Cybersécurité Aérienne - Drones Sécurisés
Sécurisation des systèmes de drones autonomes contre les cyberattaques et vulnérabilités.
Vision Cybersécurité
Mission Critique
"Développer des systèmes de drones avec sécurité de niveau militaire pour applications civiles critiques."
Menaces Identifiées
- GPS Spoofing : Falsification coordonnées navigation
- Communication Hijacking : Interception commandes de vol
- Malware Embarqué : Infection système de contrôle
- Déni de Service : Perturbation communications critiques
- Intrusion Physique : Accès non autorisé au matériel
Architecture Sécurisée
Modèle Zero Trust
graph TB
subgraph ThrustBoundary["🛡️ Zero Trust Perimeter"]
subgraph GroundStation["🏢 Station Sol Sécurisée"]
Operator["Opérateur<br/>Authentifié"]
SecureTerminal["Terminal Sécurisé<br/>Hardware HSM"]
CertAuth["Autorité<br/>Certification"]
end
subgraph SecureComm["🔐 Communications Chiffrées"]
Encryption["Chiffrement AES-256<br/>Clés Ephémères"]
VPN["VPN IPSec<br/>Tunnel Sécurisé"]
Mesh["Mesh Network<br/>Redondance"]
end
subgraph DroneSecure["🚁 Drone Sécurisé"]
HSM["Hardware Security<br/>Module Intégré"]
SecureBoot["Secure Boot<br/>Signature Verified"]
IDS["Intrusion Detection<br/>Temps Réel"]
Firewall["Firewall Embarqué<br/>Filtrage Trafic"]
end
subgraph DataProtection["🔒 Protection Données"]
Encryption_Storage["Chiffrement<br/>Données Rest"]
KeyManagement["Gestion Clés<br/>Distribuée"]
BackupSecure["Backup Chiffré<br/>Multi-Site"]
end
end
subgraph ThreatMonitoring["⚠️ Surveillance Menaces"]
SIEM["SIEM Centralisé<br/>Corrélation Events"]
ThreatIntel["Threat Intelligence<br/>Signatures MAJ"]
IncidentResponse["Réponse Incident<br/>Automatisée"]
end
%% Flux sécurisés
Operator --> SecureTerminal
SecureTerminal --> Encryption
Encryption --> VPN
VPN --> Mesh
Mesh --> HSM
HSM --> SecureBoot
SecureBoot --> IDS
IDS --> Firewall
%% Monitoring
DroneSecure --> SIEM
SecureComm --> SIEM
SIEM --> ThreatIntel
ThreatIntel --> IncidentResponse
%% Protection données
DroneSecure --> DataProtection
DataProtection --> BackupSecure
classDef secure fill:#10b981,stroke:#047857,color:#fff
classDef threat fill:#ef4444,stroke:#dc2626,color:#fff
classDef comm fill:#3b82f6,stroke:#1e40af,color:#fff
classDef data fill:#f59e0b,stroke:#d97706,color:#000
class GroundStation,DroneSecure secure
class ThreatMonitoring threat
class SecureComm comm
class DataProtection dataChiffrement Bout-en-Bout
from cryptography.hazmat.primitives.ciphers import Cipher, algorithms, modes
from cryptography.hazmat.primitives import hashes, serialization
from cryptography.hazmat.primitives.asymmetric import rsa, padding
from cryptography.hazmat.primitives.kdf.pbkdf2 import PBKDF2HMAC
import os
import json
import time
class SecureDroneComm:
"""Communication sécurisée drone avec chiffrement militaire"""
def __init__(self):
self.session_keys = {}
self.message_counter = 0
self.replay_window = set()
def generate_session_key(self, drone_id: str) -> bytes:
"""Génération clé session éphémère"""
# Génération clé AES-256 aléatoire
session_key = os.urandom(32) # 256 bits
# Stockage sécurisé avec timestamp
self.session_keys[drone_id] = {
'key': session_key,
'created_at': time.time(),
'message_count': 0
}
return session_key
def encrypt_command(self, drone_id: str, command_data: dict) -> bytes:
"""Chiffrement commande avec authentification"""
# Vérification clé session
if drone_id not in self.session_keys:
raise ValueError(f"Pas de clé session pour drone {drone_id}")
session_info = self.session_keys[drone_id]
# Rotation automatique clés (chaque 1000 messages ou 1h)
if (session_info['message_count'] > 1000 or
time.time() - session_info['created_at'] > 3600):
self.generate_session_key(drone_id)
session_info = self.session_keys[drone_id]
# Préparation message
message = {
'timestamp': time.time(),
'counter': self.message_counter,
'drone_id': drone_id,
'command': command_data
}
# Sérialisation
plaintext = json.dumps(message, separators=(',', ':')).encode('utf-8')
# Chiffrement AES-GCM (authentification intégrée)
iv = os.urandom(12) # 96 bits pour GCM
cipher = Cipher(
algorithms.AES(session_info['key']),
modes.GCM(iv)
)
encryptor = cipher.encryptor()
ciphertext = encryptor.update(plaintext) + encryptor.finalize()
# Message final avec métadonnées
encrypted_message = {
'iv': iv.hex(),
'ciphertext': ciphertext.hex(),
'tag': encryptor.tag.hex(),
'drone_id': drone_id
}
# Mise à jour compteurs
self.message_counter += 1
session_info['message_count'] += 1
return json.dumps(encrypted_message).encode('utf-8')
def decrypt_telemetry(self, encrypted_data: bytes) -> dict:
"""Déchiffrement télémétrie avec validation"""
try:
# Parse message
message = json.loads(encrypted_data.decode('utf-8'))
drone_id = message['drone_id']
# Vérification clé session
if drone_id not in self.session_keys:
raise ValueError(f"Clé session inconnue: {drone_id}")
session_key = self.session_keys[drone_id]['key']
# Reconstruction éléments chiffrement
iv = bytes.fromhex(message['iv'])
ciphertext = bytes.fromhex(message['ciphertext'])
tag = bytes.fromhex(message['tag'])
# Déchiffrement AES-GCM
cipher = Cipher(
algorithms.AES(session_key),
modes.GCM(iv, tag)
)
decryptor = cipher.decryptor()
plaintext = decryptor.update(ciphertext) + decryptor.finalize()
# Désérialisation
decrypted_data = json.loads(plaintext.decode('utf-8'))
# Validation anti-replay
msg_counter = decrypted_data['counter']
if msg_counter in self.replay_window:
raise ValueError(f"Attaque replay détectée: {msg_counter}")
# Mise à jour fenêtre anti-replay
self.replay_window.add(msg_counter)
if len(self.replay_window) > 1000:
min_counter = min(self.replay_window)
self.replay_window.remove(min_counter)
return decrypted_data
except Exception as e:
raise ValueError(f"Déchiffrement échoué: {str(e)}")
Détection d'Intrusion
Système IDS Embarqué
import numpy as np
from sklearn.ensemble import IsolationForest
from sklearn.preprocessing import StandardScaler
import threading
import queue
import time
from dataclasses import dataclass
from typing import Dict, List, Optional
@dataclass
class SecurityEvent:
timestamp: float
event_type: str
severity: str # 'low', 'medium', 'high', 'critical'
source: str
description: str
raw_data: dict
class DroneIDS:
"""Système de détection d'intrusion embarqué"""
def __init__(self):
self.models = {
'network': IsolationForest(contamination=0.1, random_state=42),
'flight_pattern': IsolationForest(contamination=0.05, random_state=42),
'system_behavior': IsolationForest(contamination=0.08, random_state=42)
}
self.scalers = {
'network': StandardScaler(),
'flight_pattern': StandardScaler(),
'system_behavior': StandardScaler()
}
self.is_trained = False
self.event_queue = queue.Queue()
self.running = False
# Baseline comportement normal
self.normal_patterns = {
'cpu_usage': (20, 80), # Min, Max normal
'memory_usage': (30, 70),
'network_latency': (10, 100),
'flight_speed': (0, 25),
'altitude_change_rate': (-5, 5)
}
def train_baseline(self, training_data: Dict[str, np.ndarray]):
"""Entraînement baseline comportement normal"""
for model_name, data in training_data.items():
if model_name in self.models:
# Normalisation
scaled_data = self.scalers[model_name].fit_transform(data)
# Entraînement modèle détection anomalies
self.models[model_name].fit(scaled_data)
self.is_trained = True
print("Baseline IDS entraîné avec succès")
def start_monitoring(self):
"""Démarrage surveillance temps réel"""
self.running = True
# Thread surveillance réseau
network_thread = threading.Thread(target=self._monitor_network)
network_thread.daemon = True
network_thread.start()
# Thread surveillance vol
flight_thread = threading.Thread(target=self._monitor_flight)
flight_thread.daemon = True
flight_thread.start()
# Thread surveillance système
system_thread = threading.Thread(target=self._monitor_system)
system_thread.daemon = True
system_thread.start()
# Thread traitement événements
event_thread = threading.Thread(target=self._process_events)
event_thread.daemon = True
event_thread.start()
def _monitor_network(self):
"""Surveillance trafic réseau"""
while self.running:
try:
# Collecte métriques réseau
network_metrics = self._collect_network_metrics()
# Détection anomalies
if self.is_trained:
anomaly_score = self._detect_network_anomaly(network_metrics)
if anomaly_score < -0.5: # Seuil anomalie
event = SecurityEvent(
timestamp=time.time(),
event_type='network_anomaly',
severity=self._calculate_severity(anomaly_score),
source='network_monitor',
description=f'Anomalie réseau détectée: score={anomaly_score:.3f}',
raw_data=network_metrics
)
self.event_queue.put(event)
time.sleep(1) # Monitoring chaque seconde
except Exception as e:
print(f"Erreur monitoring réseau: {e}")
def _monitor_flight(self):
"""Surveillance patterns de vol"""
while self.running:
try:
# Collecte données vol
flight_data = self._collect_flight_data()
# Vérifications règles métier
violations = self._check_flight_rules(flight_data)
for violation in violations:
event = SecurityEvent(
timestamp=time.time(),
event_type='flight_violation',
severity=violation['severity'],
source='flight_monitor',
description=violation['description'],
raw_data=flight_data
)
self.event_queue.put(event)
# Détection anomalies ML
if self.is_trained:
anomaly_score = self._detect_flight_anomaly(flight_data)
if anomaly_score < -0.3:
event = SecurityEvent(
timestamp=time.time(),
event_type='flight_anomaly',
severity=self._calculate_severity(anomaly_score),
source='flight_monitor',
description=f'Pattern vol anormal: score={anomaly_score:.3f}',
raw_data=flight_data
)
self.event_queue.put(event)
time.sleep(0.5) # Monitoring vol plus fréquent
except Exception as e:
print(f"Erreur monitoring vol: {e}")
def _check_flight_rules(self, flight_data: dict) -> List[dict]:
"""Vérification règles de sécurité vol"""
violations = []
# Vérification altitude max
if flight_data.get('altitude', 0) > 120: # 120m limite légale
violations.append({
'severity': 'high',
'description': f"Altitude excessive: {flight_data['altitude']}m"
})
# Vérification vitesse
if flight_data.get('speed', 0) > 30: # 30 m/s limite
violations.append({
'severity': 'medium',
'description': f"Vitesse excessive: {flight_data['speed']} m/s"
})
# Vérification zone interdite (géofencing)
if self._in_no_fly_zone(flight_data.get('position')):
violations.append({
'severity': 'critical',
'description': "Entrée en zone interdite détectée"
})
# Vérification batterie critique
if flight_data.get('battery_level', 100) < 20:
violations.append({
'severity': 'high',
'description': f"Niveau batterie critique: {flight_data['battery_level']}%"
})
return violations
def _process_events(self):
"""Traitement événements sécurité"""
while self.running:
try:
event = self.event_queue.get(timeout=1)
# Log événement
self._log_security_event(event)
# Réponse automatique selon gravité
if event.severity == 'critical':
self._handle_critical_event(event)
elif event.severity == 'high':
self._handle_high_event(event)
# Notification opérateur si nécessaire
if event.severity in ['high', 'critical']:
self._notify_operator(event)
except queue.Empty:
continue
except Exception as e:
print(f"Erreur traitement événement: {e}")
def _handle_critical_event(self, event: SecurityEvent):
"""Réponse automatique événement critique"""
if event.event_type == 'flight_violation':
# Activation RTL (Return to Launch) automatique
self._trigger_rtl()
elif event.event_type == 'network_intrusion':
# Isolation réseau
self._isolate_network()
elif event.event_type == 'gps_spoofing':
# Basculement navigation inertielle
self._switch_to_inertial_nav()
# Log action prise
print(f"Action critique prise pour: {event.description}")
Protection GPS & Navigation
Anti-Spoofing GPS
import numpy as np
from scipy import signal
import threading
import time
from dataclasses import dataclass
from typing import List, Tuple, Optional
@dataclass
class GPSSignal:
timestamp: float
satellite_id: int
signal_strength: float
pseudorange: float
carrier_phase: float
doppler_shift: float
elevation_angle: float
azimuth_angle: float
class GPSAntiSpoofing:
"""Système de protection anti-spoofing GPS"""
def __init__(self):
self.baseline_signals = {}
self.signal_history = []
self.spoofing_detected = False
self.confidence_threshold = 0.8
# Référence signaux satellites authentiques
self.authentic_constellation = self._load_constellation_data()
def analyze_gps_signals(self, signals: List[GPSSignal]) -> dict:
"""Analyse signaux GPS pour détection spoofing"""
analysis_result = {
'spoofing_probability': 0.0,
'suspicious_satellites': [],
'authentication_score': 1.0,
'recommendations': []
}
# 1. Vérification cohérence constellation
constellation_score = self._check_constellation_consistency(signals)
# 2. Analyse patterns temporels
temporal_score = self._analyze_temporal_patterns(signals)
# 3. Vérification puissance signaux
power_score = self._check_signal_power(signals)
# 4. Corrélation croisée signaux
correlation_score = self._cross_correlate_signals(signals)
# Score global spoofing
spoofing_probability = 1.0 - (
constellation_score * 0.3 +
temporal_score * 0.25 +
power_score * 0.25 +
correlation_score * 0.2
)
analysis_result['spoofing_probability'] = spoofing_probability
analysis_result['authentication_score'] = 1.0 - spoofing_probability
# Détection spoofing
if spoofing_probability > self.confidence_threshold:
self.spoofing_detected = True
analysis_result['recommendations'] = self._generate_countermeasures()
return analysis_result
def _check_constellation_consistency(self, signals: List[GPSSignal]) -> float:
"""Vérification cohérence constellation satellites"""
if not signals:
return 0.0
consistency_score = 1.0
# Vérification nombre satellites (minimum 4 pour position 3D)
if len(signals) < 4:
consistency_score *= 0.5
elif len(signals) > 12: # Trop de satellites = suspect
consistency_score *= 0.7
# Vérification géométrie constellation
elevation_angles = [s.elevation_angle for s in signals]
azimuth_angles = [s.azimuth_angle for s in signals]
# GDOP (Geometric Dilution of Precision) check
gdop = self._calculate_gdop(elevation_angles, azimuth_angles)
if gdop > 10: # GDOP élevé = géométrie suspecte
consistency_score *= 0.6
# Vérification satellites visibles selon almanach
for signal in signals:
if not self._is_satellite_visible(signal):
consistency_score *= 0.8
return consistency_score
def _analyze_temporal_patterns(self, signals: List[GPSSignal]) -> float:
"""Analyse patterns temporels pour détection anomalies"""
temporal_score = 1.0
# Stockage historique
self.signal_history.append({
'timestamp': time.time(),
'signals': signals
})
# Garder seulement dernières 60 secondes
cutoff_time = time.time() - 60
self.signal_history = [
entry for entry in self.signal_history
if entry['timestamp'] > cutoff_time
]
if len(self.signal_history) < 5:
return temporal_score
# Analyse variations Doppler
for sat_id in set(s.satellite_id for s in signals):
doppler_history = []
for entry in self.signal_history[-10:]:
sat_signals = [s for s in entry['signals'] if s.satellite_id == sat_id]
if sat_signals:
doppler_history.append(sat_signals[0].doppler_shift)
if len(doppler_history) > 3:
# Vérification continuité Doppler
doppler_gradient = np.gradient(doppler_history)
if np.std(doppler_gradient) > 50: # Variations brutales
temporal_score *= 0.8
return temporal_score
def _check_signal_power(self, signals: List[GPSSignal]) -> float:
"""Vérification cohérence puissance signaux"""
power_score = 1.0
for signal in signals:
# Puissance attendue selon angle élévation
expected_power = self._calculate_expected_power(signal.elevation_angle)
power_deviation = abs(signal.signal_strength - expected_power)
# Signaux trop forts = potentiel spoofing
if signal.signal_strength > expected_power + 10:
power_score *= 0.7
# Signaux uniformes = suspect (spoofing souvent uniformise)
if power_deviation < 2: # Trop uniform
power_score *= 0.9
# Vérification variance puissance globale
powers = [s.signal_strength for s in signals]
if np.std(powers) < 3: # Variance trop faible
power_score *= 0.8
return power_score
def generate_secure_position(self, gps_signals: List[GPSSignal],
imu_data: dict,
visual_odometry: dict) -> dict:
"""Calcul position sécurisée avec fusion multi-capteurs"""
# Analyse spoofing GPS
spoofing_analysis = self.analyze_gps_signals(gps_signals)
position_result = {
'position': None,
'confidence': 0.0,
'source': 'unknown',
'spoofing_detected': spoofing_analysis['spoofing_probability'] > 0.8
}
if not position_result['spoofing_detected']:
# GPS considéré fiable
gps_position = self._calculate_gps_position(gps_signals)
position_result.update({
'position': gps_position,
'confidence': spoofing_analysis['authentication_score'],
'source': 'gps'
})
else:
# Fallback navigation inertielle + vision
backup_position = self._calculate_backup_position(imu_data, visual_odometry)
position_result.update({
'position': backup_position,
'confidence': 0.6, # Confiance réduite sans GPS
'source': 'inertial_visual'
})
return position_result
Blockchain pour Authentification
Système d'Authentification Distribué
import hashlib
import json
import time
from dataclasses import dataclass
from typing import List, Dict, Optional
from cryptography.hazmat.primitives import hashes
from cryptography.hazmat.primitives.asymmetric import rsa, padding
@dataclass
class DroneIdentity:
drone_id: str
public_key: bytes
manufacturer: str
model: str
firmware_version: str
certification_level: str
registration_timestamp: float
@dataclass
class AuthenticationBlock:
index: int
timestamp: float
drone_authentications: List[dict]
previous_hash: str
nonce: int
hash: str
class DroneAuthBlockchain:
"""Blockchain pour authentification drone distribuée"""
def __init__(self):
self.chain: List[AuthenticationBlock] = []
self.pending_authentications = []
self.registered_drones: Dict[str, DroneIdentity] = {}
self.difficulty = 4 # Difficulté mining
# Création bloc genesis
self._create_genesis_block()
def _create_genesis_block(self):
"""Création bloc genesis"""
genesis_block = AuthenticationBlock(
index=0,
timestamp=time.time(),
drone_authentications=[],
previous_hash="0",
nonce=0,
hash=""
)
genesis_block.hash = self._calculate_hash(genesis_block)
self.chain.append(genesis_block)
def register_drone(self, drone_identity: DroneIdentity) -> bool:
"""Enregistrement drone dans blockchain"""
# Vérification unicité ID
if drone_identity.drone_id in self.registered_drones:
return False
# Validation certificat
if not self._validate_drone_certificate(drone_identity):
return False
# Ajout à la registry
self.registered_drones[drone_identity.drone_id] = drone_identity
# Transaction d'enregistrement
registration_tx = {
'type': 'registration',
'drone_id': drone_identity.drone_id,
'public_key': drone_identity.public_key.hex(),
'timestamp': time.time(),
'signature': self._sign_registration(drone_identity)
}
self.pending_authentications.append(registration_tx)
return True
def authenticate_drone(self, drone_id: str, challenge_response: dict) -> dict:
"""Authentification drone via défi cryptographique"""
if drone_id not in self.registered_drones:
return {'authenticated': False, 'reason': 'Drone non enregistré'}
drone_identity = self.registered_drones[drone_id]
# Vérification signature challenge
if not self._verify_challenge_response(drone_identity, challenge_response):
return {'authenticated': False, 'reason': 'Challenge échoué'}
# Vérification réputation drone
reputation_score = self._calculate_reputation(drone_id)
if reputation_score < 0.5:
return {'authenticated': False, 'reason': 'Réputation insuffisante'}
# Transaction d'authentification
auth_tx = {
'type': 'authentication',
'drone_id': drone_id,
'timestamp': time.time(),
'challenge_hash': challenge_response['challenge_hash'],
'reputation_score': reputation_score
}
self.pending_authentications.append(auth_tx)
return {
'authenticated': True,
'session_token': self._generate_session_token(drone_id),
'reputation_score': reputation_score
}
def mine_block(self) -> Optional[AuthenticationBlock]:
"""Mining nouveau bloc authentifications"""
if not self.pending_authentications:
return None
previous_block = self.get_latest_block()
new_block = AuthenticationBlock(
index=previous_block.index + 1,
timestamp=time.time(),
drone_authentications=self.pending_authentications[:],
previous_hash=previous_block.hash,
nonce=0,
hash=""
)
# Proof of Work
new_block.hash = self._mine_block_hash(new_block)
# Ajout à la chaîne
self.chain.append(new_block)
# Nettoyage transactions pending
self.pending_authentications = []
return new_block
def _mine_block_hash(self, block: AuthenticationBlock) -> str:
"""Mining hash avec Proof of Work"""
target = "0" * self.difficulty
while True:
block.nonce += 1
calculated_hash = self._calculate_hash(block)
if calculated_hash.startswith(target):
return calculated_hash
def _calculate_hash(self, block: AuthenticationBlock) -> str:
"""Calcul hash bloc"""
block_string = json.dumps({
'index': block.index,
'timestamp': block.timestamp,
'authentications': block.drone_authentications,
'previous_hash': block.previous_hash,
'nonce': block.nonce
}, sort_keys=True)
return hashlib.sha256(block_string.encode()).hexdigest()
def validate_chain(self) -> bool:
"""Validation intégrité blockchain"""
for i in range(1, len(self.chain)):
current_block = self.chain[i]
previous_block = self.chain[i-1]
# Vérification hash bloc
if current_block.hash != self._calculate_hash(current_block):
return False
# Vérification lien précédent
if current_block.previous_hash != previous_block.hash:
return False
# Vérification Proof of Work
if not current_block.hash.startswith("0" * self.difficulty):
return False
return True
def get_drone_authentication_history(self, drone_id: str) -> List[dict]:
"""Historique authentifications drone"""
history = []
for block in self.chain:
for auth in block.drone_authentications:
if auth.get('drone_id') == drone_id:
history.append({
'block_index': block.index,
'timestamp': auth['timestamp'],
'type': auth['type'],
'details': auth
})
return sorted(history, key=lambda x: x['timestamp'])
def _calculate_reputation(self, drone_id: str) -> float:
"""Calcul score réputation drone"""
history = self.get_drone_authentication_history(drone_id)
if not history:
return 0.5 # Score neutre
# Facteurs réputation
successful_auths = sum(1 for h in history if h['type'] == 'authentication')
failed_auths = sum(1 for h in history if h['type'] == 'failed_authentication')
recent_activity = sum(1 for h in history if time.time() - h['timestamp'] < 86400) # 24h
# Score basé sur succès/échecs et activité récente
success_rate = successful_auths / max(successful_auths + failed_auths, 1)
activity_bonus = min(recent_activity / 10, 0.2)
reputation = success_rate + activity_bonus
return min(max(reputation, 0.0), 1.0)
Réponse aux Incidents
Système de Réponse Automatisée
import threading
import time
import json
from enum import Enum
from dataclasses import dataclass
from typing import Dict, List, Callable, Optional
class IncidentSeverity(Enum):
LOW = 1
MEDIUM = 2
HIGH = 3
CRITICAL = 4
@dataclass
class SecurityIncident:
incident_id: str
timestamp: float
severity: IncidentSeverity
type: str
description: str
affected_systems: List[str]
raw_data: dict
status: str = "detected"
class IncidentResponseSystem:
"""Système de réponse automatisée aux incidents de sécurité"""
def __init__(self):
self.incidents: Dict[str, SecurityIncident] = {}
self.response_handlers: Dict[str, Callable] = {}
self.escalation_rules = {}
self.running = False
# Enregistrement handlers par défaut
self._register_default_handlers()
def _register_default_handlers(self):
"""Enregistrement handlers réponse par défaut"""
self.response_handlers.update({
'gps_spoofing': self._handle_gps_spoofing,
'communication_hijacking': self._handle_comm_hijacking,
'malware_detection': self._handle_malware,
'unauthorized_access': self._handle_unauthorized_access,
'network_intrusion': self._handle_network_intrusion,
'physical_tampering': self._handle_physical_tampering
})
# Règles d'escalation
self.escalation_rules = {
IncidentSeverity.CRITICAL: {
'immediate_response': True,
'operator_notification': True,
'emergency_landing': True,
'system_isolation': True
},
IncidentSeverity.HIGH: {
'immediate_response': True,
'operator_notification': True,
'emergency_landing': False,
'system_isolation': False
},
IncidentSeverity.MEDIUM: {
'immediate_response': False,
'operator_notification': True,
'emergency_landing': False,
'system_isolation': False
}
}
def report_incident(self, incident: SecurityIncident) -> str:
"""Signalement nouvel incident"""
# Stockage incident
self.incidents[incident.incident_id] = incident
# Log incident
self._log_incident(incident)
# Déclenchement réponse automatique
if incident.severity in [IncidentSeverity.HIGH, IncidentSeverity.CRITICAL]:
threading.Thread(
target=self._execute_immediate_response,
args=(incident,),
daemon=True
).start()
return incident.incident_id
def _execute_immediate_response(self, incident: SecurityIncident):
"""Exécution réponse immédiate"""
try:
# Mise à jour statut
incident.status = "responding"
# Application règles escalation
rules = self.escalation_rules.get(incident.severity, {})
if rules.get('system_isolation'):
self._isolate_affected_systems(incident.affected_systems)
if rules.get('emergency_landing'):
self._trigger_emergency_landing()
if rules.get('operator_notification'):
self._notify_operator(incident)
# Handler spécifique type incident
if incident.type in self.response_handlers:
self.response_handlers[incident.type](incident)
# Collecte preuves forensiques
self._collect_forensic_evidence(incident)
incident.status = "mitigated"
except Exception as e:
incident.status = "response_failed"
print(f"Échec réponse incident {incident.incident_id}: {e}")
def _handle_gps_spoofing(self, incident: SecurityIncident):
"""Réponse spoofing GPS"""
# Basculement navigation inertielle
self._switch_to_inertial_navigation()
# Activation mode sécurisé
self._enable_secure_mode()
# Validation position via capteurs alternatifs
self._validate_position_alternative_sensors()
print(f"Réponse GPS spoofing activée pour incident {incident.incident_id}")
def _handle_comm_hijacking(self, incident: SecurityIncident):
"""Réponse détournement communications"""
# Rotation clés chiffrement d'urgence
self._emergency_key_rotation()
# Basculement fréquences backup
self._switch_to_backup_frequencies()
# Activation protocole authentification renforcée
self._enable_enhanced_authentication()
print(f"Réponse comm hijacking activée pour incident {incident.incident_id}")
def _handle_malware(self, incident: SecurityIncident):
"""Réponse détection malware"""
# Isolation processus suspects
affected_processes = incident.raw_data.get('processes', [])
for process in affected_processes:
self._quarantine_process(process)
# Scan sécurité complet
self._initiate_security_scan()
# Sauvegarde état système sain
self._backup_clean_system_state()
print(f"Réponse malware activée pour incident {incident.incident_id}")
def _collect_forensic_evidence(self, incident: SecurityIncident):
"""Collecte preuves forensiques"""
evidence = {
'timestamp': time.time(),
'incident_id': incident.incident_id,
'system_state': self._capture_system_state(),
'network_logs': self._extract_network_logs(),
'flight_data': self._extract_flight_data(),
'security_events': self._extract_security_events()
}
# Stockage sécurisé preuves
evidence_file = f"/secure/forensics/{incident.incident_id}_evidence.json"
with open(evidence_file, 'w') as f:
json.dump(evidence, f, indent=2)
# Hash intégrité
evidence_hash = hashlib.sha256(
json.dumps(evidence, sort_keys=True).encode()
).hexdigest()
print(f"Preuves forensiques collectées: {evidence_hash}")
def generate_incident_report(self, incident_id: str) -> dict:
"""Génération rapport incident détaillé"""
if incident_id not in self.incidents:
return {'error': 'Incident introuvable'}
incident = self.incidents[incident_id]
report = {
'incident_summary': {
'id': incident.incident_id,
'timestamp': incident.timestamp,
'severity': incident.severity.name,
'type': incident.type,
'description': incident.description,
'status': incident.status
},
'impact_analysis': self._analyze_incident_impact(incident),
'response_actions': self._get_response_actions(incident),
'lessons_learned': self._extract_lessons_learned(incident),
'recommendations': self._generate_recommendations(incident)
}
return report
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