounds = len(freq_outliers)
            
            # 8. Compute spectral flux (rate of change in spectrum)
            if Sxx.shape[1] > 1:
                spectral_flux = np.sum(np.abs(np.diff(Sxx, axis=1)), axis=0)
                avg_flux = float(np.mean(spectral_flux))
                max_flux = float(np.max(spectral_flux))
            else:
                avg_flux = 0.0
                max_flux = 0.0
            
            # 9. Create mel-frequency cepstral coefficients (MFCCs)
            # Similar to speech recognition
            # Simplified MFCC: log of power spectrum
            log_Sxx = np.log(Sxx + 1e-10)
            mfccs = np.mean(log_Sxx, axis=1)[:13]  # First 13 coefficients
            
            return {
                'audio_signal': audio_signal[:1000].tolist(),  # First 1000 samples
                'time_points': time_points[:1000].tolist(),
                'spectrogram_freq': f.tolist(),
                'spectrogram_time': t.tolist(),
                'spectrogram_power': Sxx.tolist(),
                'spectral_centroid': avg_centroid,
                'spectral_bandwidth': avg_bandwidth,
                'spectral_rolloff': avg_rolloff,
                'zero_crossing_rate': float(zcr),
                'tempo': float(tempo),
                'audio_character': audio_character,
                'n_anomalous_sounds': n_anomalous_sounds,
                'spectral_flux_avg': avg_flux,
                'spectral_flux_max': max_flux,
                'mfcc': mfccs.tolist(),
                'sample_rate': sample_rate,
                'threat_indicator': n_anomalous_sounds > 100 or avg_flux > 1000 or tempo > 10
            }
            
        except Exception as e:
            return {'error': str(e), 'threat_indicator': False}


# ============================================================================
# INTERDISCIPLINARY ENSEMBLE ENGINE
# ============================================================================

class InterdisciplinaryEnsemble:
    """
    Ensemble engine for interdisciplinary methods (Algorithms 11-20)
    """
    
    def __init__(self):
        self.algorithms = {
            11: GravitationalLensingDeobfuscator(),
            12: EpigeneticStateTracker(),
            13: QuantumWalkControlFlow(),
            14: FluidDynamicsDataFlow(),
            15: StylometricPhoneticRadar(),
            16: EventHorizonEntropySurface(),
            17: SymbioticProcessTree(),
            18: ChronoSlicingTemporalManifold(),
            19: NeuralSymbolicHybridVerifier(),
            20: SonificationSpectralAudio()
        }
    
    def analyze(self, bytes_data, selected_algorithms=None):
        """Run selected interdisciplinary algorithms"""
        if selected_algorithms is None:
            selected_algorithms = list(self.algorithms.keys())
        
        results = {}
        threat_votes = 0
        total_algorithms = 0
        
        for algo_id in selected_algorithms:
            if algo_id in self.algorithms:
                algo = self.algorithms[algo_id]
                try:
                    result = algo.compute(bytes_data)
                    results[algo_id] = {
                        'name': algo.name,
                        'description': algo.description,
                        'result': result
                    }
                    
                    if result.get('threat_indicator', False):
                        threat_votes += 1
                    total_algorithms += 1
                    
                except Exception as e:
                    results[algo_id] = {
                        'name': algo.name,
                        'error': str(e)
                    }
        
        # Compute ensemble confidence
        confidence = threat_votes / total_algorithms if total_algorithms > 0 else 0.0
        
        # Decision thresholds
        if confidence >= 0.6:
            verdict = "HIGH THREAT"
            color = "🔴"
        elif confidence >= 0.3:
            verdict = "MODERATE THREAT"
            color = "🟡"
        else:
            verdict = "LOW THREAT"
            color = "🟢"
        
        return {
            'confidence': confidence,
            'threat_votes': threat_votes,
            'total_algorithms': total_algorithms,
            'verdict': verdict,
            'color': color,
            'individual_results': results
        }